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• Table Of Contents

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “Big Data,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is Data Analysis? (Types, Methods, and Tools)

• Couchbase Product Marketing December 17, 2023

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
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What is Data Analysis? An Introductory Guide

The data analysis process, key data analysis skills, start your journey into data analysis with the official enterpise big data analyst certification, data analysis examples in the enterprise, frequently asked questions (faqs).

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to derive meaningful insights and make informed decisions. It involves examining raw data to identify patterns, trends, and relationships that can be used to understand various aspects of a business, organization, or phenomenon. This process often employs statistical methods, machine learning algorithms, and data visualization techniques to extract valuable information from data sets.

At its core, data analysis aims to answer questions, solve problems, and support decision-making processes. It helps uncover hidden patterns or correlations within data that may not be immediately apparent, leading to actionable insights that can drive business strategies and improve performance. Whether it’s analyzing sales figures to identify market trends, evaluating customer feedback to enhance products or services, or studying medical data to improve patient outcomes, data analysis plays a crucial role in numerous domains.

Effective data analysis requires not only technical skills but also domain knowledge and critical thinking. Analysts must understand the context in which the data is generated, choose appropriate analytical tools and methods, and interpret results accurately to draw meaningful conclusions. Moreover, data analysis is an iterative process that may involve refining hypotheses, collecting additional data, and revisiting analytical techniques to ensure the validity and reliability of findings.

Why spend time to learn data analysis?

Learning about data analysis is beneficial for your career because it equips you with the skills to make data-driven decisions, which are highly valued in today’s data-centric business environment. Employers increasingly seek professionals who can gather, analyze, and interpret data to drive innovation, optimize processes, and achieve strategic objectives.

The data analysis process is a systematic approach to extracting valuable insights and making informed decisions from raw data. It begins with defining the problem or question at hand, followed by collecting and cleaning the relevant data. Exploratory data analysis (EDA) helps in understanding the data’s characteristics and uncovering patterns, while data modeling and analysis apply statistical or machine learning techniques to derive meaningful conclusions. In most organizations, data analysis is structured in a number of steps:

• Define the Problem or Question: The first step is to clearly define the problem or question you want to address through data analysis. This could involve understanding business objectives, identifying research questions, or defining hypotheses to be tested.
• Data Collection: Once the problem is defined, gather relevant data from various sources. This could include structured data from databases, spreadsheets, or surveys, as well as unstructured data like text documents or social media posts.
• Data Cleaning and Preprocessing: Clean and preprocess the data to ensure its quality and reliability. This step involves handling missing values, removing duplicates, standardizing formats, and transforming data if needed (e.g., scaling numerical data, encoding categorical variables).
• Exploratory Data Analysis (EDA): Explore the data through descriptive statistics, visualizations (e.g., histograms, scatter plots, heatmaps), and data profiling techniques. EDA helps in understanding the distribution of variables, detecting outliers, and identifying patterns or trends.
• Data Modeling and Analysis: Apply appropriate statistical or machine learning models to analyze the data and answer the research questions or address the problem. This step may involve hypothesis testing, regression analysis, clustering, classification, or other analytical techniques depending on the nature of the data and objectives.
• Interpretation of Results: Interpret the findings from the data analysis in the context of the problem or question. Determine the significance of results, draw conclusions, and communicate insights effectively.
• Decision Making and Action: Use the insights gained from data analysis to make informed decisions, develop strategies, or take actions that drive positive outcomes. Monitor the impact of these decisions and iterate the analysis process as needed.
• Communication and Reporting: Present the findings and insights derived from data analysis in a clear and understandable manner to stakeholders, using visualizations, dashboards, reports, or presentations. Effective communication ensures that the analysis results are actionable and contribute to informed decision-making.

These steps form a cyclical process, where feedback from decision-making may lead to revisiting earlier stages, refining the analysis, and continuously improving outcomes.

Key data analysis skills encompass a blend of technical expertise, critical thinking, and domain knowledge. Some of the essential skills for effective data analysis include:

Statistical Knowledge: Understanding statistical concepts and methods such as hypothesis testing, regression analysis, probability distributions, and statistical inference is fundamental for data analysis.

Data Manipulation and Cleaning: Proficiency in tools like Python, R, SQL, or Excel for data manipulation, cleaning, and transformation tasks, including handling missing values, removing duplicates, and standardizing data formats.

Data Visualization: Creating clear and insightful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to communicate trends, patterns, and relationships within data to non-technical stakeholders.

Machine Learning: Familiarity with machine learning algorithms such as decision trees, random forests, logistic regression, clustering, and neural networks for predictive modeling, classification, clustering, and anomaly detection tasks.

Programming Skills: Competence in programming languages such as Python, R, or SQL for data analysis, scripting, automation, and building data pipelines, along with version control using Git.

Critical Thinking: Ability to think critically, ask relevant questions, formulate hypotheses, and design robust analytical approaches to solve complex problems and extract actionable insights from data.

Domain Knowledge: Understanding the context and domain-specific nuances of the data being analyzed, whether it’s finance, healthcare, marketing, or any other industry, is crucial for meaningful interpretation and decision-making.

Data Ethics and Privacy: Awareness of data ethics principles , privacy regulations (e.g., GDPR, CCPA), and best practices for handling sensitive data responsibly and ensuring data security and confidentiality.

Communication and Storytelling: Effectively communicating analysis results through clear reports, presentations, and data-driven storytelling to convey insights, recommendations, and implications to diverse audiences, including non-technical stakeholders.

These skills are crucial in data analysis because they empower analysts to effectively extract, interpret, and communicate insights from complex datasets across various domains. Statistical knowledge forms the foundation for making data-driven decisions and drawing reliable conclusions. Proficiency in data manipulation and cleaning ensures data accuracy and consistency, essential for meaningful analysis. Here

The Enterprise Big Data Analyst certification is aimed at Data Analyst and provides in-depth theory and practical guidance to deduce value out of Big Data sets. The curriculum segments between different kinds of Big Data problems and its corresponding solutions. This course will teach participants how to autonomously find valuable insights in large data sets in order to realize business benefits.

Data analysis plays an important role in driving informed decision-making and strategic planning within enterprises across various industries. By harnessing the power of data, organizations can gain valuable insights into market trends, customer behaviors, operational efficiency, and performance metrics. Data analysis enables businesses to identify opportunities for growth, optimize processes, mitigate risks, and enhance overall competitiveness in the market. Examples of data analysis in the enterprise span a wide range of applications, including sales and marketing optimization, customer segmentation, financial forecasting, supply chain management, fraud detection, and healthcare analytics.

• Sales and Marketing Optimization: Enterprises use data analysis to analyze sales trends, customer preferences, and marketing campaign effectiveness. By leveraging techniques like customer segmentation and predictive modeling, businesses can tailor marketing strategies, optimize pricing strategies, and identify cross-selling or upselling opportunities.
• Customer Segmentation: Data analysis helps enterprises segment customers based on demographics, purchasing behavior, and preferences. This segmentation allows for targeted marketing efforts, personalized customer experiences, and improved customer retention and loyalty.
• Financial Forecasting: Data analysis is used in financial forecasting to analyze historical data, identify trends, and predict future financial performance. This helps businesses make informed decisions regarding budgeting, investment strategies, and risk management.
• Supply Chain Management: Enterprises use data analysis to optimize supply chain operations, improve inventory management, reduce lead times, and enhance overall efficiency. Analyzing supply chain data helps identify bottlenecks, forecast demand, and streamline logistics processes.
• Fraud Detection: Data analysis is employed to detect and prevent fraud in financial transactions, insurance claims, and online activities. By analyzing patterns and anomalies in data, enterprises can identify suspicious activities, mitigate risks, and protect against fraudulent behavior.
• Healthcare Analytics: In the healthcare sector, data analysis is used for patient care optimization, disease prediction, treatment effectiveness evaluation, and resource allocation. Analyzing healthcare data helps improve patient outcomes, reduce healthcare costs, and support evidence-based decision-making.

These examples illustrate how data analysis is a vital tool for enterprises to gain actionable insights, improve decision-making processes, and achieve strategic objectives across diverse areas of business operations.

Below are some of the most frequently asked questions about data analysis and their answers:

What role does domain knowledge play in data analysis?

Domain knowledge is crucial as it provides context, understanding of data nuances, insights into relevant variables and metrics, and helps in interpreting results accurately within specific industries or domains.

How do you ensure the quality and accuracy of data for analysis?

Ensuring data quality and accuracy involves data validation, cleaning techniques like handling missing values and outliers, standardizing data formats, performing data integrity checks, and validating results through cross-validation or data audits.

What tools and techniques are commonly used in data analysis?

Commonly used tools and techniques in data analysis include programming languages like Python and R, statistical methods such as regression analysis and hypothesis testing, machine learning algorithms for predictive modeling, data visualization tools like Tableau and Matplotlib, and database querying languages like SQL.

What are the steps involved in the data analysis process?

The data analysis process typically includes defining the problem, collecting data, cleaning and preprocessing the data, conducting exploratory data analysis, applying statistical or machine learning models for analysis, interpreting results, making decisions based on insights, and communicating findings to stakeholders.

What is data analysis, and why is it important?

Data analysis involves examining, cleaning, transforming, and modeling data to derive meaningful insights and make informed decisions. It is crucial because it helps organizations uncover trends, patterns, and relationships within data, leading to improved decision-making, enhanced business strategies, and competitive advantage.

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Data Analysis: Definition, Types and Examples

Nowadays, data is collected at various stages of processes and transactions, which has the potential to improve the way we work significantly. However, to fully realize the value of data analysis, this data must be analyzed to gain valuable insights into improving products and services.

Data analysis consists aspect of making informed decisions in various industries. With the advancement of technology, it has become a dynamic and exciting field But what is it in simple words?

What is Data Analysis?

Data analysis is the science of examining data to conclude the information to make decisions or expand knowledge on various subjects. It consists of subjecting data to operations. This process happens to obtain precise conclusions to help us achieve our goals, such as operations that cannot be previously defined since data collection may reveal specific difficulties.

“A lot of this [data analysis] will help humans work smarter and faster because we have data on everything that happens.” –Daniel Burrus, business consultant and speaker on business and innovation issues.

Why is data analytics important?

Data analytics help businesses understand the target market faster, increase sales, reduce costs, increase revenue, and allow for better problem-solving. Data analysis is important for several reasons, as it plays a critical role in various aspects of modern businesses and organizations. Here are some key reasons why data analysis important is crucial:

Informed decision-making

Data analytics helps businesses make more informed and data-driven decisions. By analyzing data, organizations can gain insights into customer behavior, market trends, and operational performance, enabling them to make better choices that are supported by evidence rather than relying on intuition alone.

Identifying opportunities and challenges

Data analytics allows businesses to identify new opportunities for growth, product development, or market expansion. It also helps identify potential challenges and risks, allowing organizations to address them proactively.

Improving efficiency and productivity

Organizations can identify inefficiencies and bottlenecks by analyzing processes and performance data, leading to process optimization and improved productivity. This, in turn, can result in cost savings and better resource allocation.

Customer understanding and personalization

Data analytics enables businesses to understand their customers better, including their preferences, buying behaviors, and pain points. With this understanding, organizations can offer personalized products and services, enhancing customer satisfaction and loyalty.

Competitive advantage

Organizations that leverage data analytics effectively gain a competitive edge in today’s data-driven world. By analyzing data, businesses can identify unique insights and trends that better understand the market and their competitors, helping them stay ahead of the competition.

Performance tracking and evaluation

Data analytics allows organizations to track and measure their performance against key performance indicators (KPIs) and goals. This helps in evaluating the success of various strategies and initiatives, enabling continuous improvement.

Predictive analytics

Data analytics can be used for predictive modeling, helping organizations forecast future trends and outcomes. This is valuable for financial planning, demand forecasting, risk management, and proactive decision-making.

