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Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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> General Analytics

Different Types of Research Methods

Mallika Rangaiah
Dec 22, 2021
Updated on: Nov 21, 2023

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth.

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means.

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it?

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same. Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data.

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue.

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition), the basic types of Research Methods are the following :

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research.

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place.

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends.

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies.

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data.

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based.

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done.

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue.

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business.

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company.

The crux of Applied Research is to figure out the solution to a certain growing practical issue.

The 3 Types of Applied Research are mainly

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research.

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry.

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science.

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression.

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it.

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed.

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists.

Quantitative research methods

Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables.
Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone.
(Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting.
Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey.

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men.

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts.

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association.

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
Interviews: Personally asking people questions in one-on-one conversations.
Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same.
Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers.
Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts.

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments.

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective.

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available.

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment.

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research.

One time Research : The research is restricted to a single time period.

Longitudinal Research : The research is executed over multiple time periods.

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on.

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time.

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships.

Research can also be Exploratory or Formalized.

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results.

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing.

We can also classify Research as conclusion-oriented and decision-oriented.

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements.

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases.

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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Analytical Methods

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Analytical Methods welcomes early applications of new analytical and bioanalytical methods and technology demonstrating the potential for societal impact.

We require that methods and technology reported in the journal are sufficiently innovative, robust, accurate, and compared to other available methods for the intended application. Developments with interdisciplinary approaches are particularly welcome. Systems should be proven with suitably complex and analytically challenging samples.

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How do Analyst and Analytical Methods compare? From discovery to recovery – Analyst and Analytical Methods working together for the analytical community Analyst , 2011, 136 , 429 DOI: 10.1039/c0an90013c

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Please note that it may not always be possible for the author's first choice associate editor to be selected. In situations where this is not possible the editorial office will assign the most suitable alternative.

On submission to the journal, all manuscripts are assigned to an external (academic) Associate Editor. Each submission is assessed for quality, scope, and impact. Those that do not meet the criteria based on these factors are rejected without further peer review. Otherwise, the article is sent to at least two external reviewers with expertise in the article topic for confidential review. An editorial decision to reject or accept the article is made on these reports.

More reviewers may be consulted in cases of opposing reports or when more clarification is needed. Articles needing significant revisions may be sent for further peer review before acceptance. Authors may appeal a rejection via communication with the Associate Editor. Our processes and policies can provide full details of the initial assessment process.

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Descriptive vs. Analytical Research in Sociology: A Comparative Study

Table of Contents

When we delve into the world of research, particularly in fields like patterns of social relationships , social interaction, and culture.">sociology , we encounter a myriad of methods designed to uncover the layers of human society and behavior. Two of the most fundamental research methods are descriptive and analytical research . Each plays a crucial role in understanding our world, but they do so in distinctly different ways. So, what exactly are these methods, and how do they compare when applied in the realm of social studies? Let’s embark on a comparative journey to understand these methodologies better.

Understanding Descriptive Research

Descriptive research is akin to the meticulous work of an artist attempting to capture the intricate details of a landscape. It aims to accurately describe the characteristics of a particular population or phenomenon. By painting a picture of the ‘what’ aspect, this method helps researchers to understand the prevalence of certain attributes, behaviors, or issues within a group.

Key Features of Descriptive Research

Snapshot in time: It often involves studying a single point or period, providing a snapshot rather than a motion picture.
Surveys and observations : Common tools include surveys , observations, and case studies .
Quantitative data: It leans heavily on quantitative data to present findings in numerical format.
No hypothesis testing: Unlike other research types, it doesn’t typically involve hypothesis testing.

When to Use Descriptive Research

Establishing a baseline : When there’s a need to set a reference point for future studies or track changes over time.
Exploratory purposes: When little is known about a topic and there’s a need to gather initial information that could inform future research.
Policy-making: When organizations or government bodies need factual data to inform decisions and policies.

Exploring Analytical Research

On the flip side, analytical research steps beyond mere description to explore the ‘why’ and ‘how’. It’s like a detective piecing together clues to not just recount events, but to understand the relationships and causations behind them. Analytical researchers critically evaluate information to draw conclusions and generalizations that extend beyond the immediate data.

Key Characteristics of Analytical Research

Critical evaluation: It involves a deep analysis of the available information to form judgments.
Qualitative and quantitative data: Uses both numerical data and non-numerical data for a more comprehensive analysis.
Hypothesis-driven: This method often starts with a hypothesis that the research is designed to test.
Seeking patterns: Aims to identify patterns, relationships, and causations.

When to Opt for Analytical Research

Understanding complexities: When the research question is complex and requires understanding the interplay of various factors.
Building upon previous research: When expanding on existing knowledge or challenging prevailing theories.
Recommendations for action: When research is aimed at providing actionable insights or solutions to problems.

