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Descriptive Analytics – Methods, Tools and Examples

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Descriptive Analytics

Definition:

Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.

Descriptive Analytics in Research

Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:

• Descriptive Statistics: In research, descriptive analytics often takes the form of descriptive statistics . This includes calculating measures of central tendency (like mean, median, and mode), measures of dispersion (like range, variance, and standard deviation), and measures of frequency (like count, percent, and frequency). These calculations help researchers summarize and understand their data.
• Visualizing Data: Descriptive analytics also involves creating visual representations of data to better understand and communicate research findings . This might involve creating bar graphs, line graphs, pie charts, scatter plots, box plots, and other visualizations.
• Exploratory Data Analysis: Before conducting any formal statistical tests, researchers often conduct an exploratory data analysis, which is a form of descriptive analytics. This might involve looking at distributions of variables, checking for outliers, and exploring relationships between variables.
• Initial Findings: Descriptive analytics are often reported in the results section of a research study to provide readers with an overview of the data. For example, a researcher might report average scores, demographic breakdowns, or the percentage of participants who endorsed each response on a survey.
• Establishing Patterns and Relationships: Descriptive analytics helps in identifying patterns, trends, or relationships in the data, which can guide subsequent analysis or future research. For instance, researchers might look at the correlation between variables as a part of descriptive analytics.

Descriptive Analytics Techniques

Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:

Statistical Analysis

This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).

Data Aggregation

It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.

Data Mining

This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).

Data Visualization

This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.

This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.

Cross-tabulation (or Pivot Tables)

It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.

Descriptive Modeling

Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.

Descriptive Analytics Tools

Some common Descriptive Analytics Tools are as follows:

Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.

Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.

QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.

Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.

R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.

Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

When to use Descriptive Analytics

Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:

Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.

Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.

Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.

Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.

Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.

Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.

Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.

Examples of Descriptive Analytics

Examples of Descriptive Analytics are as follows:

Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.

Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.

Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.

Advantages of Descriptive Analytics

Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:

• Understanding the Past: Descriptive analytics provides an understanding of what has happened in the past, offering valuable context for future decision-making.
• Data Summarization: Descriptive analytics is used to simplify and summarize complex datasets, which can make the information more understandable and accessible.
• Identifying Patterns and Trends: With descriptive analytics, organizations can identify patterns, trends, and correlations in their data, which can provide valuable insights.
• Inform Decision-Making: The insights generated through descriptive analytics can inform strategic decisions and help organizations to react more quickly to events or changes in behavior.
• Basis for Further Analysis: Descriptive analytics lays the groundwork for further analytical activities. It’s the first necessary step before moving on to more advanced forms of analytics like predictive analytics (forecasting future events) or prescriptive analytics (advising on possible outcomes).
• Performance Evaluation: It allows organizations to evaluate their performance by comparing current results with past results, enabling them to see where improvements have been made and where further improvements can be targeted.
• Enhanced Reporting and Dashboards: Through the use of visualization techniques, descriptive analytics can improve the quality of reports and dashboards, making the data more understandable and easier to interpret for stakeholders at all levels of the organization.
• Immediate Value: Unlike some other types of analytics, descriptive analytics can provide immediate insights, as it doesn’t require complex models or deep analytical capabilities to provide value.

Disadvantages of Descriptive Analytics

While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:

• Limited to Past Data: Descriptive analytics primarily deals with historical data and provides insights about past events. It does not predict future events or trends and can’t help you understand possible future outcomes on its own.
• Lack of Deep Insights: While descriptive analytics helps in identifying what happened, it does not answer why it happened. For deeper insights, you would need to use diagnostic analytics, which analyzes data to understand the root cause of a particular outcome.
• Can Be Misleading: If not properly executed, descriptive analytics can sometimes lead to incorrect conclusions. For example, correlation does not imply causation, but descriptive analytics might tempt one to make such an inference.
• Data Quality Issues: The accuracy and usefulness of descriptive analytics are heavily reliant on the quality of the underlying data. If the data is incomplete, incorrect, or biased, the results of the descriptive analytics will be too.
• Over-reliance on Descriptive Analytics: Businesses may rely too much on descriptive analytics and not enough on predictive and prescriptive analytics. While understanding past and present data is important, it’s equally vital to forecast future trends and make data-driven decisions based on those predictions.
• Doesn’t Provide Actionable Insights: Descriptive analytics is used to interpret historical data and identify patterns and trends, but it doesn’t provide recommendations or courses of action. For that, prescriptive analytics is needed.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Thank you. I will try my best to write my results.

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this was great explaination

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Descriptive Analysis: How-To, Types, Examples

We review the basics of descriptive analysis, including what exactly it is, what benefits it has, how to do it, as well as some types and examples.

From diagnostic to predictive, there are many different types of data analysis . Perhaps the most straightforward of them is descriptive analysis, which seeks to describe or summarize past and present data, helping to create accessible data insights. In this short guide, we'll review the basics of descriptive analysis, including what exactly it is, what benefits it has, how to do it, as well as some types and examples.

What Is Descriptive Analysis?

Descriptive analysis, also known as descriptive analytics or descriptive statistics, is the process of using statistical techniques to describe or summarize a set of data. As one of the major types of data analysis, descriptive analysis is popular for its ability to generate accessible insights from otherwise uninterpreted data.

Unlike other types of data analysis, the descriptive analysis does not attempt to make predictions about the future. Instead, it draws insights solely from past data, by manipulating in ways that make it more meaningful.

Benefits of Descriptive Analysis

Descriptive analysis is all about trying to describe or summarize data. Although it doesn't make predictions about the future, it can still be extremely valuable in business environments . This is chiefly because descriptive analysis makes it easier to consume data, which can make it easier for analysts to act on.

Another benefit of descriptive analysis is that it can help to filter out less meaningful data. This is because the statistical techniques used within this type of analysis usually focus on the patterns in data, and not the outliers.

Types of Descriptive Analysis

According to CampusLabs.com , descriptive analysis can be categorized as one of four types. They are measures of frequency, central tendency, dispersion or variation, and position.

Measures of Frequency

In descriptive analysis, it's essential to know how frequently a certain event or response occurs. This is the purpose of measures of frequency, like a count or percent. For example, consider a survey where 1,000 participants are asked about their favourite ice cream flavor. A list of 1,000 responses would be difficult to consume, but the data can be made much more accessible by measuring how many times a certain flavor was selected.

Measures of Central Tendency

In descriptive analysis, it's also worth knowing the central (or average) event or response. Common measures of central tendency include the three averages — mean, median, and mode. As an example, consider a survey in which the height of 1,000 people is measured. In this case, the mean average would be a very helpful descriptive metric.

Measures of Dispersion

Sometimes, it may be worth knowing how data is distributed across a range. To illustrate this, consider the average height in a sample of two people. If both individuals are six feet tall, the average height is six feet. However, if one individual is five feet tall and the other is seven feet tall, the average height is still six feet. In order to measure this kind of distribution, measures of dispersion like range or standard deviation can be employed.