Data-driven innovation

Data analytics can fuel innovation by providing insights that lead to the development of new products, services, or business models. Innovations based on data analysis can lead to groundbreaking advancements and disruption in various industries.

Fraud detection and security

Data analytics can be used to detect anomalies and patterns indicative of fraudulent activities. It plays a crucial role in enhancing security and protecting businesses from financial losses and reputational risk .

Regulatory compliance

In many industries, regulations, and laws are mandatory. Data analytics can help organizations ensure that they meet these compliance requirements by tracking and auditing relevant data.

Types of data analysis

There are several types of data analysis, each with a specific purpose and method. Let’s talk about some significant types:

Descriptive Analysis

Descriptive analysis is used to summarize and describe the main features of a dataset. It involves calculating measures of central tendency and dispersion to describe the data. The descriptive analysis provides a comprehensive overview of the data and insights into its properties and structure.

LEARN ABOUT: Descriptive Analysis

Inferential Analysis

The inferential analysis is used statistical analysis plan and testing to make inferences about the population parameters, such as the mean or proportion. This unit of analysis involves using models and hypothesis testing to make predictions and draw conclusions about the population.

LEARN ABOUT:   Statistical Analysis Methods

Predictive Analysis

Predictive analysis is used to predict future events or outcomes based on historical data and other relevant information. It involves using statistical models and machine learning algorithms to identify patterns in the data and make predictions about future outcomes.

Prescriptive Analysis

Prescriptive analysis is a decision-making analysis that uses mathematical modeling, optimization algorithms, and other data-driven techniques to identify the action for a given problem or situation. It combines mathematical models, data, and business constraints to find the best move or decision.

Text Analysis

Text analysis is a process of extracting meaningful information from unstructured text data. It involves a variety of techniques, including natural language processing (NLP), text mining, sentiment analysis, and topic modeling, to uncover insights and patterns in text data.

Diagnostic Analysis

The diagnostic analysis seeks to identify the root causes of specific events or outcomes. It is often used in troubleshooting problems or investigating anomalies in data.

LEARN ABOUT: Data Analytics Projects

Uses of data analysis

It is used in many industries regardless of the branch. It gives us the basis for making decisions or confirming a hypothesis.

A researcher or data analyst mainly performs data analysis to predict consumer behavior and help companies place their products and services in the market accordingly. For instance, sales data analysis can help you identify the product range not-so-popular in a specific demographic group. It can give you insights into tweaking your current marketing campaign to better connect with the target audience and address their needs. 

Human Resources

Organizations can use data analysis tools to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data to find out the best resources whose skill set matches the organizational goals.

Universities and academic institutions can perform the analysis to measure student performance and gather insights on how certain behaviors can further improve education.

Techniques for data analysis

It is essential to analyze raw data to understand it. We must resort to various data analysis techniques that depend on the type of information collected, so it is crucial to define the method before implementing it.

• Qualitative data: Researchers collect qualitative data from the underlying emotions, body language, and expressions. Its foundation is the data interpretation of verbal responses. The most common ways of obtaining this information are through open-ended interviews, focus groups, and observation groups, where researchers generally analyze patterns in observations throughout the data collection phase.
• Quantitative data: Quantitative data presents itself in numerical form. It focuses on tangible results.

Data analysis focuses on reaching a conclusion based solely on the researcher’s current knowledge. How you collect your data should relate to how you plan to analyze and use it. You also need to collect accurate and trustworthy information. 

Many data collection techniques exist, but experts’ most commonly used method is online surveys. It offers significant benefits, such as reducing time and money compared to traditional data collection methods .

Data analysis and data analytics are two interconnected but distinct processes in data science. Data analysis involves examining raw data using various techniques to uncover patterns, correlations, and insights. It’s about understanding historical data to make informed conclusions. On the other hand, data analytics goes a step further by utilizing those insights to predict future trends, prescribe actions, and guide decision-making.

At QuestionPro, we have an accurate tool that will help you professionally make better decisions.

Data Analysis Methods

The term data analysis technique has often been used interchangeably by professional researchers. Frequently people also throw out the previous analysis type. We’re hoping for this to be an important distinction between how and when data analyses are done. 

However, there are many different techniques that allow for data analysis. Here are some of the main common methods used for data analysis:

Descriptive Statistics

Descriptive statistics involves summarizing and describing the main features of a dataset, such as mean, median, mode, standard deviation, range, and percentiles. It provides a basic understanding of the data’s distribution and characteristics.

Inferential Statistics

Inferential statistics are used to make inferences and draw conclusions about a larger population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.

Data Visualization

Data visualization is the graphical representation of data to help analysts and stakeholders understand patterns, trends, and insights. Common visualization techniques include bar charts, line graphs, scatter plots, heat maps, and pie charts.

Exploratory Data Analysis (EDA)

EDA involves analyzing and visualizing data to discover patterns, relationships, and potential outliers. It helps in gaining insights into the data before formal statistical testing.

Predictive Modeling

Predictive modeling uses algorithms and statistical techniques to build models that can make predictions about future outcomes based on historical data. Machine learning algorithms, such as decision trees, logistic regression, and neural networks, are commonly used for predictive modeling.

Time Series Analysis

Time series analysis is used to analyze data collected over time, such as stock prices, temperature readings, or sales data. It involves identifying trends and seasonality and forecasting future values.

Cluster Analysis

Cluster analysis is used to group similar data points together based on certain features or characteristics. It helps in identifying patterns and segmenting data into meaningful clusters.

Factor Analysis and Principal Component Analysis (PCA)

These techniques are used to reduce the dimensionality of data and identify underlying factors or components that explain the variance in the data.

Text Mining and Natural Language Processing (NLP)

Text mining and NLP techniques are used to analyze and extract information from unstructured text data, such as social media posts, customer reviews, or survey responses.

Qualitative Data Analysis

Qualitative data analysis involves interpreting non-numeric data, such as text, images, audio, or video. Techniques like content analysis, thematic analysis, and grounded theory are used to analyze qualitative data.

Quantitative Data Analysis

Quantitative analysis focuses on analyzing numerical data to discover relationships, trends, and patterns. This analysis often involves statistical methods.

Data Mining

Data mining involves discovering patterns, relationships, or insights from large datasets using various algorithms and techniques.

Regression Analysis

Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It helps understand how changes in one variable impact the other(s).

Step-by-step guide data analysis

With these five steps in your data analysis process, you will make better decisions for your business because data that has been well collected and analyzed support your choices.

LEARN ABOUT: Data Mining Techniques

Step 1: Define your questions

Start by selecting the right questions. Questions should be measurable, clear, and concise. Design your questions to qualify or disqualify possible solutions to your specific problem.

Step 2: Establish measurement priorities

This step divides into two sub-steps:

• Decide what to measure: Analyze what kind of data you need.
• Decide how to measure it: Thinking about how to measure your data is just as important, especially before the data collection phase, because your measurement process supports or discredits your thematic analysis later on.

Step 3: Collect data

With the question clearly defined and your measurement priorities established, now it’s time to collect your data. As you manage and organize your data, remember to keep these essential points in mind:

• Before collecting new data, determine what information you could gather from existing databases or sources.
• Determine a storage and file naming system to help all team members collaborate in advance. This process saves time and prevents team members from collecting the same information twice.
• If you need to collect data through surveys, observation, or interviews, develop a questionnaire in advance to ensure consistency and save time.
• Keep the collected data organized with a log of collection dates, and add any source notes as you go along.

Step 4: Analyze the data

Once you’ve collected the correct data to answer your Step 1 question, it’s time to conduct a deeper statistical analysis . Find relationships, identify trends, and sort and filter your data according to variables. You will find the exact data you need as you analyze the data.

Step 5: Interpret the results

After analyzing the data and possibly conducting further research, it is finally time to interpret the results. Ask yourself these key questions:

• Does the data answer your original question? How?
• Does the data help you defend any objections? How?
• Are there any limitations to the conclusions, any angles you haven’t considered?

If the interpretation of data holds up under these questions and considerations, you have reached a productive conclusion. The only remaining step is to use the process results to decide how you will act.

Join us as we look into the most frequently used question types and how to analyze your findings effectively.

Make the right decisions by analyzing data the right way!

Data analysis advantages

Many industries use data to draw conclusions and decide on actions to implement. It is worth mentioning that science also uses to test or discard existing theories or models.

There’s more than one advantage to data analysis done right. Here are some examples:

• Make faster and more informed business decisions backed by facts.
• Identify performance issues that require action.
• Gain a deeper understanding of customer requirements, which creates better business relationships.
• Increase awareness of risks to implement preventive measures.
• Visualize different dimensions of the data.
• Gain competitive advantage.
• A better understanding of the financial performance of the business.
• Identify ways to reduce costs and thus increase profits.

These questions are examples of different types of data analysis. You can include them in your post-event surveys aimed at your customers:

• Questions start with: Why? How? 

Example of qualitative data research analysis: Panels where a discussion is held, and consumers are interviewed about what they like or dislike about the place.

• Data is collected by asking questions like: How many? Who? How often? Where?

Example of quantitative research analysis: Surveys focused on measuring sales, trends, reports, or perceptions.

Data analysis with QuestionPro

Data analysis is crucial in aiding organizations and individuals in making informed decisions by comprehensively understanding the data. If you’re in need of various data analysis techniques solutions, consider using QuestionPro. Our software allows you to collect data easily, create real-time reports, and analyze data. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

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Start a free trial or schedule a demo to see the full potential of our powerful tool. We’re here to help you every step of the way!

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What is Data Analysis? Research, Types & Example

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

In this Data Science Tutorial, you will learn:

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.

Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

Text Analysis

Statistical analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools . It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it’s not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!

So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.

Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Data Analysis Process

The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.

Data Analysis consists of the following phases:

Data Requirement Gathering

Data collection, data cleaning, data analysis, data interpretation, data visualization.

First of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis of data. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.

After requirement gathering, you will get a clear idea about what things you have to measure and what should be your findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.

Now whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.

After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.

Data visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Data visualization often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.

• Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
• Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
• Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of data science and  data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and  modeling data to draw useful insights from it.

Types of Data Analysis

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in decision-making , providing a better, faster and more effective system that minimizes risks and reduces human biases .

That said, there are different kinds of data analysis with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book R for Data Science :

• Hypothesis Generation: This involves looking deeply at the data and combining your domain knowledge to generate  hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation: This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.  

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data . Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries of samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

2. Diagnostic Analysis  

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies .  

Diagnostic Analysis Example

A footwear store wants to review its  website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.    

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or  exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection .

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses. 

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or  standard deviation to your estimation.
• The accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the central limit theorem .

Inferential Analysis Example

A psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven to nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions. 

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 United States election is a popular topic and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. This way, researchers can examine how a change in one variable affects another. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of a phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome. 

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables . In some ways, it is a predictive analysis, but it’s modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high  precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

8. Prescriptive Analysis  

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. Artificial intelligence systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram,  algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an  algorithm seeks out similar content that is likely to elicit the same response and  recommends it on a user’s personal feed. 

More on Data Explaining the Empirical Rule for Normal Distribution

When to Use the Different Types of Data Analysis  

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Frequently Asked Questions

What is an example of data analysis.

A marketing team reviews a company’s web traffic over the past 12 months. To understand why sales rise and fall during certain months, the team breaks down the data to look at shoe type, seasonal patterns and sales events. Based on this in-depth analysis, the team can determine variables that influenced web traffic and make adjustments as needed.

How do you know which data analysis method to use?

Selecting a data analysis method depends on the goals of the analysis and the complexity of the task, among other factors. It’s best to assess the circumstances and consider the pros and cons of each type of data analysis before moving forward with a particular method.