Comparing Descriptive and Analytical Research in Real-World Scenarios

Imagine a sociologist aiming to tackle a pressing social issue, such as the dynamics of homelessness in urban areas. Descriptive research would enable them to establish the scale and scope of homelessness, identifying key demographics and patterns. Analytical research, however, would take these findings and probe deeper into the causes, examining the social, economic, and political factors that contribute to the situation and what can be done to alleviate it.

Advantages and Limitations

Each research type has its own set of strengths and weaknesses. Descriptive research is powerful for mapping out the landscape but may fall short in explaining the underlying reasons for observed phenomena. Analytical research, with its depth, can provide those explanations, but it may be more time-consuming and complex to conduct.

Choosing the Right Approach

Deciding between descriptive and analytical research often comes down to the specific objectives of the study. It’s not uncommon for researchers to employ both methods within the same broader research project to maximize their understanding of a topic.

In conclusion, descriptive and analytical research are two sides of the same coin, offering different lenses through which we can view and interpret the intricacies of social phenomena. By understanding their distinctions and applications, researchers can better design studies that yield rich, actionable insights into the fabric of society.

What do you think? Could a blend of both descriptive and analytical research provide a more holistic understanding of social issues? Are there situations where one method is clearly preferable over the other?

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Research Methodologies & Methods

1 Logic of Inquiry in Social Research

A Science of Society
Comte’s Ideas on the Nature of Sociology
Observation in Social Sciences
Logical Understanding of Social Reality

2 Empirical Approach

Empirical Approach
Rules of Data Collection
Cultural Relativism
Problems Encountered in Data Collection
Difference between Common Sense and Science
What is Ethical?
What is Normal?
Understanding the Data Collected
Managing Diversities in Social Research
Problematising the Object of Study
Conclusion: Return to Good Old Empirical Approach

3 Diverse Logic of Theory Building

Concern with Theory in Sociology
Concepts: Basic Elements of Theories
Why Do We Need Theory?
Hypothesis Description and Experimentation
Controlled Experiment
Designing an Experiment
How to Test a Hypothesis
Sensitivity to Alternative Explanations
Rival Hypothesis Construction
The Use and Scope of Social Science Theory
Theory Building and Researcher’s Values

4 Theoretical Analysis

Premises of Evolutionary and Functional Theories
Critique of Evolutionary and Functional Theories
Turning away from Functionalism
What after Functionalism
Post-modernism
Trends other than Post-modernism

5 Issues of Epistemology

Some Major Concerns of Epistemology
Rationalism
Phenomenology: Bracketing Experience

6 Philosophy of Social Science

Foundations of Science
Science, Modernity, and Sociology
Rethinking Science
Crisis in Foundation

7 Positivism and its Critique

Heroic Science and Origin of Positivism
Early Positivism
Consolidation of Positivism
Critiques of Positivism

8 Hermeneutics

Methodological Disputes in the Social Sciences
Tracing the History of Hermeneutics
Hermeneutics and Sociology
Philosophical Hermeneutics
The Hermeneutics of Suspicion
Phenomenology and Hermeneutics

9 Comparative Method

Relationship with Common Sense; Interrogating Ideological Location
The Historical Context
Elements of the Comparative Approach

10 Feminist Approach

Features of the Feminist Method
Feminist Methods adopt the Reflexive Stance
Feminist Discourse in India

11 Participatory Method

Delineation of Key Features

12 Types of Research

Basic and Applied Research
Descriptive and Analytical Research
Empirical and Exploratory Research
Quantitative and Qualitative Research
Explanatory (Causal) and Longitudinal Research
Experimental and Evaluative Research
Participatory Action Research

13 Methods of Research

Evolutionary Method
Comparative Method
Historical Method
Personal Documents

14 Elements of Research Design

Structuring the Research Process

15 Sampling Methods and Estimation of Sample Size

Classification of Sampling Methods
Sample Size

16 Measures of Central Tendency

Relationship between Mean, Mode, and Median
Choosing a Measure of Central Tendency

17 Measures of Dispersion and Variability

The Variance
The Standard Deviation
Coefficient of Variation

18 Statistical Inference- Tests of Hypothesis

Statistical Inference
Tests of Significance

19 Correlation and Regression

Correlation
Method of Calculating Correlation of Ungrouped Data
Method Of Calculating Correlation Of Grouped Data

20 Survey Method

Rationale of Survey Research Method
History of Survey Research
Defining Survey Research
Sampling and Survey Techniques
Operationalising Survey Research Tools
Advantages and Weaknesses of Survey Research

21 Survey Design

Preliminary Considerations
Stages / Phases in Survey Research
Formulation of Research Question
Survey Research Designs
Sampling Design

22 Survey Instrumentation

Techniques/Instruments for Data Collection
Questionnaire Construction
Issues in Designing a Survey Instrument

23 Survey Execution and Data Analysis

Problems and Issues in Executing Survey Research
Data Analysis
Ethical Issues in Survey Research

24 Field Research – I

History of Field Research
Ethnography
Theme Selection
Gaining Entry in the Field
Key Informants
Participant Observation