Measures of Position

Last of all, descriptive analysis can involve identifying the position of one event or response in relation to others. This is where measures like percentiles and quartiles can be used.

How to Do Descriptive Analysis

Like many types of data analysis, descriptive analysis can be quite open-ended. In other words, it's up to you what you want to look for in your analysis. With that said, the process of descriptive analysis usually consists of the same few steps.

• Collect data

The first step in any type of data analysis is to collect the data. This can be done in a variety of ways, but surveys and good old fashioned measurements are often used.

Another important step in descriptive and other types of data analysis is to clean the data. This is because data may be formatted in inaccessible ways, which will make it difficult to manipulate with statistics. Cleaning data may involve changing its textual format, categorizing it, and/or removing outliers.

• Apply methods

Finally, descriptive analysis involves applying the chosen statistical methods so as to draw the desired conclusions. What methods you choose will depend on the data you are dealing with and what you are looking to determine. If in doubt, review the four types of descriptive analysis methods explained above.

When to Do Descriptive Analysis

Descriptive analysis is often used when reviewing any past or present data. This is because raw data is difficult to consume and interpret, while the metrics offered by descriptive analysis are much more focused.

Descriptive analysis can also be conducted as the precursor to diagnostic or predictive analysis , providing insights into what has happened in the past before attempting to explain why it happened or predicting what will happen in the future.

Descriptive Analysis Example

As an example of descriptive analysis, consider an insurance company analyzing its customer base.

The insurance company may know certain traits about its customers, such as their gender, age, and nationality. To gain a better profile of their customers, the insurance company can apply descriptive analysis.

Measures of frequency can be used to identify how many customers are under a certain age; measures of central tendency can be used to identify who most of their customers are; measures of dispersion can be used to identify the variation in, for example, the age of their customers; finally, measures of position can be used to compare segments of customers based on specific traits.

Final Thoughts

Descriptive analysis is a popular type of data analysis. It's often conducted before diagnostic or predictive analysis, as it simply aims to describe and summarize past data.

To do so, descriptive analysis uses a variety of statistical techniques, including measures of frequency, central tendency, dispersion, and position. How exactly you conduct descriptive analysis will depend on what you are looking to find out, but the steps usually involve collecting, cleaning, and finally analyzing data.

In any case, this business analysis process is invaluable when working with data.

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Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.

The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.

Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).

Some fields prefer to put means and standard deviations in parentheses like this:

If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.

If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.

At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.

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Descriptive Statistics | Definitions, Types, Examples

Published on 4 November 2022 by Pritha Bhandari . Revised on 9 January 2023.

Descriptive statistics summarise and organise characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population .

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalisable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, frequently asked questions.

There are 3 main types of descriptive statistics:

• The distribution concerns the frequency of each value.
• The central tendency concerns the averages of the values.
• The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

• Go to a library
• Watch a movie at a theater
• Visit a national park

A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarise the frequency of every possible value of a variable in numbers or percentages.

• Simple frequency distribution table
• Grouped frequency distribution table

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Measures of central tendency estimate the center, or average, of a data set. The mean , median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then, the median is the number in the middle. If there are two numbers in the middle, find their mean.

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

• List each score and find their mean.
• Subtract the mean from each score to get the deviation from the mean.
• Square each of these deviations.
• Add up all of the squared deviations.
• Divide the sum of the squared deviations by N – 1.
• Find the square root of the number you found.

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

If you were to only consider the mean as a measure of central tendency, your impression of the ‘middle’ of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to extreme values, you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read ‘across’ the table to see how the independent and dependent variables relate to each other.

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables. It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

Descriptive statistics summarise the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalisable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.
• Univariate statistics summarise only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

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Descriptive Analysis and Interpretation

• First Online: 15 September 2017

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• Karen A. Monsen 2  

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This chapter presents a step-by-step data analysis process beginning with data cleaning and preprocessing, which leads to and may include descriptive analyses of all variables. Once the data are clean and final descriptive statistics are completed, inferential statistics (Chap. 6 ) may be leveraged to better understand the practical and statistical significance of the data. Use of exploratory Data Analysis (Chap. 7 ) at any stage may reveal patterns in the data for subsequent formal hypothesis testing. Interpretation of the analysis is discussed for each of the methods. Your project may employ a number of complementary analysis methods based on study design, the variables representing the PIO MM concepts, and the measures used to operationalize them.

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Monsen, K.A. (2018). Descriptive Analysis and Interpretation. In: Intervention Effectiveness Research: Quality Improvement and Program Evaluation. Springer, Cham. https://doi.org/10.1007/978-3-319-61246-1_5

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Descriptive Analysis: What It Is + Best Research Tips

Leading statistical analysis usually begins with a descriptive analysis. It is also known as descriptive analytics or descriptive statistics. It helps you think about how to utilize your data, help you identify exceptions and mistakes, and see how variables are related, putting you in a position to lead future statistical research.

Keeping raw data in a format that makes it easy to understand and analyze, i.e., rearranging, sorting, and changing data so that it can tell you something useful about the data it contains.

Descriptive analysis is one of the most crucial phases of statistical data analysis. It provides you with a conclusion about the distribution of your data and aids in detecting errors and outliers. It lets you spot patterns between variables, preparing you for future statistical analysis.

In this blog, we will discuss descriptive analysis and the best tips for researchers.

What is Descriptive Analysis?

Descriptive analysis is a sort of data research that aids in describing, demonstrating, or helpfully summarizing data points so those patterns may develop that satisfy all of the conditions of the data.

It is the technique of identifying patterns and links by utilizing recent and historical data. Because it identifies patterns and associations without going any further, it is frequently referred to as the most basic data analysis .

When describing change over time, this analysis is beneficial. It utilizes patterns as a jumping-off point for further research to inform decision-making. When done systematically, they are not tricky or tiresome.

Data aggregation and mining are two methods used in descriptive analysis to generate historical data. Information is gathered and sorted in data aggregation to simplify large datasets. Data mining is the next analytical stage, which entails searching the data for patterns and significance. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

Types of Descriptive Analysis

A variety of empirical methodologies support practical descriptive analyses. The most popular descriptive work tools are simple statistics representing core trends and variations (such as means, medians, and modes), which may be highly useful for explaining data.

It is the responsibility of the descriptive researcher to condense the body of data into a form that the audience will find helpful. This data reduction does not mean a situation or phenomenon should be equally weighted in all its components.

Instead, it concentrates on the most critical aspects of the phenomenon as it is and, more generally, the context of real-world practice in which a research study is to be read. The four types of descriptive analysis methods are:

01. Measurements of Frequency

Understanding how often a particular event or reaction is likely to occur is crucial for descriptive analysis. The main goal of frequency measurements is to provide something like a count or a percentage.