Recent Data Science Articles

is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”.. While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye, Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, research) and the form of the data (field notes, documents, audiotape, videotape). An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well. Considerations/issues in data analysis There are a number of issues that researchers should be cognizant of with respect to data analysis. These include: when analyzing qualitative data A tacit assumption of investigators is that they have received training sufficient to demonstrate a high standard of research practice. Unintentional ‘scientific misconduct' is likely the result of poor instruction and follow-up. A number of studies suggest this may be the case more often than believed (Nowak, 1994; Silverman, Manson, 2003). For example, Sica found that adequate training of physicians in medical schools in the proper design, implementation and evaluation of clinical trials is “abysmally small” (Sica, cited in Nowak, 1994). Indeed, a single course in biostatistics is the most that is usually offered (Christopher Williams, cited in Nowak, 1994). A common practice of investigators is to defer the selection of analytic procedure to a research team ‘statistician’. Ideally, investigators should have substantially more than a basic understanding of the rationale for selecting one method of analysis over another. This can allow investigators to better supervise staff who conduct the data analyses process and make informed decisions While methods of analysis may differ by scientific discipline, the optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought. According to Smeeton and Goda (2003), “Statistical advice should be obtained at the stage of initial planning of an investigation so that, for example, the method of sampling and design of questionnaire are appropriate”. The chief aim of analysis is to distinguish between an event occurring as either reflecting a true effect versus a false one. Any bias occurring in the collection of the data, or selection of method of analysis, will increase the likelihood of drawing a biased inference. Bias can occur when recruitment of study participants falls below minimum number required to demonstrate statistical power or failure to maintain a sufficient follow-up period needed to demonstrate an effect (Altman, 2001). When failing to demonstrate statistically different levels between treatment groups, investigators may resort to breaking down the analysis to smaller and smaller subgroups in order to find a difference. Although this practice may not inherently be unethical, these analyses should be proposed before beginning the study even if the intent is exploratory in nature. If it the study is exploratory in nature, the investigator should make this explicit so that readers understand that the research is more of a hunting expedition rather than being primarily theory driven. Although a researcher may not have a theory-based hypothesis for testing relationships between previously untested variables, a theory will have to be developed to explain an unanticipated finding. Indeed, in exploratory science, there are no a priori hypotheses therefore there are no hypothetical tests. Although theories can often drive the processes used in the investigation of qualitative studies, many times patterns of behavior or occurrences derived from analyzed data can result in developing new theoretical frameworks rather than determined (Savenye, Robinson, 2004). It is conceivable that multiple statistical tests could yield a significant finding by chance alone rather than reflecting a true effect. Integrity is compromised if the investigator only reports tests with significant findings, and neglects to mention a large number of tests failing to reach significance. While access to computer-based statistical packages can facilitate application of increasingly complex analytic procedures, inappropriate uses of these packages can result in abuses as well. Every field of study has developed its accepted practices for data analysis. Resnik (2000) states that it is prudent for investigators to follow these accepted norms. Resnik further states that the norms are ‘…based on two factors: (1) the nature of the variables used (i.e., quantitative, comparative, or qualitative), (2) assumptions about the population from which the data are drawn (i.e., random distribution, independence, sample size, etc.). If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods. If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods. While the conventional practice is to establish a standard of acceptability for statistical significance, with certain disciplines, it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning, i.e., . Jeans (1992) defines ‘clinical significance’ as “the potential for research findings to make a real and important difference to clients or clinical practice, to health status or to any other problem identified as a relevant priority for the discipline”. Kendall and Grove (1988) define clinical significance in terms of what happens when “… troubled and disordered clients are now, after treatment, not distinguishable from a meaningful and representative non-disturbed reference group”. Thompson and Noferi (2002) suggest that readers of counseling literature should expect authors to report either practical or clinical significance indices, or both, within their research reports. Shepard (2003) questions why some authors fail to point out that the magnitude of observed changes may too small to have any clinical or practical significance, “sometimes, a supposed change may be described in some detail, but the investigator fails to disclose that the trend is not statistically significant ”. No amount of statistical analysis, regardless of the level of the sophistication, will correct poorly defined objective outcome measurements. Whether done unintentionally or by design, this practice increases the likelihood of clouding the interpretation of findings, thus potentially misleading readers. The basis for this issue is the urgency of reducing the likelihood of statistical error. Common challenges include the exclusion of , filling in missing data, altering or otherwise changing data, data mining, and developing graphical representations of the data (Shamoo, Resnik, 2003). At times investigators may enhance the impression of a significant finding by determining how to present (as opposed to data in its raw form), which portion of the data is shown, why, how and to whom (Shamoo, Resnik, 2003). Nowak (1994) notes that even experts do not agree in distinguishing between analyzing and massaging data. Shamoo (1989) recommends that investigators maintain a sufficient and accurate paper trail of how data was manipulated for future review. The integrity of data analysis can be compromised by the environment or context in which data was collected i.e., face-to face interviews vs. focused group. The occurring within a dyadic relationship (interviewer-interviewee) differs from the group dynamic occurring within a focus group because of the number of participants, and how they react to each other’s responses. Since the data collection process could be influenced by the environment/context, researchers should take this into account when conducting data analysis. Analyses could also be influenced by the method in which data was recorded. For example, research events could be documented by: a. recording audio and/or video and transcribing later b. either a researcher or self-administered survey c. either or d. preparing ethnographic field notes from a participant/observer e. requesting that participants themselves take notes, compile and submit them to researchers. While each methodology employed has rationale and advantages, issues of objectivity and subjectivity may be raised when data is analyzed. During content analysis, staff researchers or ‘raters’ may use inconsistent strategies in analyzing text material. Some ‘raters’ may analyze comments as a whole while others may prefer to dissect text material by separating words, phrases, clauses, sentences or groups of sentences. Every effort should be made to reduce or eliminate inconsistencies between “raters” so that data integrity is not compromised. A major challenge to data integrity could occur with the unmonitored supervision of inductive techniques. Content analysis requires raters to assign topics to text material (comments). The threat to integrity may arise when raters have received inconsistent training, or may have received previous training experience(s). Previous experience may affect how raters perceive the material or even perceive the nature of the analyses to be conducted. Thus one rater could assign topics or codes to material that is significantly different from another rater. Strategies to address this would include clearly stating a list of analyses procedures in the protocol manual, consistent training, and routine monitoring of raters. Researchers performing analysis on either quantitative or qualitative analyses should be aware of challenges to reliability and validity. For example, in the area of content analysis, Gottschalk (1995) identifies three factors that can affect the reliability of analyzed data: The potential for compromising data integrity arises when researchers cannot consistently demonstrate stability, reproducibility, or accuracy of data analysis According Gottschalk, (1995), the validity of a content analysis study refers to the correspondence of the categories (the classification that raters’ assigned to text content) to the conclusions, and the generalizability of results to a theory (did the categories support the study’s conclusion, and was the finding adequately robust to support or be applied to a selected theoretical rationale?). Upon coding text material for content analysis, raters must classify each code into an appropriate category of a cross-reference matrix. Relying on computer software to determine a frequency or word count can lead to inaccuracies. “One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage” (Gottschalk, 1995). Further analyses might be appropriate to discover the dimensionality of the data set or identity new meaningful underlying variables. Whether statistical or non-statistical methods of analyses are used, researchers should be aware of the potential for compromising data integrity. While statistical analysis is typically performed on quantitative data, there are numerous analytic procedures specifically designed for qualitative material including content, thematic, and ethnographic analysis. Regardless of whether one studies quantitative or qualitative phenomena, researchers use a variety of tools to analyze data in order to test hypotheses, discern patterns of behavior, and ultimately answer research questions. Failure to understand or acknowledge data analysis issues presented can compromise data integrity. References: Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc Jeans, M. E. (1992). Clinical significance of research: A growing concern. Canadian Journal of Nursing Research, 24, 1-4. Lefort, S. (1993). The statistical versus clinical significance debate. Image, 25, 57-62. Kendall, P. C., & Grove, W. (1988). Normative comparisons in therapy outcome. Behavioral Assessment, 10, 147-158. Nowak, R. (1994). Problems in clinical trials go far beyond misconduct. Science. 264(5165): 1538-41. Resnik, D. (2000). Statistics, ethics, and research: an agenda for educations and reform. Accountability in Research. 8: 163-88 Schroder, K.E., Carey, M.P., Venable, P.A. (2003). Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytic options. Ann Behav Med, 26(2): 76-103. Shamoo, A.E., Resnik, B.R. (2003). Responsible Conduct of Research. Oxford University Press. Shamoo, A.E. (1989). Principles of Research Data Audit. Gordon and Breach, New York. Shepard, R.J. (2002). Ethics in exercise science research. Sports Med, 32 (3): 169-183. Silverman, S., Manson, M. (2003). Research on teaching in physical education doctoral dissertations: a detailed investigation of focus, method, and analysis. Journal of Teaching in Physical Education, 22(3): 280-297. Smeeton, N., Goda, D. (2003). Conducting and presenting social work research: some basic statistical considerations. Br J Soc Work, 33: 567-573. Thompson, B., Noferi, G. 2002. Statistical, practical, clinical: How many types of significance should be considered in counseling research? Journal of Counseling & Development, 80(4):64-71. Research Guide: Data analysis and reporting findings Postgraduate Online Training subject guide This link opens in a new window Open Educational Resources (OERs) Library support Research ideas You and your supervisor Researcher skills Research Data Management This link opens in a new window Literature review Plagiarism This link opens in a new window Research Methods Data analysis and reporting findings Statistical support Writing support Researcher visibility Conferences and Presentations Postgraduate Forums Soft skills development Emotional support The Commons Informer (blog) Research Tip Archives RC Newsletter Archives Evaluation Forms Editing FAQs Data analysis and findings Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends.  Data Analysis Checklist Cleaning  data * Did you capture and code your data in the right manner? *Do you have all data or missing data? * Do you have enough observations? * Do you have any outliers? If yes, what is the remedy for outlier? * Does your data have the potential to answer your questions? Analyzing data * Visualize your data, e.g. charts, tables, and graphs, to mention a few. *  Identify patterns, correlations, and trends * Test your hypotheses * Let your data tell a story Reports the results * Communicate and interpret the results * Conclude and recommend * Your targeted audience must understand your results * Use more datasets and samples * Use accessible and understandable data analytical tool * Do not delegate your data analysis * Clean data to confirm that they are complete and free from errors * Analyze cleaned data * Understand your results * Keep in mind who will be reading your results and present it in a way that they will understand it * Share the results with the supervisor oftentimes Past presentations PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga. Online Resources Qualitative analysis of interview data: A step-by-step guide Qualitative Data Analysis - Coding & Developing Themes Recommended Quantitative Data Analysis books Recommended Qualitative Data Analysis books << Previous: Data collection techniques Next: Statistical support >> Last Updated: Jul 16, 2024 8:49 AM URL: https://library.up.ac.za/c.php?g=485435 [email protected] Statistical Analysis in Research: Meaning, Methods and Types Home » Videos » Statistical Analysis in Research: Meaning, Methods and Types The scientific method is an empirical approach to acquiring new knowledge by making skeptical observations and analyses to develop a meaningful interpretation. It is the basis of research and the primary pillar of modern science. Researchers seek to understand the relationships between factors associated with the phenomena of interest. In some cases, research works with vast chunks of data, making it difficult to observe or manipulate each data point. As a result, statistical analysis in research becomes a means of evaluating relationships and interconnections between variables with tools and analytical techniques for working with large data. Since researchers use statistical power analysis to assess the probability of finding an effect in such an investigation, the method is relatively accurate. Hence, statistical analysis in research eases analytical methods by focusing on the quantifiable aspects of phenomena. What is Statistical Analysis in Research? A Simplified Definition Statistical analysis uses quantitative data to investigate patterns, relationships, and patterns to understand real-life and simulated phenomena. The approach is a key analytical tool in various fields, including academia, business, government, and science in general. This statistical analysis in research definition implies that the primary focus of the scientific method is quantitative research. Notably, the investigator targets the constructs developed from general concepts as the researchers can quantify their hypotheses and present their findings in simple statistics. When a business needs to learn how to improve its product, they collect statistical data about the production line and customer satisfaction. Qualitative data is valuable and often identifies the most common themes in the stakeholders’ responses. On the other hand, the quantitative data creates a level of importance, comparing the themes based on their criticality to the affected persons. For instance, descriptive statistics highlight tendency, frequency, variation, and position information. While the mean shows the average number of respondents who value a certain aspect, the variance indicates the accuracy of the data. In any case, statistical analysis creates simplified concepts used to understand the phenomenon under investigation. It is also a key component in academia as the primary approach to data representation, especially in research projects, term papers and dissertations.  