02. Measures of Central Tendency

Finding the central (or average) tendency or response is crucial in descriptive analysis. Three standards—mean, median, and mode—are used to calculate central tendency.

03. Measures of Dispersion

At times, understanding how data is distributed throughout a range is crucial. This kind of distribution may be measured using dispersion metrics like range or standard deviation.

04. Measures of Position

Finding a value’s or response’s location concerning other matters is another aspect of descriptive analysis. In this area of knowledge, metrics like quartiles and percentiles are beneficial.

How to Conduct a Descriptive Analysis?

Descriptive analysis is an important phase in data exploration that involves summarizing and describing the primary properties of a dataset. It provides vital insights into the data’s frequency distribution, central tendency, dispersion, and identifying position. It assists researchers and analysts in better understanding their data.

Conducting a descriptive analysis entails several critical phases, which we will discuss below.

Step 1: Data Collection

Before conducting any analysis, you must first collect relevant data. This process involves identifying data sources, selecting appropriate data-collecting methods, and verifying that the data acquired accurately represents the population or topic of interest.

You can collect data through surveys, experiments, observations, existing databases, or other data collection methods .

Step 2: Data Preparation

Data preparation is crucial for ensuring the dataset is clean, consistent, and ready for analysis. This step covers the following tasks:

• Data Cleaning: Handle missing values, exceptions, and errors in the dataset. Input missing values or develop appropriate statistical techniques for dealing with them.
• Data Transformation: Convert data into an appropriate format. Examples of this are changing data types, encoding categorical variables, or scaling numerical variables.
• Data Reduction: For large datasets, try reducing their size by sampling or aggregation to make the analysis more manageable.

Step 3: Apply Methods

In this step, you will analyze and describe the data using a variety of methodologies and procedures. The following are some common descriptive analysis methods:

• Frequency Distribution Analysis: Create frequency tables or bar charts to show the number or proportion of occurrences for each category for categorical variables.
• Measures of Central Tendency: Calculate numerical variables’ mean, median, and mode to determine the center or usual value.
• Measures of Dispersion: Calculate the range, variance, and standard deviation to examine the dispersion or variability of the data.
• Measures of Position: Identify the position of a single value or its response to others.

Identify which variables are important to your descriptive analysis and research questions. Various methods are used for numerical and categorical variables, so it is essential to distinguish between them.

• After the data set has been analyzed, researchers may interpret the findings in light of the goals. The analysis was successful if the conclusions were what was anticipated. Otherwise, they must search for weaknesses in their strategy and repeat these processes to get better outcomes.

Step 4: Summary Statistics and Visualization

Descriptive statistics refers to a set of methods for summarizing and describing the main characteristics of a dataset. Summarize the data through statistics and visualization. This step involves the following tasks:

• Summary Statistics: Summarize your findings clearly and concisely.
• Data Visualization: Use various charts and plots to visualize the data. Create histograms, box plots, scatter plots, or line charts for numerical data. Use bar charts, pie charts, or stacked bar charts for categorical data.

Best Research Tips to Complete Descriptive Analysis

Moreover, what researchers can do to complete descriptive analysis are:

• They must specify the purpose of the in-depth analysis , the goals, the direction they will take, the things they must overlook, and the format in which the data must be provided.
• They must gather data after identifying the goals. This is a critical phase since collecting incorrect data might lead them far from their objective.
• Cleaning up the data is the next stage. When working with massive data sets, data cleansing may become challenging. The working data set’s noise or irrelevant information might skew the findings. Researchers should clean the data following the specifications for reliable results.
• Different descriptive techniques are used once the data has been cleaned. In the form of in-depth descriptive summaries, the descriptive analysis highlights the fundamental characteristics of the data.
• When you’re presenting your analysis to non-technical stakeholders and teams, it might be challenging to communicate the findings. Data visualization helps to complete this task efficiently. To give the results, researchers might use a variety of data visualization approaches, such as charts, pie charts, graphs, and others.

Descriptive analysis is a crucial research approach, regardless of whether the researcher wants to discover causal relationships between variables, explain population patterns, or develop new metrics for basic phenomena. When used correctly, it may significantly contribute to various descriptive and causal research investigations.

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An Overview of Descriptive Analysis

• Ayush Singh Rawat
• Mar 31, 2021

Nowadays, Big Data and Data Science have become high volume keywords. They tend to become extensively researched and this makes this data to be processed and studied with scrutiny. One of the techniques to analyse this data is Descriptive Analysis.

This data needs to be analysed to provide great insights and influential trends that allows the next batch of content to be made in accordance to the general population’s liking or dis-liking.

Introduction

The conversion of raw data into a form that will make it easy to understand & interpret, ie., rearranging, ordering, and manipulating data to provide insightful information about the provided data.

Descriptive Analysis is the type of analysis of data that helps describe, show or summarize data points in a constructive way such that patterns might emerge that fulfill every condition of the data.

It is one of the most important steps for conducting statistical data analysis . It gives you a conclusion of the distribution of your data, helps you detect typos and outliers, and enables you to identify similarities among variables, thus making you ready for conducting further statistical analyses.   

Techniques for Descriptive Analysis

Data aggregation and data mining are two techniques used in descriptive analysis to churn out historical data. In Data aggregation, data is first collected and then sorted in order to make the datasets more manageable.

Descriptive techniques often include constructing tables of quantiles and means, methods of dispersion such as variance or standard deviation, and cross-tabulations or "crosstabs" that can be used to carry out many disparate hypotheses. These hypotheses often highlight differences among subgroups.

Measures like segregation, discrimination, and inequality are studied using specialised descriptive techniques. Discrimination is measured with the help of audit studies or decomposition methods. More segregation on the basis of type or inequality of outcomes need not be wholly good or bad in itself, but it is often considered a marker of unjust social processes; accurate measurement of the different steps across space and time is a prerequisite to understanding these processes.

A table of means by subgroup is used to show important differences across subgroups, which mostly results in inference and conclusions being made. When we notice a gap in earnings, for example, we naturally tend to extrapolate reasons for those patterns complying. 

But this also enters the province of measuring impacts which requires the use of different techniques. Often, random variation causes difference in means, and statistical inference is required to determine whether observed differences could happen merely due to chance.

A crosstab or two-way tabulation is supposed to show the proportions of components with unique values for each of two variables available, or cell proportions. For example, we might tabulate the proportion of the population that has a high school degree and also receives food or cash assistance, meaning a crosstab of education versus receipt of assistance is supposed to be made. 

Then we might also want to examine row proportions, or the fractions in each education group who receive food or cash assistance, perhaps seeing assistance levels dip extraordinarily at higher education levels.

Column proportions can also be examined, for the fraction of population with different levels of education, but this is the opposite from any causal effects. We might come across a surprisingly high number or proportion of recipients with a college education, but this might be a result of larger numbers of people being college graduates than people who have less than a high school degree.

(Must check: 4 Types of Data in Statistics )

Types of Descriptive Analysis

Descriptive analysis can be categorized into four types which are measures of frequency, central tendency, dispersion or variation, and position. These methods are optimal for a single variable at a time.

Different types of Descriptive Analysis

Measures of Frequency

In descriptive analysis, it’s essential to know how frequently a certain event or response is likely to occur. This is the prime purpose of measures of frequency to make like a count or percent. 

For example, consider a survey where 500 participants are asked about their favourite IPL team. A list of 500 responses would be difficult to consume and accommodate, but the data can be made much more accessible by measuring how many times a certain IPL team was selected.

Measures of Central Tendency

In descriptive analysis, it’s also important to find out the Central (or average) Tendency or response. Central tendency is measured with the use of three averages — mean, median, and mode. As an example, consider a survey in which the weight of 1,000 people is measured. In this case, the mean average would be an excellent descriptive metric to measure mid-values.

Measures of Dispersion

Sometimes, it is important to know how data is divided across a range. To elaborate this, consider the average weight in a sample of two people. If both individuals are 60 kilos, the average weight will be 60 kg. However, if one individual is 50 kg and the other is 70 kg, the average weight is still 60 kg. Measures of dispersion like range or standard deviation can be employed to measure this kind of distribution.

Measures of Position

Descriptive analysis also involves identifying the position of a single value or its response in relation to others. Measures like percentiles and quartiles become very useful in this area of expertise.

Apart from it, if you’ve collected data on multiple variables, you can use the Bivariate or Multivariate descriptive statistics to study whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two different variables to see if they seem to have a pattern and vary together. You can also test and compare the central tendency of the two variables before carrying out further types of statistical analysis .

Multivariate analysis is the same as bivariate analysis but it is carried out for more than two variables. Following 2 methods are for bivariate analysis.

Contingency table

In a contingency table, each cell represents the combination of the two variables. Naturally, an independent variable (e.g., gender) is listed along the vertical axis and a dependent one is tallied along the horizontal axis (e.g., activities). You need to read “across” the table to witness how the two variables i.e. independent and dependent variables relate to each other.

A table showing a tally of different gender with number of activities

Scatter plots

A scatter plot is a chart that enables you to see the relationship between two or three different variables. It’s a visual rendition of the strength of a relationship.

In a scatter plot, you are supposed to plot one variable along the x-axis and another one along the y-axis. Each data point is denoted by a point in the chart.

The scatter plot shows the hours of sleep needed per day by age, Source

(Recommend Blog: Introduction to Bayesian Statistics )

Advantages of Descriptive Analysis

High degree of objectivity and neutrality of the researchers are one of the main advantages of Descriptive Analysis. The reason why researchers need to be extra vigilant is because descriptive analysis shows different characteristics of the data extracted and if the data doesn’t match with the trends then it will lead to major dumping of data.

Descriptive analysis is considered to be more vast than other quantitative methods and provide a broader picture of an event or phenomenon. It can use any number of variables or even a single number of variables to conduct a descriptive research. 

This type of analysis is considered as a better method for collecting information that describes relationships as natural and exhibits the world as it exists. This reason makes this analysis very real and close to humanity as all the trends are made after research about the real-life behaviour of the data.

It is considered useful for identifying variables and new hypotheses which can be further analyzed through experimental and inferential studies. It is considered useful because the margin for error is very less as we are taking the trends straight from the data properties.

This type of study gives the researcher the flexibility to use both quantitative and qualitative data in order to discover the properties of the population.

For example, researchers can use both case study which is a qualitative analysis and correlation analysis to describe a phenomena in its own way. Using the case studies for describing people, events, institutions enables the researcher to understand the behavior and pattern of the concerned set to its maximum potential. 

In the case of surveys which consist of one of the main types of Descriptive Analysis, the researcher tends to gather data points from a relatively large number of samples unlike experimental studies that generally need smaller samples.

This is an out and out advantage of the survey method over other descriptive methods that it enables researchers to study larger groups of individuals with ease. If the surveys are properly administered, it gives a broader and neater description of the unit under research.

(Also check: Importance of Statistics for Data Science )

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While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

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Title : An Introduction on Descriptive Analysis; Its advantages and disadvantages

Research is a crucial tool for leading man towards achieving progress, findings new facts, new concepts and discovering truths which leads to better ways of doing things. In the other words, “research is a diligent search, studious inquiry, investigation, experiment or collection of information, interpretation of facts, revision of existing theories and laws aimed at discovery of new facts and findings” (Adams al.,2007,P.20). Research Begins when researchers discover real world problems and try to answer those problems with the required mechanisms, tools and methods. Therefore, research methods have gained acceptance in all branches of science and disciplines which seek to find the answer for research questions in scientific manner (Ibid). It is believed, if a research does not follow any methodology, it may produce false results. There are different types of research for different disciplines and each discipline is associated with the particular scientific tools. Social sciences are one of those branches of sciences that follow its own research methods, methodologies and tools. Research method in social sciences is a vast topic. This is due to the fact that Social sciences include a great number of disciplines namely; Political Science, International Relations, Sociology, Economics, Anthropology, Social Capital, Education, Management, History, Psychology and so forth. Within each discipline researchers apply different methods and methodologies. The most frequently used methods are laboratory experiments, comparative politics, inferential analysis, descriptive analysis, exploratory research, Analytical Research and Predictive Research. Despite differences in disciplines and methods used in research, most disciplines in social sciences share same features and use same language for interpretation and reporting of their results (Walliman, 2011). It also happens that researchers use different methodologies for the similar type of problem of a discipline, it is as a result of limiting factors such as; cost, time, availability of tools, literature, access to publications and a country’s own peculiarities and circumstances (Adams et al.,2007). Descriptive research is one of the most commonly used type of researches in social sciences. A descriptive research aims to describe a phenomena the ways it is, for example, describing social systems or relationships between events (Adams et al., 2007). This paper attempts to introduce descriptive analysis; its advantages, disadvantages an example of Descriptive Analysis and conclusion. The next section introduces Descriptive Analysis.