Most Useful Statistical Analysis Methods in Research Using statistical analysis methods in research is inevitable, especially in academic assignments, projects, and term papers. It’s always advisable to seek assistance from your professor or you can try research paper writing by CustomWritings before you start your academic project or write statistical analysis in research paper. Consulting an expert when developing a topic for your thesis or short mid-term assignment increases your chances of getting a better grade. Most importantly, it improves your understanding of research methods with insights on how to enhance the originality and quality of personalized essays. Professional writers can also help select the most suitable statistical analysis method for your thesis, influencing the choice of data and type of study. Descriptive Statistics Descriptive statistics is a statistical method summarizing quantitative figures to understand critical details about the sample and population. A description statistic is a figure that quantifies a specific aspect of the data. For instance, instead of analyzing the behavior of a thousand students, research can identify the most common actions among them. By doing this, the person utilizes statistical analysis in research, particularly descriptive statistics. Measures of central tendency . Central tendency measures are the mean, mode, and media or the averages denoting specific data points. They assess the centrality of the probability distribution, hence the name. These measures describe the data in relation to the center. Measures of frequency . These statistics document the number of times an event happens. They include frequency, count, ratios, rates, and proportions. Measures of frequency can also show how often a score occurs. Measures of dispersion/variation . These descriptive statistics assess the intervals between the data points. The objective is to view the spread or disparity between the specific inputs. Measures of variation include the standard deviation, variance, and range. They indicate how the spread may affect other statistics, such as the mean. Measures of position . Sometimes researchers can investigate relationships between scores. Measures of position, such as percentiles, quartiles, and ranks, demonstrate this association. They are often useful when comparing the data to normalized information. Inferential Statistics Inferential statistics is critical in statistical analysis in quantitative research. This approach uses statistical tests to draw conclusions about the population. Examples of inferential statistics include t-tests, F-tests, ANOVA, p-value, Mann-Whitney U test, and Wilcoxon W test. This Common Statistical Analysis in Research Types Although inferential and descriptive statistics can be classified as types of statistical analysis in research, they are mostly considered analytical methods. Types of research are distinguishable by the differences in the methodology employed in analyzing, assembling, classifying, manipulating, and interpreting data. The categories may also depend on the type of data used. Predictive Analysis Predictive research analyzes past and present data to assess trends and predict future events. An excellent example of predictive analysis is a market survey that seeks to understand customers’ spending habits to weigh the possibility of a repeat or future purchase. Such studies assess the likelihood of an action based on trends. Prescriptive Analysis On the other hand, a prescriptive analysis targets likely courses of action. It’s decision-making research designed to identify optimal solutions to a problem. Its primary objective is to test or assess alternative measures. Causal Analysis Causal research investigates the explanation behind the events. It explores the relationship between factors for causation. Thus, researchers use causal analyses to analyze root causes, possible problems, and unknown outcomes. Mechanistic Analysis This type of research investigates the mechanism of action. Instead of focusing only on the causes or possible outcomes, researchers may seek an understanding of the processes involved. In such cases, they use mechanistic analyses to document, observe, or learn the mechanisms involved. Exploratory Data Analysis Similarly, an exploratory study is extensive with a wider scope and minimal limitations. This type of research seeks insight into the topic of interest. An exploratory researcher does not try to generalize or predict relationships. Instead, they look for information about the subject before conducting an in-depth analysis. The Importance of Statistical Analysis in Research As a matter of fact, statistical analysis provides critical information for decision-making. Decision-makers require past trends and predictive assumptions to inform their actions. In most cases, the data is too complex or lacks meaningful inferences. Statistical tools for analyzing such details help save time and money, deriving only valuable information for assessment. An excellent statistical analysis in research example is a randomized control trial (RCT) for the Covid-19 vaccine. You can download a sample of such a document online to understand the significance such analyses have to the stakeholders. A vaccine RCT assesses the effectiveness, side effects, duration of protection, and other benefits. Hence, statistical analysis in research is a helpful tool for understanding data. Sources and links For the articles and videos I use different databases, such as Eurostat, OECD World Bank Open Data, Data Gov and others. You are free to use the video I have made on your site using the link or the embed code. If you have any questions, don’t hesitate to write to me! Support statistics and data, if you have reached the end and like this project, you can donate a coffee to “statistics and data”.. Copyright © 2022 Statistics and Data Extract insights from customer & stakeholder interviews. At Scale. Qualitative observation science definition explained. Home » Qualitative Observation Science Definition Explained Subjective Data Analysis plays a pivotal role in qualitative observation science, providing insights that quantitative methods often overlook. By focusing on personal experiences and perceptions, researchers are able to uncover deeper meanings behind human behavior and social phenomena. Qualitative data invites a nuanced exploration of emotions, motivations, and attitudes, which can enrich our understanding of complex subjects. Engaging in subjective data analysis allows researchers to navigate the intricacies of human experiences. This method requires critical thinking and a reflective approach to interpret findings accurately. Ultimately, embracing this analytical framework empowers researchers to derive meaningful insights that inform decisions, policy, and practice in various fields. Understanding these qualitative dimensions is crucial for anyone seeking to enhance their research skills. The Basics of Qualitative Observation Science Qualitative Observation Science is primarily about understanding human behavior and experiences through subjective data analysis. This process involves collecting insights from interviews, focus groups, and other methods that prioritize personal perspectives over numerical data. By focusing on real human experiences, researchers can uncover nuanced meanings behind actions and reactions that quantitative methods might overlook. In practice, qualitative observation encompasses several key components. First, it encourages the gathering of rich descriptive data, which allows for a deeper understanding of complex issues. Second, it emphasizes the importance of context in interpreting findings. Lastly, it involves a reflective approach, where researchers continuously analyze their own biases and influences throughout the study. This comprehensive understanding equips businesses and organizations with the knowledge needed to make informed decisions based on people’s true feelings and beliefs. Defining Qualitative Observation Qualitative observation is a key component of gathering subjective data, focusing on understanding behaviors, experiences, and emotions in their natural context. This method relies on the observer's interpretation, highlighting the significance of individual perspectives. This approach is essential for researchers seeking to delve deeper into human interactions and the meanings behind them. In qualitative observation, the researcher becomes a participant, allowing for a richer understanding of the subject matter. This form of observation can take various forms, including interviews, field studies, and focus groups. Such methods enable the collection of nuanced, subjective data that often reveal patterns and themes not readily visible through quantitative analysis. Understanding these insights is crucial for developing comprehensive conclusions about complex social phenomena. Qualitative observation thus plays a vital role in interpreting subjective data analysis and enriches our comprehension of human behavior. Importance and Applications of Qualitative Observation Qualitative observation plays a crucial role in the research landscape, offering insights that quantitative methods may overlook. By capturing subjective data analysis in the form of behaviors, emotions, and interactions, researchers can build a nuanced understanding of their subjects. This approach allows for deeper exploration of contexts and cultures, leading to findings that resonate on a personal level. This is particularly beneficial in fields such as anthropology, sociology, and psychology, where human experience is integral to understanding phenomena. The applications of qualitative observation extend to various domains, including market research, where businesses harness these insights to inform product development. For instance, understanding customer behaviors and preferences through direct observation helps companies tailor their offerings effectively. Additionally, in educational settings, qualitative methods provide valuable feedback about teaching practices and student engagement. Overall, the importance of qualitative observation lies in its ability to unveil the complexities of human experiences, guiding informed decision-making across multiple sectors. Subjective Data Analysis in Qualitative Observation Subjective data analysis is crucial in qualitative observation, as it shapes our understanding of human experiences and perceptions. In qualitative studies, researchers often rely on their interpretations and insights to draw conclusions. This process can be highly subjective, meaning that individual biases and perspectives may influence the analysis of data. As a result, researchers must approach their observations with a clear framework to minimize inconsistencies and ensure reliability. To enhance subjective data analysis, it's essential to adopt specific practices. First, researchers should engage in reflective journaling to capture their thoughts and feelings during data collection. This practice can help identify personal biases. Second, employing triangulation—using multiple data sources or perspectives—allows for a more comprehensive understanding of the observed behaviors. Lastly, maintaining transparency in reporting findings can foster trust and enhance the credibility of the analysis. By embracing these practices, researchers can improve the robustness of their subjective data analysis in qualitative observation. Understanding Subjective Data in Qualitative Research Subjective data analysis plays a crucial role in qualitative research by providing rich, in-depth insights into participants' experiences. Understanding subjective data means interpreting the thoughts, feelings, and perspectives shared by individuals through methods like interviews, focus groups, or open-ended surveys. Researchers analyze this subjective information to uncover themes and patterns that can inform decision-making or improve services. To navigate subjective data effectively, it can be useful to consider the following aspects: Context Matters : Always keep in mind the background and environment in which responses are given. This context helps to interpret subjective insights accurately. Themes and Patterns : Identify recurring themes among participants. This allows researchers to see shared experiences while also highlighting unique perspectives. Participant Voice : Empower participants by allowing their voices and experiences to shape the analysis. Their insights provide depth to quantitative findings. Flexibility in Interpretation : Subjective interpretations are not fixed; they can evolve as more data is collected or as new contexts are considered. Engaging with subjective data analysis requires careful thought and sensitivity, as it directly reflects the complex nature of human experiences. Techniques for Analyzing Subjective Data Analyzing subjective data is crucial in qualitative observation science as it captures the nuanced perspectives of participants. One effective approach is thematic analysis, where researchers identify patterns or themes within the data to uncover underlying meanings. This method allows for a deep exploration of participants' experiences and insights, enabling a rich understanding of complex social phenomena. Another useful technique is narrative analysis, which focuses on how individuals construct their stories. By examining the narratives shared by study participants, researchers can gain insight into personal motivations and cultural contexts. Both these techniques enhance subjective data analysis, providing structured frameworks for interpreting qualitative information. Finally, utilizing visual tools—such as matrices or templates—can facilitate the digestibility of data, allowing for the identification of trends and connections in a more accessible format. These techniques collectively contribute to a comprehensive understanding of subjective insights. Challenges and Limitations of Subjective Data Analysis in Qualitative Observation Science Subjective data analysis in qualitative observation science presents numerous challenges that can hinder the integrity of research findings. One significant issue is the inherent bias in how data is interpreted. Analysts may unknowingly impose their perspectives or expectations, leading to inconsistent results that can distort the overall picture. This subjectivity can cause critical details to be overlooked, which may skew conclusions and affect decision-making. Furthermore, the manual nature of subjective data analysis can lead to inefficiencies. Teams often spend excessive time analyzing data without deriving actionable insights quickly. While tools exist to document and capture data, they often fall short in helping researchers make sense of the information. This combination of bias and inefficiency highlights the limitations of subjective data analysis, emphasizing the need for more structured approaches in qualitative research. Conclusion on Subjective Data Analysis in Qualitative Observation Science Subjective data analysis plays a crucial role in qualitative observation science, characterized by its interpretative nature. However, this subjectivity often introduces biases, leading to inconsistent outcomes. Teams frequently overlook critical details when analyzing transcripts, resulting in slow insights that hinder decision-making within organizations. To improve the efficiency of subjective data analysis, it is essential to incorporate advanced tools designed for data synthesis and interpretation. By utilizing technology to aggregate insights from various sources, teams can enhance their understanding of qualitative data. This methodological shift can streamline processes and ultimately yield more reliable findings in qualitative observation. Turn interviews into actionable insights On this Page Risk Sensitivity Analysis: AI Tools for Market Research 2024 You may also like, understanding grounded theories in market research applications. How to Cite Wikipedia in Essay Writing Effectively Grounded theory method for analyzing transcripts. 