Related Papers

Jacob Otachi ORINA

The study sought to establish the influence of governance on corruption levels from the perspective of the Public Service in Kenya. One of the study objectives was to: assess the influence of institutional leadership on corruption levels in the Public Service. A review of literature was done anchored on Principal-Agent Theory. The study adopted both the correlational and descriptive research designs. A study population of 265 institutions (as on 2015) provided a target sample size of 157 institutions. The target respondents in the sampled institutions were public officers who had undergone training on the following disciplines: leadership, integrity, values and principles of the public service and management during the study period (2010-2015). These purposely selected respondents were subjected to questionnaire. To augment data from the questionnaires, 23 key informant interviews were conducted targeting senior officers in the public service, non-state actors and experts. Data collected was analyzed by descriptive and inferential statistics. The overall correlation analysis results showed that there was a significant but negative relationship between institutional leadership and corruption levels as supported by correlation coefficient of-.525. The regression analysis results showed the coefficient of determination R square is .291 and R is .540 at 0.05 level of significance. The coefficient of determination indicates that 29.1% of the variation on corruption level is influenced by institutional leadership. The findings

The study sought to establish the influence of governance on corruption levels in the Public Service in Kenya. One of the study objectives was to: assess the influence of stakeholder participation on corruption levels in the Public Service. A review of literature was done anchored on Stakeholder Theory. Further, the empirical review, critique of reviewed literature, a summary and the research gaps were presented. The study adopted both the correlational and descriptive research designs. A study population of 265 institutions (as at 2015) provided a target sample size of 157 institutions where 133 were positive. The target respondents (unit of observation) in the sampled institutions were public officers who had undergone training on the following disciplines: leadership, integrity, values and principles of the public service and management during the study period (2010-2015). These purposely selected respondents were subjected to questionnaire as a primary tool of data collection. To augment data from the questionnaires, 23 key informant interviews were conducted targeting senior officers in the public service, non-state actors and experts. Data collected was analyzed by descriptive and inferential statistics. Data was presented in form of pie charts, graphs, tables and equations. The overall correlation analysis results showed that there was a significant but negative relationship between stakeholder participation and corruption levels as supported by correlation coefficient of -.741. The regression analysis results showed the coefficient of determination R square is 0.548 and R is 0.720 at 0.05 significance level. The coefficient of determination indicates that 54.8% of the variation on corruption level is influenced by stakeholder participation. The findings from the study are to benefit the policy makers, public service, citizens of Kenya and other stakeholders. It also fills the knowledge gap owed to previous little research on the influence of stakeholder participation on corruption levels. The study recommended that the public service should be keen to design policies and implement programs targeted on addressing the specific stakeholder sub constructs (stakeholder voice, openness, and partnership) so as to address the run-away corruption in the public service.

Oirc Journals

Risk is a fact of life in procurement but in spite of this, majority of manufacturing companies give this topic much less attention than it deserves. However, little or no research has been published that specifically addresses the procurement risk and mitigation strategies within the manufacturing sector in Africa land more so in the I Kenyan I manufacturing I firms that is central to delivery of goods and services to its customers. The main purpose of the study was to assess the influence of risk reduction on procurement performance. The study was guided by risk compensation theory. Explanatory research design was adopted. The target population was employees from four manufacturing firms and a sample of 127 respondents were selected using Yamane’s formula from an accessible population of 187. Data was collected through structured questionnaires and was summarized, edited, coded, entered and analyzed using statistical package for social scientists (SPSS). Inferential statistics involved regression analysis. The result was as follows: Based on risk reduction strategy, the correlation result was 0.583 and β = 0.051 at P<0.05. The study concluded that risk reduction was statistically significant and had a positive influence on procurement performance. The study findings rejected the null hypothesis that there is no statistically significant influence of risk reduction strategy on procurement performance. The study recommended policy makers to embrace other risk reduction strategies tools like diversification, underwriting and hedges. The study suggests that a further study be done on specific risk reduction strategies suitable for the manufacturing sector and a further study be done that focuses on specific procurement risks affecting the manufacturing sector and their effect on procurement performance.

International Journal of Strategic Management and Procurement

Performance of microfinance institutions is indicated by contributions to social welfare, job creation, general economic empowerment and improvement of lives of the poor. Despite the interest in the sector and the subsidies that have flowed into some of the mission-oriented MFIs, it seems that most MFIs struggle with the challenge of remaining viable over the long-term. Sensing capabilities could offer a solution to this dilemma through providing a customer management system which incorporates all functional areas of the organization. Thus, the main purpose of the study was to determine effect of sensing capability on performance of micro finance institutions in Eldoret town. This study was guided by resource-based view theory. Explanatory research design was used in this study. The target population for this study comprised of 584 employees drawn from 14 MFIs within Eldoret town. Stratified and simple random sampling technique was used in this study to select a sample of 162 employees. Primary data was obtained from the respondents using questionnaire. This study used questionnaires and interview schedules to collect data from respondents. Quantitative data collected from questionnaires were analysed using descriptive statistical techniques which were the frequencies, mean, standard deviation. Qualitative data collected from interview schedules of senior managers were analysed thematically. The researcher also used inferential statistics of Pearson Product Moment Correlation to show the relationships that exist between the variables and multiple regressions and correlation analysis, the significance of each independent variable was tested at a confidence level of 95%. Analysed data was presented in form of tables, figures and percentages. From the study finding, sensing capability has a significant effect on performance of micro finance institutions in Eldoret town with a beta coefficient of 0.127 and significance of (p<0.05). The study concluded that sensing capabilities about environment is a coping capability mechanism that enables the organization to be competitive.

Danial Zemchal Media Development in Tigray

Danial Zemchal

This paper comprises an ongoing MA Thesis research project titled “Assessment of Media Development in Tigray”. The main focus of this investigation concentrates on measuring the media development based on the UNESCO’s Media Development Measures. The pillars of the assessment are the system of regulation and practice in relation to freedom of expression, transparency of media ownership and concentration, diversity and plurality of the media, media as a platform of public discourse, professional capacity building as well as capacity of media infrastructure including its inclusive access to the marginalized society. It also examines the relationship among the media development measures through statistical Measure, SPSS. The research project which spotlight in examining the media development context in Tigray began in October 2018 and lasts in July 2019. A combination of quantitative questionnaire survey, qualitative; in-depth personal interview and focus group discussion are employed. Professionals in media firms in Tigray, higher education journalism and communication schools, democratic institutions; human right office, ombudsman office, civic and civil societies, Tigray, Kunama and Irob ethnicity communities are subjects of the research. The research project is currently progressed the quantitative and qualitative data collection process and analysis and presentation will be followed.

Assessment of Media Development in Tigray

International Journal of Scientific and Technological Research

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Characteristics of Qualitative Descriptive Studies: A Systematic Review

MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing

Justine S. Sefcik

MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing

Christine Bradway

PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing

Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic review, we identified characteristics of methods and findings reported in research articles published in 2014 whose authors identified the work as QD. After searching and screening, data were extracted from the sample of 55 QD articles and examined to characterize research objectives, design justification, theoretical/philosophical frameworks, sampling and sample size, data collection and sources, data analysis, and presentation of findings. In this review, three primary findings were identified. First, despite inconsistencies, most articles included characteristics consistent with limited, available QD definitions and descriptions. Next, flexibility or variability of methods was common and desirable for obtaining rich data and achieving understanding of a phenomenon. Finally, justification for how a QD approach was chosen and why it would be an appropriate fit for a particular study was limited in the sample and, therefore, in need of increased attention. Based on these findings, recommendations include encouragement to researchers to provide as many details as possible regarding the methods of their QD study so that readers can determine whether the methods used were reasonable and effective in producing useful findings.

Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena ( Polit & Beck, 2009 , 2014 ). QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a poorly understood phenomenon. It is also the label of choice when a straight description of a phenomenon is desired or information is sought to develop and refine questionnaires or interventions ( Neergaard et al., 2009 ; Sullivan-Bolyai et al., 2005 ).

Despite many strengths and frequent citations of its use, limited discussions regarding QD are found in qualitative research textbooks and publications. To the best of our knowledge, only seven articles include specific guidance on how to design, implement, analyze, or report the results of a QD study ( Milne & Oberle, 2005 ; Neergaard, Olesen, Andersen, & Sondergaard, 2009 ; Sandelowski, 2000 , 2010 ; Sullivan-Bolyai, Bova, & Harper, 2005 ; Vaismoradi, Turunen, & Bondas, 2013 ; Willis, Sullivan-Bolyai, Knafl, & Zichi-Cohen, 2016 ). Furthermore, little is known about characteristics of QD as reported in journal-published, nursing-related, qualitative studies. Therefore, the purpose of this systematic review was to describe specific characteristics of methods and findings of studies reported in journal articles (published in 2014) self-labeled as QD. In this review, we did not have a goal to judge whether QD was done correctly but rather to report on the features of the methods and findings.

Features of QD

Several QD design features and techniques have been described in the literature. First, researchers generally draw from a naturalistic perspective and examine a phenomenon in its natural state ( Sandelowski, 2000 ). Second, QD has been described as less theoretical compared to other qualitative approaches ( Neergaard et al., 2009 ), facilitating flexibility in commitment to a theory or framework when designing and conducting a study ( Sandelowski, 2000 , 2010 ). For example, researchers may or may not decide to begin with a theory of the targeted phenomenon and do not need to stay committed to a theory or framework if their investigations take them down another path ( Sandelowski, 2010 ). Third, data collection strategies typically involve individual and/or focus group interviews with minimal to semi-structured interview guides ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fourth, researchers commonly employ purposeful sampling techniques such as maximum variation sampling which has been described as being useful for obtaining broad insights and rich information ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fifth, content analysis (and in many cases, supplemented by descriptive quantitative data to describe the study sample) is considered a primary strategy for data analysis ( Neergaard et al., 2009 ; Sandelowski, 2000 ). In some instances thematic analysis may also be used to analyze data; however, experts suggest care should be taken that this type of analysis is not confused with content analysis ( Vaismoradi et al., 2013 ). These data analysis approaches allow researchers to stay close to the data and as such, interpretation is of low-inference ( Neergaard et al., 2009 ), meaning that different researchers will agree more readily on the same findings even if they do not choose to present the findings in the same way ( Sandelowski, 2000 ). Finally, representation of study findings in published reports is expected to be straightforward, including comprehensive descriptive summaries and accurate details of the data collected, and presented in a way that makes sense to the reader ( Neergaard et al., 2009 ; Sandelowski, 2000 ).

It is also important to acknowledge that variations in methods or techniques may be appropriate across QD studies ( Sandelowski, 2010 ). For example, when consistent with the study goals, decisions may be made to use techniques from other qualitative traditions, such as employing a constant comparative analytic approach typically associated with grounded theory ( Sandelowski, 2000 ).

Search Strategy and Study Screening

The PubMed electronic database was searched for articles written in English and published from January 1, 2014 to December 31, 2014, using the terms, “qualitative descriptive study,” “qualitative descriptive design,” and “qualitative description,” combined with “nursing.” This specific publication year, “2014,” was chosen because it was the most recent full year at the time of beginning this systematic review. As we did not intend to identify trends in QD approaches over time, it seemed reasonable to focus on the nursing QD studies published in a certain year. The inclusion criterion for this review was data-based, nursing-related, research articles in which authors used the terms QD, qualitative descriptive study, or qualitative descriptive design in their titles or abstracts as well as in the main texts of the publication.

All articles yielded through an initial search in PubMed were exported into EndNote X7 ( Thomson Reuters, 2014 ), a reference management software, and duplicates were removed. Next, titles and abstracts were reviewed to determine if the publication met inclusion criteria; all articles meeting inclusion criteria were then read independently in full by two authors (HK and JS) to determine if the terms – QD or qualitative descriptive study/design – were clearly stated in the main texts. Any articles in which researchers did not specifically state these key terms in the main text were then excluded, even if the terms had been used in the study title or abstract. In one article, for example, although “qualitative descriptive study” was reported in the published abstract, the researchers reported a “qualitative exploratory design” in the main text of the article ( Sundqvist & Carlsson, 2014 ); therefore, this article was excluded from our review. Despite the possibility that there may be other QD studies published in 2014 that were not labeled as such, to facilitate our screening process we only included articles where the researchers clearly used our search terms for their approach. Finally, the two authors compared, discussed, and reconciled their lists of articles with a third author (CB).

Study Selection

Initially, although the year 2014 was specifically requested, 95 articles were identified (due to ahead of print/Epub) and exported into the EndNote program. Three duplicate publications were removed and the 20 articles with final publication dates of 2015 were also excluded. The remaining 72 articles were then screened by examining titles, abstracts, and full-texts. Based on our inclusion criteria, 15 (of 72) were then excluded because QD or QD design/study was not identified in the main text. We then re-examined the remaining 57 articles and excluded two additional articles that did not meet inclusion criteria (e.g., QD was only reported as an analytic approach in the data analysis section). The remaining 55 publications met inclusion criteria and comprised the sample for our systematic review (see Figure 1 ).

Flow Diagram of Study Selection

Of the 55 publications, 23 originated from North America (17 in the United States; 6 in Canada), 12 from Asia, 11 from Europe, 7 from Australia and New Zealand, and 2 from South America. Eleven studies were part of larger research projects and two of them were reported as part of larger mixed-methods studies. Four were described as a secondary analysis.

Quality Appraisal Process

Following the identification of the 55 publications, two authors (HK and JS) independently examined each article using the Critical Appraisal Skills Programme (CASP) qualitative checklist ( CASP, 2013 ). The CASP was chosen to determine the general adequacy (or rigor) of the qualitative studies included in this review as the CASP criteria are generic and intend to be applied to qualitative studies in general. In addition, the CASP was useful because we were able to examine the internal consistency between study aims and methods and between study aims and findings as well as the usefulness of findings ( CASP, 2013 ). The CASP consists of 10 main questions with several sub-questions to consider when making a decision about the main question ( CASP, 2013 ). The first two questions have reviewers examine the clarity of study aims and appropriateness of using qualitative research to achieve the aims. With the next eight questions, reviewers assess study design, sampling, data collection, and analysis as well as the clarity of the study’s results statement and the value of the research. We used the seven questions and 17 sub-questions related to methods and statement of findings to evaluate the articles. The results of this process are presented in Table 1 .

CASP Questions and Quality Appraisal Results (N = 55)

Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/

Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.

Process for Data Extraction and Analysis

To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .

Elements for Data Extraction

Data Extraction and Analysis Results

Note . NR = not reported

Quality Appraisal Results

Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).

Research Objectives

In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).

Design Justification

A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).

In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).

Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:

The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).

Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.

Theoretical or Philosophical Framework

In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.

Sampling and Sample Size

In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.

Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.

Data Collection and Data Sources

In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).

Data Analysis

The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.

Presentation of Findings

Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.

In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.

In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.

In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:

“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).

This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).

However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.

Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).

Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.

In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.

Limitations

Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.

Conclusions

A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.

Acknowledgments

This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.

Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.

Conflict of Interest Statement

The Authors declare that there is no conflict of interest.

Contributor Information

Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.

Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.

Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.

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• How to write a descriptive essay | Example & tips

How to Write a Descriptive Essay | Example & Tips

Published on July 30, 2020 by Jack Caulfield . Revised on August 14, 2023.

A descriptive essay gives a vivid, detailed description of something—generally a place or object, but possibly something more abstract like an emotion. This type of essay , like the narrative essay , is more creative than most academic writing .

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Table of contents

Descriptive essay topics, tips for writing descriptively, descriptive essay example, other interesting articles, frequently asked questions about descriptive essays.

When you are assigned a descriptive essay, you’ll normally be given a specific prompt or choice of prompts. They will often ask you to describe something from your own experience.

• Describe a place you love to spend time in.
• Describe an object that has sentimental value for you.

You might also be asked to describe something outside your own experience, in which case you’ll have to use your imagination.

• Describe the experience of a soldier in the trenches of World War I.
• Describe what it might be like to live on another planet.

Sometimes you’ll be asked to describe something more abstract, like an emotion.

If you’re not given a specific prompt, try to think of something you feel confident describing in detail. Think of objects and places you know well, that provoke specific feelings or sensations, and that you can describe in an interesting way.

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The key to writing an effective descriptive essay is to find ways of bringing your subject to life for the reader. You’re not limited to providing a literal description as you would be in more formal essay types.

Make use of figurative language, sensory details, and strong word choices to create a memorable description.

Use figurative language

Figurative language consists of devices like metaphor and simile that use words in non-literal ways to create a memorable effect. This is essential in a descriptive essay; it’s what gives your writing its creative edge and makes your description unique.

Take the following description of a park.

This tells us something about the place, but it’s a bit too literal and not likely to be memorable.

If we want to make the description more likely to stick in the reader’s mind, we can use some figurative language.

Here we have used a simile to compare the park to a face and the trees to facial hair. This is memorable because it’s not what the reader expects; it makes them look at the park from a different angle.

You don’t have to fill every sentence with figurative language, but using these devices in an original way at various points throughout your essay will keep the reader engaged and convey your unique perspective on your subject.

Use your senses

Another key aspect of descriptive writing is the use of sensory details. This means referring not only to what something looks like, but also to smell, sound, touch, and taste.

Obviously not all senses will apply to every subject, but it’s always a good idea to explore what’s interesting about your subject beyond just what it looks like.

Even when your subject is more abstract, you might find a way to incorporate the senses more metaphorically, as in this descriptive essay about fear.

Choose the right words

Writing descriptively involves choosing your words carefully. The use of effective adjectives is important, but so is your choice of adverbs , verbs , and even nouns.

It’s easy to end up using clichéd phrases—“cold as ice,” “free as a bird”—but try to reflect further and make more precise, original word choices. Clichés provide conventional ways of describing things, but they don’t tell the reader anything about your unique perspective on what you’re describing.

Try looking over your sentences to find places where a different word would convey your impression more precisely or vividly. Using a thesaurus can help you find alternative word choices.

• My cat runs across the garden quickly and jumps onto the fence to watch it from above.
• My cat crosses the garden nimbly and leaps onto the fence to survey it from above.

However, exercise care in your choices; don’t just look for the most impressive-looking synonym you can find for every word. Overuse of a thesaurus can result in ridiculous sentences like this one:

• My feline perambulates the allotment proficiently and capers atop the palisade to regard it from aloft.

An example of a short descriptive essay, written in response to the prompt “Describe a place you love to spend time in,” is shown below.

Hover over different parts of the text to see how a descriptive essay works.

On Sunday afternoons I like to spend my time in the garden behind my house. The garden is narrow but long, a corridor of green extending from the back of the house, and I sit on a lawn chair at the far end to read and relax. I am in my small peaceful paradise: the shade of the tree, the feel of the grass on my feet, the gentle activity of the fish in the pond beside me.

My cat crosses the garden nimbly and leaps onto the fence to survey it from above. From his perch he can watch over his little kingdom and keep an eye on the neighbours. He does this until the barking of next door’s dog scares him from his post and he bolts for the cat flap to govern from the safety of the kitchen.

With that, I am left alone with the fish, whose whole world is the pond by my feet. The fish explore the pond every day as if for the first time, prodding and inspecting every stone. I sometimes feel the same about sitting here in the garden; I know the place better than anyone, but whenever I return I still feel compelled to pay attention to all its details and novelties—a new bird perched in the tree, the growth of the grass, and the movement of the insects it shelters…

Sitting out in the garden, I feel serene. I feel at home. And yet I always feel there is more to discover. The bounds of my garden may be small, but there is a whole world contained within it, and it is one I will never get tired of inhabiting.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The key difference is that a narrative essay is designed to tell a complete story, while a descriptive essay is meant to convey an intense description of a particular place, object, or concept.

Narrative and descriptive essays both allow you to write more personally and creatively than other kinds of essays , and similar writing skills can apply to both.

If you’re not given a specific prompt for your descriptive essay , think about places and objects you know well, that you can think of interesting ways to describe, or that have strong personal significance for you.

The best kind of object for a descriptive essay is one specific enough that you can describe its particular features in detail—don’t choose something too vague or general.

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Descriptive analytics: importance, benefits, & example

Have you ever wondered how businesses make sense of the mountains of data they collect daily? The secret lies in the realm of data analytics, where one type of analytics – descriptive analytics – transforms raw data into invaluable insights.

In this article, we’ll explore the importance of descriptive analytics, examine its remarkable benefits, and showcase real-world examples of how various industries use it successfully. 

Prepare to unlock the mysterious world of descriptive analytics and explore how it could elevate your business. By the time you finish reading, you’ll be armed with the knowledge needed to harness your data and steer your organization toward greater success.