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By UOnline News 08-08-2024 In today's fast-paced, data-driven world, businesses are increasingly relying on data analytics to gain a competitive edge. In such highly competitive business environments, data analytics has emerged as a powerful tool for all kinds of businesses. More than ever before, the benefits of data analytics to businesses have become vast and far-reaching. This blog post explores the various benefits of data analytics in business, illustrating how it can transform operations and drive growth. How Does Data Analytics Help Business? Data analytics, the process of examining large datasets to uncover hidden patterns, correlations, and insights, has become a cornerstone of modern business strategy. From enhancing decision-making to improving operational efficiency, the benefits of data analytics in business are multifaceted and profound. By leveraging the advantages of data analytics, businesses can make more informed decisions, improve operational efficiency, enhance customer experiences, and drive innovation. Data analytics has the potential to transform every aspect of a business. Leveraging data analytics is no longer just an advantage but a necessity for sustainable growth and success. By investing in data analytics capabilities, businesses can unlock new opportunities, mitigate risks, and achieve their strategic objectives more effectively. Read on to delve into the myriad benefits of data analytics, including how it can transform your business and drive sustainable growth. Understanding Data Analytics Data analytics refers to the process of examining data sets to draw conclusions about the information they contain. It involves applying algorithms, statistical techniques, and machine learning models to uncover patterns, correlations, and insights that can inform decision-making. Data analytics can be categorized into four main types: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive Analytics: This involves summarizing historical data to understand what has happened in the past. It includes data aggregation and data mining to provide insights into past performance. Diagnostic Analytics: This focuses on understanding why something happened. It involves data discovery, drill-down, and correlations to identify the root causes of past outcomes. Predictive Analytics: This type of analytics uses historical data to forecast future events. Techniques such as regression analysis, time series analysis, and machine learning are employed to predict future trends and behaviors. Prescriptive Analytics: This goes a step further by recommending actions to achieve desired outcomes. It involves optimization and simulation algorithms to suggest the best course of action based on the predictive analysis. Data Analytics Benefits for Business Enhanced Decision-Making One of the most significant benefits of data analytics is its ability to enhance decision-making. By analyzing historical data, businesses can identify trends and patterns that inform strategic decisions. This data-driven approach minimizes the reliance on gut feelings and intuition, leading to more accurate and reliable outcomes. For example, a retail company can analyze sales data to determine which products are performing well and adjust their inventory accordingly. This not only reduces the risk of stockouts but also ensures that capital is not tied up in slow-moving products. Improved Operational Efficiency In terms of operational efficiency, the importance of data analytics in business cannot be overstated. Data analytics can streamline operations by identifying bottlenecks and inefficiencies within business processes. By examining process data, businesses can pinpoint areas where resources are being wasted and implement changes to enhance efficiency. For instance, a manufacturing company can use data analytics to monitor machine performance and predict maintenance needs, reducing downtime and increasing productivity. Similarly, logistics companies can optimize delivery routes based on traffic patterns and delivery schedules, resulting in faster deliveries and lower fuel costs. Personalized Customer Experiences In an era where customer experience is paramount, data analytics provides businesses with the tools to deliver personalized experiences. By analyzing customer data, businesses can gain insights into individual preferences, behaviors, and purchase histories. This enables the creation of targeted marketing campaigns and personalized product recommendations, enhancing customer satisfaction and loyalty. For example, an e-commerce platform can analyze browsing and purchase data to recommend products that align with a customer's interests, increasing the likelihood of repeat purchases. Risk Management and Mitigation Every business faces risks, whether they are financial, operational, or market-related. The benefits of big data analytics include helping businesses identify and mitigate these risks by providing a comprehensive understanding of potential threats. For instance, financial institutions can use data analytics to detect fraudulent activities by analyzing transaction patterns and flagging anomalies. Similarly, businesses can analyze market data to anticipate changes in demand and adjust their strategies accordingly, reducing the impact of market volatility. Optimized Marketing Campaigns Marketing is a critical area where data analytics’ importance is significant. By analyzing data from various sources such as social media, website traffic, and customer interactions, businesses can gain insights into the effectiveness of their marketing campaigns. This allows for the optimization of marketing strategies, ensuring that resources are allocated to the most effective channels. For example, a company can analyze the performance of different ad campaigns to determine which ones are generating the highest return on investment (ROI) and adjust their budget allocation accordingly. Cost Reduction Data analytics can lead to significant cost reductions by identifying areas where expenses can be minimized without compromising quality. For instance, by analyzing procurement data, businesses can identify suppliers that offer the best value for money and negotiate better contracts. Additionally, data analytics can help optimize inventory management, reducing carrying costs and minimizing waste. For example, a company can use data analytics to forecast demand accurately, ensuring that they order the right amount of stock and avoid overproduction. Competitive Advantage In a competitive business environment, gaining an edge over rivals is crucial. Data analytics provides businesses with insights that can drive innovation and differentiate them from the competition. By analyzing market trends and customer feedback, businesses can identify unmet needs and develop new products or services to address them. Additionally, data analytics can help businesses benchmark their performance against competitors, identifying areas where they can improve and capitalize on opportunities. Enhanced Employee Performance and Satisfaction Data analytics is not only beneficial for external operations but also for internal processes. By analyzing employee performance data, businesses can identify top performers and areas where additional training may be needed. This enables the implementation of targeted development programs that enhance employee skills and productivity. Additionally, data analytics can help improve employee satisfaction by analyzing feedback and identifying factors that contribute to a positive work environment. For example, by analyzing survey data, a company can identify common employee concerns and implement changes to address them, leading to higher retention rates. Innovation and Product Development Data analytics can drive innovation by providing insights into customer needs and market trends. By analyzing data from various sources, businesses can identify gaps in the market and develop new products or services to meet those needs. This data-driven approach to innovation ensures that new offerings are aligned with customer preferences and have a higher likelihood of success. For instance, a tech company can analyze user feedback and usage data to develop new features for their software products, enhancing user satisfaction and driving adoption. Supply Chain Optimization Data analytics can significantly enhance supply chain management by providing visibility into every stage of the supply chain. Businesses can analyze data related to suppliers, inventory levels, transportation, and demand forecasts to optimize their supply chain operations. For example, a logistics company can use data analytics to optimize delivery routes, reduce fuel consumption, and improve delivery times. This not only lowers operational costs but also enhances customer satisfaction by ensuring timely deliveries. Human Resources Management Data analytics can transform human resources management by providing insights into employee performance, engagement, and retention. By analyzing HR data, businesses can identify trends and patterns that inform hiring decisions, employee development programs, and retention strategies. For example, an organization can use data analytics to identify the factors that contribute to high employee turnover. By addressing these factors, the organization can improve employee satisfaction and retention rates. Regulatory Compliance In many industries, regulatory compliance is a critical concern. Data analytics can help businesses ensure compliance with various regulations by providing a comprehensive view of their operations and identifying areas where they may be falling short. For example, in the healthcare industry, data analytics can be used to monitor patient records and ensure that they are being handled in accordance with privacy regulations. Similarly, in the financial sector, data analytics can help monitor transactions for compliance with anti-money laundering (AML) regulations. Real-World Examples of How Data Analytics Help Business Walmart, the world's largest retailer, uses data analytics extensively to optimize its operations and enhance customer experience. The company collects vast amounts of data from its stores and online channels, which it analyzes to make data-driven decisions. For example, Walmart uses predictive analytics to forecast demand and manage inventory levels, ensuring that products are available when customers need them. This has helped Walmart reduce stockouts and improve customer satisfaction. Kaiser Permanente Kaiser Permanente, a leading healthcare provider, leverages data analytics to improve patient care and operational efficiency. The organization uses data analytics to monitor patient outcomes, identify trends, and develop personalized treatment plans. By analyzing patient data, Kaiser Permanente can identify high-risk patients and intervene early to prevent complications. This has led to improved patient outcomes and reduced healthcare costs. Capital One Capital One, a major financial institution, uses data analytics to enhance its risk management and customer experience. The company analyzes transaction data to detect fraudulent activities and prevent financial losses. Additionally, Capital One uses data analytics to create personalized offers and recommendations for its customers, improving customer satisfaction and loyalty. Final Thoughts on Data Analytics in Business Data analytics offers many benefits for businesses across various industries. From enhanced decision-making and operational efficiency to improved customer experiences and risk management, data analytics has the potential to transform business operations and drive growth. By leveraging the power of data, businesses can uncover new opportunities, innovate, and gain a competitive edge in the market. As the volume of data continues to grow, the importance of data analytics in business will only increase, making it an essential tool for success in the modern business landscape. Businesses that embrace this powerful tool will be well-positioned to navigate the complexities of the modern business landscape and thrive in an ever-evolving market. Whether you are a small startup or a large enterprise, the insights gained from data analytics can provide the foundation for informed decision-making, strategic planning, and long-term success. Learn more about the University of Miami UOnline Master of Science in Data Analytics and Program Evaluation . 800-411-2290 800-411-2290 UM News and Events Alumni & Friends 'Cane Watch Tools and Resources Academic Calendar Department Search Parking & Transportation social-facebook social-twitter social-youtube social-instagram Copyright: 2024 University of Miami. All Rights Reserved. Emergency Information Privacy Statement & Legal Notices Title IX & Gender Equity Website Feedback Individuals with disabilities who experience any technology-based barriers accessing the University’s websites or services can visit the Office of Workplace Equity and Inclusion . Canalys is part of Informa PLC Informa PLC Investor relations This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC’s registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726. AI-capable PCs: current market trends, future opportunities and potential challenges    Get your complimentary report! Select language * the site will be machine translated 14% of PCs shipped globally in Q2 2024 were AI-capable Tuesday, 13 August 2024 According to the latest Canalys data, 8.8 million AI-capable PCs were shipped in Q2 2024. These devices are defined as desktops and notebooks that include a chipset or block for dedicated AI workloads, such as an NPU. Shipments of AI-capable PCs represented 14% of all PCs shipped in the quarter. With all major processor vendors’ AI-capable PC roadmaps now well underway, the stage is set for a significant ramp-up in device availability and end-user adoption in the second half of 2024 and beyond.  “The second quarter of 2024 added significant momentum to the expansion of AI-capable PCs,” said Ishan Dutt, Principal Analyst at Canalys. “June saw the launch of Copilot+ PCs incorporating Qualcomm’s Snapdragon X series of chips, based on Arm architecture. While shipment volumes in the quarter were relatively small due to the limited weeks and geographical coverage of availability, the broad commitment of Windows OEMs to adopt these products into their portfolios bodes well for the category’s outlook. In the x86 space, Intel ramped up its delivery of Core Ultra chipsets, reporting strong sequential performance for its AI PC products, while AMD announced its Ryzen AI 300 series of notebook processors in June, with product releases starting in mid-July.”    “With a strong foundation now set, AI-capable PC shipments are poised to gain further traction in the second half of 2024,” added Dutt. “Processor vendors and OEMs are set to target a wider base of customers through new product category availability across more price points. Meanwhile, channel partners are signaling a preference for AI-related features in PCs, with close to 60% of respondents in a May poll indicating that they expect customers to favor devices with a Copilot key. The market performance of AI-capable PCs has largely aligned with expectations and the industry remains on track to ship around 44 million units in 2024 and 103 million units in 2025, according to Canalys forecasts.”  Vendor and platform highlights  With its entire Mac portfolio incorporating M-series chips with the Neural Engine, Apple boasts the highest AI-capable PC shipments and share of the portfolio currently. The announcement of Apple Intelligence, which is now in the developer beta stage in the US, brought greater clarity to AI use cases for Mac. When the features go live, they will be compatible with the vast majority of the existing Mac installed base, providing Apple with rapid scaling of users’ exposure to its AI experiences.    In the Windows space, AI-capable PC shipments grew 127% sequentially. Lenovo, the highest shipping PC vendor globally, made its foray into Snapdragon X powered PCs with the Yoga Slim 7x and ThinkPad T14s, helping to boost the AI-capable share of its total Windows PC shipments to around 6% in the quarter. This represents a sequential growth of 228% for its AI-capable PC shipments. HP achieved around 8% AI-capable PC share within its Windows shipments, with the launch of the Omnibook X 14 and EliteBook Ultra G1 Snapdragon Copilot+ PCs on top of its broadening offerings of Core Ultra devices across various product lines. Dell had just below 7% of its Windows shipments represented by AI-capable PCs, backed by its strong commercial presence. It launched Copilot+ PCs across its XPS, Latitude and Inspiron lines but with staggered availability.    “A key benefit from AI-capable PCs that has materialized for PC OEMs is the growth boost within their premium offerings,” added Dutt. “Windows PC shipments in the above US$800 range grew 9% sequentially in Q2 2024, with AI-capable PC shipments in those price bands up 126%. As the range of features from first- and third-party applications that leverage the NPU increase and the benefits to performance and efficiency become clearer, the value proposition for AI-capable PCs shall remain strong. This is especially important over the next 12 months as a significant portion of the installed base will be refreshed as part of the ongoing Windows upgrade cycle.”   Canalys AI-capable PC definition:   At a minimum, an AI-capable PC must be a desktop or notebook possessing a dedicated chipset or block to run on-device AI workloads. Examples of these dedicated chipsets include AMD’s XDNA, Apple’s Neural Engine, Intel’s AI Boost and Qualcomm’s Hexagon.  Further reading:  Canalys special report: Now and Next for AI-capable PC    For more information, please contact:   Ishan Dutt:  [email protected]   About PC Analysis Canalys’ PC Analysis service provides quarterly updated shipment data to help with accurate market sizing, competitive analysis and identifying growth opportunities in the market. Canalys PC shipment data is granular, guided by a strict methodology, and broken down by market, vendor and channel, with additional splits, such as GPU, CPU, storage and memory. In addition, Canalys also publishes quarterly forecasts to help better understand the future trajectory and changing landscape of the PC industry.   About Canalys Canalys is an independent analyst company that strives to guide clients on the future of the technology industry and to think beyond the business models of the past. We deliver smart market insights to IT, channel and service provider professionals around the world. We stake our reputation on the quality of our data, our innovative use of technology and our high level of customer service. Receiving updates To receive media alerts directly, or for more information about our events, services or custom research and consulting capabilities, please contact us . Alternatively, you can email [email protected] . Please click here to unsubscribe Copyright © Canalys. All rights reserved. Now and next for AI-capable PCs Revolutionizing computing: ai pcs and the market outlook, you may be interested in. Worldwide tablet shipments up 18% in Q2 2024  Global PC market recovery continues with 3% growth in Q2 2024 US PC market set for 5% growth in 2024 amid a healthy recovery trajectory  India's PC and tablet market poised for 11% growth in 2024  To receive media alerts directly, or for more information about our events, services or custom research and consulting capabilities, please contact us. Alternatively, you can email [email protected]. Privacy Preference Center Your privacy, strictly necessary cookies, performance cookies. When you visit any website, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences or your device and is mostly used to make the site work as you expect it to. The information does not usually directly identify you, but it can give you a more personalized web experience. Because we respect your right to privacy, you can choose not to allow some types of cookies. 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If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance. Community Banking Studies Community Banking Research Program Acknowledgements Information Package Reference Data Research & Reports Community Bank Search Note: On June 1, 2022, the text in Appendix A: Study Definitions (p. A-1) was modified slightly from the original online version posted on December 15, 2020, and from the printed version. The wording for the second item in the “Exclude” section was changed to “Assets held in foreign branches >=10% of total assets” from “Foreign Assets >= 10% of total assets.” The FDIC's 2020 Community Banking Study is a data-driven effort to identify and explore issues and questions about community banks. This study is intended to be foundational, providing a platform for future research and analysis by the FDIC and other interested parties. All items below are PDF files. See for assistance.  - Community Bank Financial Performance  - Structural Change Among Community and Noncommunity Banks  - The Effects of Demographic Changes on Community Banks  - Notable Lending Strengths of Community Banks  - Regulatory Change and Community Banks  - Technology in Community Banks  - Study Definitions  - Selected Federal Agency Actions Affecting Community Banks, 2008–2019 FDIC Targeted Research on Community Banks - As part of the overall Community Banking Study, the FDIC conducted targeted research about specific issues and questions related to community banks. Staff Studies Report No. 2020-06 Using financial and supervisory data from the past 20 years, we show that scale economies in community banks with less than $10 billion in assets emerged during the run-up to the 2008 financial crisis due to declines in interest expenses and provisions for losses on loans and leases at larger banks. The financial crisis temporarily interrupted this trend and costs increased industry-wide, but a generally more cost-efficient industry re-emerged, returning in recent years to pre-crisis trends. We estimate that from 2000 to 2019, the cost-minimizing size of a bank’s loan portfolio rose from approximately $350 million to $3.3 billion. Though descriptive, our results suggest efficiency gains accrue early as a bank grows from $10 million in loans to $3.3 billion, with 90 percent of the potential efficiency gains occurring by $300 million.  - The FDIC Community Banking study relies on a comprehensive database that enables consistent analysis across the industry beginning in 1984. The FDIC's 2012 Community Banking Study is a data-driven effort to identify and explore issues and questions about community banks. This study is intended to be foundational, providing a platform for future research and analysis by the FDIC and other interested parties.. All items below are PDF files. See for assistance.  - Defining the Community Bank  - Structural Change Among Community and Noncommunity Banks  - The Geography of Community Banks  - Comparative Financial Performance: Community versus Noncommunity Banks  - Comparative Performance of Community Bank Lending Specialty Groups  - Capital Formation at Community Banks  - Details of the Research Definition of the Community Bank  - Regulatory Compliance Costs - A Summary of Interviews with Community Bankers FDIC Targeted Research on Community Banks - As part of the overall Community Banking Study, the FDIC conducted targeted research about specific issues and questions related to community banks. This paper examines efficiency ratio trends to identify the underlying reasons for the performance game between community and noncommunitybanks. The paper also estimates the importance of economies of scale among community banks. This paper controls for underlying economic conditions to identify bank-specific factors that affect community bank performance.  - The FDIC Community Banking study relies on a comprehensive database that enables consistent analysis across the industry beginning in 1984. Last Updated: June 2, 2022 Open access Published: 14 August 2024 Mapping ultra-processed foods (UPFs) in India: a formative research study Suparna Ghosh-Jerath   ORCID: orcid.org/0000-0002-2229-4455 1   na1 , Neha Khandpur 2   na1 , Gaurika Kumar 3 , Sahiba Kohli 1 , Meenu Singh 3 , Inderdeep Kaur Bhamra 3 , Fernanda H Marrocos-Leite 4 & K Srinath Reddy 3   BMC Public Health volume  24 , Article number:  2212 ( 2024 ) Cite this article Metrics details Increased consumption of ultra-processed foods (UPFs) which have additives such as artificial colours, flavours and are usually high in salt, sugar, fats and specific preservatives, are associated with diet-related non-communicable diseases (NCDs). In India, there are no standard criteria for identifying UPFs using a classification system based on extent and purpose of industrial processing. Scientific literature on dietary intake of foods among Indian consumers classifies foods as unhealthy based on presence of excessive amounts of specific nutrients which makes it difficult to distinguish UPFs from other commercially available processed foods. A literature review followed by an online grocery retailer scan for food label reading was conducted to map the types of UPFs in Indian food market and scrutinize their ingredient list for the presence of ultra-processed ingredients. All UPFs identified were randomly listed and then grouped into categories, followed by saliency analysis to understand preferred UPFs by consumers. Indian UPF categories were then finalized to inform a UPF screener. A lack of application of a uniform definition for UPFs in India was observed; hence descriptors such as junk-foods , fast-foods , ready-to-eat foods , instant-foods , processed-foods , packaged-foods , high-fat-sugar-and-salt foods were used for denoting UPFs. After initial scanning of such foods reported in literature based on standard definition of UPFs, an online grocery retailer scan of food labels for 375 brands (atleast 3 brands for each food item) confirmed 81 food items as UPFs. A range of packaged traditional recipes were also found to have UPF ingredients. Twenty three categories of UPFs were then developed and subjected to saliency analysis. Breads, chips and sugar-sweetened beverages (e.g. sodas and cold-drinks) were the most preferred UPFs while frozen ready-to-eat/cook foods (e.g. chicken nuggets and frozen kebabs) were least preferred. India needs to systematically apply a food classification system and define Indian food categories based on the level of industrial processing. Mapping of UPFs is the first step towards development of a quick screener that would generate UPF consumption data to inform clear policy guidelines and regulations around UPFs and address their impact on NCDs. Peer Review reports Non-communicable diseases (NCDs) are one of the leading causes of premature morbidity and mortality resulting in over 7 out of 10 deaths worldwide [ 1 ]. Mortality due to NCDs has been on the rise in India, increasing from 37.9% of all deaths in 1990 to 61.8% in 2016 [ 2 , 3 ]. Overweight/obesity have been identified as a contributing factor [ 4 ]. The recent national-level data shows an increase of 25% in the prevalence of overweight and obesity among Indian men and women over 14–15 years and 3% among children under five years [ 5 , 6 ]. Due to their thin fat phenotype, Indian infants and children, who comprise almost one quarter of the total population, are predisposed to obesity [ 7 , 8 ]. These risk factors are further amplified by changing food environments and behavioural variables such as tobacco, alcohol, drug use and low physical activity [ 9 ]. Exposure to unhealthy food environments in genetically predisposed children, along with other behavioural risk factors, increases their risk of developing obesity and diet-related non-communicable diseases (DR-NCDs) in the long term [ 10 ]. The rapidly changing food environment is characterized by diets transitioning from minimally-processed staple foods (such as pulses and whole cereals) high in vitamins, minerals and fibre to refined, processed and ultra-processed foods (UPFs) [ 11 ]. The Indian population is exposed to a wide variety of UPFs which are hyper-palatable, packaged, convenient, affordable and have a long shelf life, such as sugar-sweetened beverages, chips, biscuits and bread, and ready-to-eat/ ready-to-cook (RTC) meals [ 12 ]. The sales data of UPFs in India demonstrates an exponential increase, from USD 0.9 billion in 2006 to USD 37.9 billion in 2019 [ 13 ]. This growth indicates a notable expansion of these food products in the market, coupled with widespread advertising efforts that specifically target vulnerable populations, including children and youth [ 14 , 15 , 16 , 17 , 18 ]. Consumer demand for UPFs has increased due to higher disposable incomes, nuclear families, single-member households, and less availability of time for housework [ 19 , 20 ]. UPFs have penetrated the rural boundaries of the country and are likely making their way into households of diverse geographic and socio-economic attributes [ 21 , 22 ]. The Nova food classification system categorizes foods based on the purpose and the level of processing and includes four categories: (i) unprocessed/ minimally processed foods, (ii) processed culinary ingredients, (iii) processed foods, and (iv) ultra-processed foods [ 23 , 24 , 25 , 26 ]. UPFs are a category of food that undergo a series of industrial processes like extrusion and moulding, and have presence of classes of additives whose function is to make the final product palatable or more appealing, such as flavours, flavour enhancers, colours, emulsifiers, thickeners, sweeteners, etc. Although not unique to UPF, they also include additives that prolong the product duration and protect original properties or prevent proliferation of microorganisms [ 23 , 24 , 25 , 26 ]. In addition to this, several of these products are high in saturated fats or trans-fats, added sugars, and salt and low in dietary fibre, various micronutrients and other bioactive compounds [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Overconsumption of UPFs has been associated with higher body mass index (BMI), obesity, type-2 diabetes, hypertension, cardiovascular diseases, and certain types of cancers [ 36 , 37 , 38 ]. Given the diversity in UPFs, there is a need to systematically map the range of UPFs accessed by the Indian population. This is an important first step to understanding their potential role in contributing to the NCD burden in India and in developing strategies to encourage the substitution of the most frequently consumed UPFs to healthier alternatives. Identifying specific UPF categories could also help inform the development of dietary assessment instruments like food frequency questionnaires (FFQ) and screeners. The present study aimed at: (i) mapping the specific categories of UPFs accessed and consumed in India, (ii) assessing the ingredient composition of these products, (iii) ranking the UPFs by consumer preference, and (iv) developing a list of categories of UPFs commonly consumed in India. For this, a secondary review of available literature complemented by an online grocery retailer scan and a saliency analysis were conducted between April 2021 and February 2022 (Fig.  1 ). Flow chart briefly explaining the 3 steps of methodology Step 1. The literature review was conducted to map and identify the various types of UPFs accessed, consumed, preferred and/ or purchased (as reported behaviours) in India. This review included published cross-sectional and observational research studies that used surveys, focus group discussions and interviews to elicit reported behaviours across different population groups and regions in India. International and national survey reports on UPF food intake and purchase among Indian population were also included in the review. Articles for review were identified from two electronic databases (NLM NCBI and Google Scholar). To ensure the search captured the diversity of UPFs, search terms included proxy descriptors identified in Indian policy documents [ 39 , 40 , 41 , 42 , 43 , 44 ], including: junk food* , fast food* , modern food* , westernized food* , ultra-processed food* , UPF* , convenience food* , ready-to-eat food* , ready-to-eat snack* , ready-to-cook food* , instant food* , frozen food* , canned food* , tinned food* , processed food* , packaged food* , high fat , sugar and salt food* and HFSS*. The literature search and data extraction was conducted by two authors (MS and GK). To be eligible for the review, studies needed to: (i) include UPFs or their proxy descriptors, with examples of products, (ii) be conducted in either rural and/or urban areas of India, (iii) be published in the English language, between January 2012 and December 2022. This time frame was chosen to capture the high growth in UPFs sales during this decade [ 45 , 46 ]. Review articles, and publications that did not define the food category studied or did not cite any examples of foods were excluded. Data from eligible articles were extracted in MS-Excel to record key variables on UPFs or their proxy descriptors with examples, location of the study (national/specific state), geographical area (urban/rural), sample size, sampling method, study participants’ age (years) and dietary data collection tools such as 24 h dietary recalls, FFQ, interviews and structured questionnaires (Additional file 1 ). A free list of UPF foods and beverages identified from the reviewed studies, was developed. Step 2. An online grocery retailer scan for extracting detailed information on the UPFs identified in Step 1, was also conducted. The objective was to review and scrutinize the ingredient list provided in the food labels and to confirm that the food item qualified as UPFs. For this online scan, three researchers (GK, IKB, MS) reviewed the online grocery websites of the largest grocery retailers in India - Big Basket, Grofers, and Amazon [ 47 ]. Individual foods and beverages from Step 1, were checked for their ingredient composition and the presence of additives. This activity was guided by the FAO document ‘Ultra-processed foods, diet quality, and health using the Nova classification system’ [ 23 , 24 , 25 , 26 ] and the expertise of the co-authors (NK and FHML). Food items were specifically scrutinized for the use of additives (flavours, flavour enhancers, colours, emulsifiers, emulsifying salts, artificial sweeteners, thickeners, foaming, anti-foaming, bulking, carbonating, gelling and glazing agents), specific ingredients such as industrially derived sugars (fructose, invert sugar, maltodextrin, dextrose, lactose, high fructose corn syrup, fruit juice concentrates), modified oils (hydrogenated fats, interesterified fats), extracted proteins (hydrolysed proteins, soy protein isolate, gluten, casein, whey protein, mechanically separated meat) [ 23 , 24 , 25 , 26 , 48 ]. All food items were assessed for at least three different brands and if a majority of the items (2 out of 3) qualified as UPFs, the product category was confirmed as UPF. The free-list of UPFs identified from the literature and confirmed through label reading using the online grocery retailer scan were then categorized on the basis of the primary ingredient of their composition and/or functionality of the product. A 23 category UPF list was developed at the end of Step 2. Step 3. The confirmed UPF categories (23 categories), were then subjected to a saliency analysis, conducted by two authors (GK, MS). Saliency is a statistical accounting of items for rank and frequency of mention, across all respondents within a given domain. For example, the colour chosen most often from a free list of ten colours by a study population is referred to as the most salient [ 49 ]. The saliency test indicators included the commonly accessed, consumed, preferred and/or purchased (collectively referred to as ‘preferred’ in this paper to identify the common UPF categories accessed by the Indian consumers). These categories were limited to the food items that were confirmed as UPFs in Step 2. For example, if a study used “junk food” as a descriptor of UPFs and included freshly prepared savouries like “samosa/kachori” along with chips and soft drinks, we included data for only chips and soft drinks for the purpose of saliency analysis. The UPFs were then sorted from the most to the least preferred UPFs (Additional file 2 ). The steps and formulas [ 49 ] used to calculate composite salience scores for each UPF category have been illustrated in Fig.  2 . The UPF categories were classified per consumer preference, to the composite salience score cut-offs, defined after dividing the distribution of the composite salience scores obtained into tertiles as follows: (i) ≥ 0.61 as frequently preferred, (ii) 0.61 − 0.51 as infrequently preferred, and (iii) < 0.51 as rarely preferred UPFs. Saliency analysis method for free-listed UPF categories The literature search and study selection process of Step 1 is illustrated in Fig.  3 . A total of 23 research articles that matched the inclusion criteria were included in the final review. An overview of the extracted variables is provided in Additional File 1 . These studies were conducted in both rural (5 out of 23) and urban areas (17 out of 23), in different regions of the country, among a diverse population aged between 9 and 69 years (Table  1 ). Table  2 provides the outcome of the literature review with proxy descriptors along with the food items listed under them. These foods were verified as UPFs and non-UPFs. Flow diagram reporting the screening and selection of studies reporting consumption and availability of UPFs in India The online grocery retailer scan, label readings of 375 packaged foods were completed (atleast 3 brands per product) and 81 of those food products qualified as UPFs. Several of the packaged Indian traditional foods and snacks such as bottled and packaged pickles, namkeens (cereal and pulse-based extruded snacks), papads , frozen non-vegetarian meals and snacks, and frozen RTE meals (like rajmah curry and rice, biryani , dal makhni , etc.) had UPF ingredients and additives in their formulation that qualified them as UPFs (Table  3 ). Food products such as RTE breakfast cereals (e.g. poha , upma , etc.), RTE Indian curries (e.g. paneer makhani , butter chicken, etc.), Indian RTE bread (e.g. thepla , paratha , etc.), RTC mixes (e.g. idli mix, dal vada mix, etc.) also qualified as UPFs. However, some RTE traditional meals such as RTE biryani , RTE rajmah curry with rice, RTE kadhi pakoda with rice were not categorized as UPFs as these did not include UPF ingredients. Consumer preferences for the confirmed UPF food categories identified above, were assessed using saliency scores. Table  4 lists these categories and shows the order of preference based on the saliency scores. The last column in the table indicates ‘frequently’, ‘infrequently’ or ‘rarely’ preferred UPFs by consumers in India. The frequently preferred UPFs were breads, chips and other extruded snacks (such as potato chips, cheese balls, puff corns, etc.) and sugar-sweetened beverages (such as cold drinks, diet coke, sodas, and energy drinks. The three rarely preferred UPFs were margarine and frozen/ packaged vegetarian and non-vegetarian snacks and meals (such as stuffed/plain parantha , naan , palak paneer , rajma , cutlets, fish/seafood snacks, salami, and sausages). The present study aimed to identify the specific categories of UPFs accessed in India and rank them by consumer preference using a literature review, an online grocery retailer scan and saliency analysis. We found 23 categories of UPFs accessed by Indian consumers. After analysing the ingredient list of UPFs, we found that product formulation of several traditional Indian foods has transitioned from being processed to ultra-processed category with the use of industrially processed ingredients and presence of additives such as artificial colours, flavour enhancers, anti-caking agents. These ultra-processed versions of traditional foods even though have similar nutrient composition to home-prepared meals, are increasingly consumed, displacing home-cooked meals, and substituting staples. While the health effects of this displacement from minimally processed food ingredients to UPFs is an area of on-going research, we have growing evidence that UPF dietary patterns are linked to poor health outcomes [ 23 , 69 ]. It is crucial to track reformulation of traditional recipes to ultra-processed convenience foods especially since traditional meals are thought to be healthier [ 70 ]. The increasing market of ultra-processed traditional Indian recipes with poor nutritional profile needs more scrutiny and research. Saliency analysis identified the preferred UPFs among the Indian population with breads, chips and sugar-sweetened beverages being the most preferred UPFs and frozen non-vegetarian snacks being the least preferred. This finding is consistent with the sales trends reported by Euromonitor International in 2020, which has also highlighted a substantial contribution of similar categories of packaged foods, such as bakery items, biscuits, packaged dairy products, savory snacks, and sauces and condiments [ 46 ]. Further, saliency analysis also indicates the preference of Indian consumers towards UPFs such as fruit-based preserves, cookies and biscuits, Indian sweet mixes, sauces and pickles, instant noodles/soups/ pasta and savoury puff rolls. Studies from other low and middle-income countries (LMICs) demonstrates similar trends in preference (consumption of UPFs and contribution to percentage of total calories) towards packaged confectioneries, savoury snacks, deep-fried foods, biscuits, candy/ chocolate, savoury snacks, canned red and luncheon meats, pre-fried French fries, mayonnaise, ketchup, fast-food such as sandwiches and pizzas, chips and salty snacks (including tortillas and pretzels), sweets and sweetened beverages and sausages (including canned) [ 71 , 72 ]. Our results also suggest a benefit of utilizing a classification system based on processing. Currently several UPFs are being captured by proxy descriptors like junk foods, fast foods, convenience foods, instant foods, packaged foods, etc. This limits comparability with other studies, monitoring the preference for and consumption of these products by the population, developing targeted interventions, tracking product reformulation and other regulatory measures to control exposure of these foods to vulnerable age groups through food advertising, etc. [ 73 ]. Using UPFs more consistently in studies reporting unhealthy food consumption pattern in India will help with global comparisons and in also elucidating the health effects of these foods. Additionally, as per the packaged food sales data from 2015-19, the Indian UPF market is slowly expanding with increasing sales of RTE meals, savoury snacks, processed fruits, vegetables, meats and other packaged foods [ 46 ]. The Nova food classification system can serve this purpose and may be explored as an option for categorization of foods by regulatory authorities. This classification system is used to assess dietary patterns in several high and middle-income countries [ 23 , 70 ]. Food based dietary guidelines of several countries such as Brazil, Uruguay, Ecuador, Peru and Israel have utilized Nova classification system to inform their dietary recommendations [ 74 , 75 , 76 , 77 , 78 ]. The present paper identified only a limited number of Indian studies which were primarily reported from 2 geographical regions. More such surveys on the consumption of UPFs are desirable to identify common regional UPFs. In the Indian context, several UPFs are indigenously produced by local retailers apart from the huge market share of nationally known branded UPFs [ 79 ]. These locally accessible UPFs have greater penetration into the local markets. The categories of UPFs in India developed in the present study after due validation can be developed into a UPF consumption screener. This tool can be used for monitoring the UPF consumption in India and can address critical gap in scientific literature. This information on quantitative estimate of UPF consumption among Indian population can be useful for assessing impact of UPF consumption on increasing burden of NCDs in India. This study is one of the first attempts to explore the types of UPFs in the Indian food market, identify the types of packaged traditional recipes that have been converted to UPFs, and map their saliency. Study limitations Studies reviewed were majorly from South India and largely represented the urban population, hence the results cannot be extended to the rural population. The study could only conduct saliency mapping of preferred foods without quantity of intake of UPFs and their contribution to total day’s energy intake. We could not explore traditional variants of UPFs that may be sold in the local unregulated markets. Conclusions India needs to develop a food classification system while systematically defining food categories based on level of processing. This should be followed by an assessment of the extent of UPFs consumption in India. The mapping of the UPFs in India reported in this paper provides the first step in developing a quick screener that systematically lists all the UPF categories. The data generated on consumption of UPFs using the screener is likely to inform policies on regulating the Indian UPFs market, undertake consumer education initiatives and create nutrition literacy around UPFs and thus contain their indiscriminate consumption. This may address the impact of UPF consumption on increasing burden of NCDs in India. There is an urgent need for strengthening the food regulatory environment to check the infiltration of several unhealthy UPFs in the Indian food market. 