What is descriptive analytics?

• How descriptive analytics can help your business?

What are some data analysis and visualization techniques to try?

How do you apply descriptive analytics in action, how can you drive better business decisions with data.

Descriptive analytics is one of the foundational aspects of data analytics that transforms raw data into easily understood patterns, trends, and insights. It’s a prime example of data aggregation that uses business intelligence and data science. This analytics process focuses on giving decision-makers an overview of historical data and an understanding of how certain events or actions unfolded. 

Unlike predictive analytics or prescriptive analytics, descriptive analytics isn’t about predicting future outcomes or recommending a course of action. Instead, it gives you a clear snapshot of past data so you can understand the key factors that contributed to specific situations.

Now that we have a basic idea of what descriptive analytics is, let’s dive into its purpose. Imagine having a massive amount of data and trying to make the most of it – descriptive analytics works to present the data required in a more digestible format. 

Organizations can then spot important developments, challenges, and opportunities that can shape future strategies and improvements. They can also leverage these insights to monitor key performance indicators (KPIs) and assess how well certain initiatives are doing.

How descriptive analytics can help your business ?

Let’s go over how descriptive analytics can help your organization.

• Enhancing business performance : Descriptive analytics helps businesses identify data trends and patterns. For a simple example, let’s imagine a clothing store tracks past sales metrics to notice that jackets sell like hotcakes during the fall.

This insight offers the business an understanding of customer behavior, which ultimately helps it develop targeted marketing strategies, increase sales, and boost performance.

• Leveraging historical data: One of the most powerful things about descriptive analytics is its ability to give meaning to historical data. Businesses can use past data to gain insights into the root cause of what shaped their current situation. For example, take a company that uses AI , machine learning, and descriptive analytics to analyze historical sales data and customer demographics. 

This analysis can deliver tangible benefits for demand forecasting . By understanding patterns in previous sales, the company can better predict future product demands and adjust its inventory accordingly. This helps the company avoid excess stock, minimize waste, and improve its bottom line.

• Improving communication: Descriptive analytics can work wonders when it comes to packaging complex data into something easily digestible. Let’s say a team leader wants to share information on project progress with their team and stakeholders. 

Using descriptive analytics, they can convert that raw data into visually appealing charts or graphs. This helps ensure everyone gets the picture without swimming through a sea of numbers, making communication much more effective and enjoyable.

• Enabling data-driven decisions: Lastly, descriptive analytics empowers businesses to make well-informed decisions by providing solid data. Imagine a restaurant owner examining customer reviews to gauge a dish’s popularity. 

Descriptive analytics could highlight patterns and trends, such as a specific dish receiving rave reviews or another with less-than-stellar ratings. The owner can then decide to promote the popular dish or improve the one that’s not performing well. This data-driven approach enhances decision-making and increases the chances of achieving business goals.

Now, we’ll explore some data analysis and visualization techniques that are at your disposal thanks to descriptive analytics. These analytics tools help paint a full picture of our datasets while keeping them interesting and easy to understand. 

• Data mining : Data mining is essentially treasure hunting in the world of data analysis. As a descriptive analysis technique, it involves sifting through large data sets to identify patterns, trends, and correlations that tell an informative story behind a given situation. 

Data mining helps businesses make sense of their data by uncovering those valuable nuggets of information hidden beneath the surface.

• Charts and graphs: A picture is worth a thousand words, and that’s especially true when it comes to presenting data. Line graphs, pie charts, and bar charts all help communicate complex data in a simple, visual format. 

Charts and graphs make it easier for businesses to quickly identify trends or anomalies, so stakeholders can grasp the information and act accordingly, or get on board with new plans and proposals.

• Visualization tools: Tools like Tableau take data analysis and visualization to the next level. You can use intuitive drag-and-drop interfaces to create eye-catching visuals that really bring your data to life, even if you don’t have graphic design or coding skills. 

And the best part? These tools save you a ton of time and effort compared to wrestling with Excel. Advanced visualization capabilities let you explore data from multiple angles, identify hidden patterns, and tell a compelling narrative.

• Dashboards : The days of flipping through countless spreadsheets are behind us. In the realm of descriptive analytics, dashboards offer a one-stop shop for all your key metrics, attractively displayed in real-time. They consolidate and present important data in a way that’s engaging and easy to understand. 

Customizable dashboards tailored to specific roles or goals enable stakeholders to quickly gauge the performance of various business aspects. This accessibility supports faster decision-making and keeps everyone on the same page.

Let’s take the magic of descriptive analytics from theory to practice. We’ll explore a few real-world examples of descriptive analytics in action to help you get the hang of it. These use cases show the power of descriptive analytics and how it can help your business flourish.

Gaining customer insight

Businesses must understand customers’ preferences, habits, and behaviors to truly connect with them. Descriptive analytics lets you analyze data from sources like customer reviews, purchase history, feedback forms, and surveys. 

Identifying data trends and patterns equips you with valuable insights that enable you to tailor your products or services to customers’ needs and preferences.

Monitoring business performance

Keeping tabs on how your business is doing is essential. Descriptive analytics allows you to track various metrics and key performance indicators (KPIs), giving you a clear picture of your business’s health.  For instance, you can analyze sales trends to determine which products are most popular, or dig into website data to identify areas needing improvement. Knowledge is power, and these insights help you make data-driven decisions that can boost performance and help your business grow.

Improving marketing campaigns

Descriptive analytics can help you optimize your campaigns by analyzing data points, such as social media engagement, email open rates, or number of subscribers. By understanding what works well and what doesn’t, you can tweak your strategies and allocate resources more efficiently. 

For example, Australian-based swimming pool builder Narellan Pools experienced a decline in sales and knew they needed a targeted marketing strategy. So, the company compiled and analyzed five years of marketing data and used the insights to drive a 23% increase in sales in one year, spending only 70% of its media budget.

Supply chain management

Optimizing supply chain efficiency is a big win for businesses (think synchronized inventory control, reduced lead times, and seamless logistics). Descriptive analytics can help you achieve this by analyzing data related to supplier performance, inventory levels, and transportation.  These trends can help you identify bottlenecks or inefficiencies so you can take timely steps to improve your overall supply chain performance.

Descriptive analytics helps businesses better understand their customers, improves workflows, fine-tunes marketing campaigns, and has the power to totally transform decision-making for the better.

As we step further into the age of data-driven decision-making, businesses should make the most of descriptive analytics to stay competitive and agile amidst changing markets. Mastering these techniques can pave the way for smarter, more informed decisions, helping your business stay on top.

Enhance your digital strategy and analytical skills with IMD’s Digital Strategy, Analytics, & AI Course . You’ll improve your decision-making skills and be the change-maker your organization needs. Jump-start your journey and let data be the driving force behind your success!

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