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This work is funded through the Innovative Methods and Metrics for Agriculture and Nutrition Action (IMMANA) programme (Grant IMMANA 3.06), led by the London School of Hygiene & Tropical Medicine (LSHTM). IMMANA is co-funded with UK Aid from the UK government and by the Bill & Melinda Gates Foundation. This work was supported, in part, by the Bill & Melinda Gates Foundation [INV-002962 / OPP1211308]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Author information Suparna Ghosh-Jerath and Neha Khandpur contributed equally to this work. Authors and Affiliations The George Institute for Global Health, 308, Third Floor, Elegance Tower, Plot No. 8, Jasola District Centre, New Delhi, Delhi, 110025, India Suparna Ghosh-Jerath & Sahiba Kohli Wageningen University, Wageningen, The Netherlands Neha Khandpur Public Health Foundation of India, New Delhi, India Gaurika Kumar, Meenu Singh, Inderdeep Kaur Bhamra & K Srinath Reddy Center for Epidemiological Research in Nutrition and Health, Faculty of Public Health, University of Sao Paulo, Sao Paulo, Brazil Fernanda H Marrocos-Leite You can also search for this author in PubMed   Google Scholar Contributions The study was conceptualized by KSR, SGJ, NK and FHML. The literature review and online grocery retailer scan were conducted by MS, GK, IKB. The first draft of the manuscript was prepared by MS, GK, IKB, SK and SGJ. The manuscript was critiqued and edited by SGJ, NK, FHML and KSR. SGJ had primary responsibility for final content; and all authors read and approved the final manuscript. Corresponding author Correspondence to Suparna Ghosh-Jerath . Ethics declarations Ethics approval and consent to participate. This study was conducted according to guidelines laid down in the Declaration of Helsinki, and all procedures involving humans were approved by the Institutional Ethics Committee at the Public Health Foundation of India, and the ethics committee of University of Sao Paulo. The current manuscript, however, reports findings from an exhaustive literature review and online grocery retailer scan for which informed consent process is not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s note. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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Provided by the Springer Nature SharedIt content-sharing initiative Nova food classification UPF categories Online grocery retailer scan Tool adaptation Nova-UPF screener BMC Public Health ISSN: 1471-2458 General enquiries: [email protected] The creator economy could approach half-a-trillion dollars by 2027 The so-called “creator economy” has mushroomed and is expected to grow even more in the coming years, according to Goldman Sachs Research.  Individual people with their own brands and online audiences have emerged as one of the biggest developments of the digital age. The ecosystem is expanding for a number of reasons, including the increase in digital media consumption and the advent of technology that has lowered barriers to content creation, Eric Sheridan, senior equity research analyst covering the U.S. Internet sector, writes in the team’s report. New platforms such as TikTok have emerged, while legacy platforms like Facebook and YouTube have also introduced new formats for sharing short-form video, live streaming channels and other forms of user-generated content. As the ecosystem grows, the total addressable market of the creator economy could roughly double in size over the next five years to $480 billion by 2027 from $250 billion today, Sheridan writes. That growth is roughly in line with the team’s estimates for growth in global digital advertising spend over that period. The analysts expect spending on influencer marketing and platform payouts fueled by the monetization of short-form video platforms via advertising to be the primary growth drivers of the creator economy. Goldman Sachs Research expects the 50 million global creators to grow at a 10-20% compound annual growth rate during the next five years. Creators earn income primarily through direct branding deals to pitch products as an influencer; via a share of advertising revenues with the host platform; and through subscriptions, donations and other forms of direct payment from followers. Brand deals are the main source of revenue at about 70%, according to survey data. Only about 4% of global creators are deemed professionals, meaning they pull in more than $100,000 a year. Goldman Sachs Research expects their share of the creator universe to stay steady even as the overall ecosystem expands. Which companies will benefit the most from the ongoing growth of the creator economy? The platforms that are best positioned to attract both influential creators and a larger share of the total spending are those that will offer multiple forms of monetization, according to Goldman Sachs Research. But the analysts also cite six key enablers for creating a “flywheel effect” in which small gains build on each other over time and create further growth momentum: 1. Scale:  a large, global user base with diversified interests    2. Capital:  access to large pools of capital to fund monetization, either through a diversified revenue base and/or as part of a larger parent company 3. Strong AI-powered recommendation engines:  for surfacing relevant content and matching creators with interested users 4. Effective monetization tools:  a variety of product offerings/payout structures for creators to diversify their income streams 5. Robust data and analytics:  for providing transparency on engagement, retention, conversion and other metrics 6. E-commerce options:  the ability to shop is integrated into the core user experience At least at this point, the report points to the large incumbent platforms as being in the driver’s seat. Goldman Sachs Research sees more creators moving to these platforms as competition heats up for their content and audiences, particularly as macroeconomic uncertainty impacts brand spending and as rising interest rates pressure funding for emerging platforms. “As a result, we expect some element of a ‘flight to quality’ whereby creators will prioritize platforms with stability, scale and monetization potential,” Sheridan writes. This article is being provided for educational purposes only. The information contained in this article does not constitute a recommendation from any Goldman Sachs entity to the recipient, and Goldman Sachs is not providing any financial, economic, legal, investment, accounting, or tax advice through this article or to its recipient. 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Data Analysis in Research and its Importance 15 Data Analysis Examples (2024) What is Data Analysis in Research COMMENTS Data Analysis in Research: Types & Methods Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. Three essential things occur during the data ... Data analysis data analysis, the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data, generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making. What Is Data Analysis? (With Examples) What Is Data Analysis? (With Examples) Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims ... What is Data Analysis? An Expert Guide With Examples Data analysis is a comprehensive method of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It is a multifaceted process involving various techniques and methodologies to interpret data from various sources in different formats, both structured and unstructured. Guides: Data Analysis: Introduction to Data Analysis Data analysis can be quantitative, qualitative, or mixed methods. Quantitative research typically involves numbers and "close-ended questions and responses" (Creswell & Creswell, 2018, p. 3).Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures (Creswell & Creswell, 2018, p. 4). Data Analysis Definition: Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. Data analysis Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. [2] What is Data Analysis? (Types, Methods, and Tools) December 17, 2023. Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. In addition to further exploring the role data analysis plays this blog post will discuss common ... What Is Data Analysis? (With Examples) Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. ... This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. ... Learners are advised to conduct additional research to ensure that courses and ... What Is Data Analysis? (With Examples) Analyse the data. By manipulating the data using various data analysis techniques and tools, you can find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualisation software to help transform data into an easy-to-understand graphical ... What is Data Analysis? An Introductory Guide An Introductory Guide. Data analysis is the process of inspecting, cleaning, transforming, and modeling data to derive meaningful insights and make informed decisions. It involves examining raw data to identify patterns, trends, and relationships that can be used to understand various aspects of a business, organization, or phenomenon. A practical guide to data analysis in general literature reviews This article is a practical guide to conducting data analysis in general literature reviews. The general literature review is a synthesis and analysis of published research on a relevant clinical issue, and is a common format for academic theses at the bachelor's and master's levels in nursing, physiotherapy, occupational therapy, public health and other related fields. Data Analysis: Definition, Types and Examples Prescriptive analysis is a decision-making analysis that uses mathematical modeling, optimization algorithms, and other data-driven techniques to identify the action for a given problem or situation. It combines mathematical models, data, and business constraints to find the best move or decision. What is Data Analysis? Research, Types & Example Data Analysis Tools. Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research. Types of Data Analysis: Techniques and Methods What Is Data Analysis in Research? Why It Matters & What Data Analysts Data analysis in research is the process of uncovering insights from data sets. Data analysts can use their knowledge of statistical techniques, research theories and methods, and research practices to analyze data. They take data and uncover what it's trying to tell us, whether that's through charts, graphs, or other visual representations. The Beginner's Guide to Statistical Analysis Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations. To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process. You need to specify ... 8 Types of Data Analysis Exploratory analysis. Inferential analysis. Predictive analysis. Causal analysis. Mechanistic analysis. Prescriptive analysis. With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological ... Data Analysis in Research Data analysis in research is the systematic process of investigating facts and figures to make conclusions about a specific question or topic; there are two major types of data analysis methods in ... An Overview of Data Analysis and Interpretations in Research Research is a scientific field which helps to generate new knowledge and solve the existing problem. So, data analysis is the crucial part of research which makes the result of the study more ... Data Analysis Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures "provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present ... Research Guide: Data analysis and reporting findings Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. Qualitative Data Analysis and Interpretation: Systematic Search for Meaning Qualitative data analysis is. concerned with transforming raw data by searching, evaluating, recogni sing, cod ing, mapping, exploring and describing patterns, trends, themes an d categories in ... Statistical Analysis in Research: Meaning, Methods and Types A Simplified Definition. Statistical analysis uses quantitative data to investigate patterns, relationships, and patterns to understand real-life and simulated phenomena. The approach is a key analytical tool in various fields, including academia, business, government, and science in general. This statistical analysis in research definition ... Qualitative Observation Science Definition Explained Subjective data analysis plays a crucial role in qualitative research by providing rich, in-depth insights into participants' experiences. Understanding subjective data means interpreting the thoughts, feelings, and perspectives shared by individuals through methods like interviews, focus groups, or open-ended surveys. What are the Benefits of Data Analytics in the Business World? It involves data discovery, drill-down, and correlations to identify the root causes of past outcomes. Predictive Analytics: This type of analytics uses historical data to forecast future events. Techniques such as regression analysis, time series analysis, and machine learning are employed to predict future trends and behaviors. What does science tell us about boxing's gender row? Research is shedding light on different chromosomal make-ups and what advantages they may bring to sport. 14% of PCs shipped globally in Q2 2024 were AI-capable About PC Analysis. Canalys' PC Analysis service provides quarterly updated shipment data to help with accurate market sizing, competitive analysis and identifying growth opportunities in the market. Canalys PC shipment data is granular, guided by a strict methodology, and broken down by market, vendor and channel, with additional splits, such ... Community Banking Studies Analysis. The FDIC is proud to be a pre-eminent source of U.S. banking industry research, including quarterly banking profiles, working papers, and state banking performance data. Browse our extensive research tools and reports. More FDIC Analysis Mapping ultra-processed foods (UPFs) in India: a formative research The increasing market of ultra-processed traditional Indian recipes with poor nutritional profile needs more scrutiny and research. Saliency analysis identified the preferred UPFs among the Indian population with breads, chips and sugar-sweetened beverages being the most preferred UPFs and frozen non-vegetarian snacks being the least preferred. The creator economy could approach half-a-trillion dollars by 2027 Brand deals are the main source of revenue at about 70%, according to survey data. Only about 4% of global creators are deemed professionals, meaning they pull in more than $100,000 a year. Goldman Sachs Research expects their share of the creator universe to stay steady even as the overall ecosystem expands. assignment essay homework paper phd report resume review speech thesis