25 Field Research – II

Interview its Types and Process
Feminist and Postmodernist Perspectives on Interviewing
Narrative Analysis
Interpretation
Case Study and its Types
Life Histories
Oral History
PRA and RRA Techniques

26 Reliability, Validity and Triangulation

Concepts of Reliability and Validity
Three Types of “Reliability”
Working Towards Reliability
Procedural Validity
Field Research as a Validity Check
Method Appropriate Criteria
Triangulation
Ethical Considerations in Qualitative Research

27 Qualitative Data Formatting and Processing

Qualitative Data Processing and Analysis
Description
Classification
Making Connections
Theoretical Coding
Qualitative Content Analysis

28 Writing up Qualitative Data

Problems of Writing Up
Grasp and Then Render
“Writing Down” and “Writing Up”
Write Early
Writing Styles
First Draft

29 Using Internet and Word Processor

What is Internet and How Does it Work?
Internet Services
Searching on the Web: Search Engines
Accessing and Using Online Information
Online Journals and Texts
Statistical Reference Sites
Data Sources
Uses of E-mail Services in Research

30 Using SPSS for Data Analysis Contents

Introduction
Starting and Exiting SPSS
Creating a Data File
Univariate Analysis
Bivariate Analysis

31 Using SPSS in Report Writing

Why to Use SPSS
Working with SPSS Output
Copying SPSS Output to MS Word Document

32 Tabulation and Graphic Presentation- Case Studies

Structure for Presentation of Research Findings
Data Presentation: Editing, Coding, and Transcribing
Case Studies
Qualitative Data Analysis and Presentation through Software
Types of ICT used for Research

33 Guidelines to Research Project Assignment

Overview of Research Methodologies and Methods (MSO 002)
Research Project Objectives
Preparation for Research Project
Stages of the Research Project
Supervision During the Research Project
Submission of Research Project
Methodology for Evaluating Research Project

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AOAC (2000) Official Methods of Analysis. 17th Edition, The Association of Official Analytical Chemists, Gaithersburg, MD, USA. Methods 925.10, 65.17, 974.24, 992.16.

has been cited by the following article:

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KEYWORDS: Maize Grain Size , Nixtamalization , Starch , Rheological Properties

JOURNAL NAME: Agricultural Sciences , Vol.7 No.2 , February 29, 2016

ABSTRACT: Usually, the maize cob is formed by grains of medium size. However, the extremes have larger or smaller size grains. The objective of this study was to investigate the influence of grain size from the same hybrid on the physicochemical properties of isolated starch, crude maize flours and nixtamalized maize flours. Two hybrids, one from CIMMyT-Mexico called IMIC-254 and one commercial sample from Monsanto (Puma) were studied. The isolated starch granules from small, medium, and large grains exhibit the same size and distribution. The grain size has influence in the determination of cooking and steeping times; small grains reach these parameters faster than medium and large ones. The hardness of the grain size for both hybrids does not showed statistical differences between them. The starch from small, medium and large grains is mainly composed of amylopectin; this result is confirmed by X-ray diffraction and Megazine analysis. The apparent viscosity of the isolated starches of small grains showed statistically significant higher peak values. According to these results, it is possible to use small, medium, and large grains to obtain products with the same physicochemical properties, by adjusting the cooking and steeping times and Ca2+ content.

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New analytical tool can improve understanding of heritable human traits and diseases

by University of Oslo

$Replication analysis for schizophrenia. Replication rate is computed for GSA-MiXeR and MAGMA analyses using twofold cross-validation, after applying both methods to two independent and equally sized GWAS subsamples. After ranking gene sets according to fold enrichment of heritability (GSA-MiXeR) or enrichment P value (MAGMA) obtained in the discovery sample, the replication rate was then defined as the fraction of gene sets that remain within top N gene sets in the replication half of the dataset. Credit: Nature Genetics (2024). DOI: 10.1038/s41588-024-01771-1$

Researchers from the University of Oslo have developed an innovative method to improve our understanding of heritable human traits and diseases. The analytical tool, called GSA-MiXeR, is designed to make sense of genetic data by focusing on the role of individual genes, and how groups of genes contribute to the risk of developing a disease. With it, researchers now have a powerful new way to translate genetic research into practical insights that could lead to better treatments for a range of complex diseases.

The findings are published in the journal Nature Genetics .

More than 970 million people worldwide are living with a mental illness, according to WHO, and the global burden of these diseases is considerable. While researchers have been successful in identifying genetic factors associated with conditions such as schizophrenia through genome-wide association studies (GWAS), figuring out what these discoveries mean for our health is still a big challenge.

"GWAS, which are often produced by large international consortia, analyze the genomes of many individuals to find genetic variations associated with specific diseases. Our tool, called GSA-MiXeR, is designed to analyze the genetic data collected from these large-scale studies, aiming to identify how groups of genes contribute to the risk of developing a disease," says Oleksandr Frei, a researcher at the Center for Precision Psychiatry at the University of Oslo.

Complex polygenic traits, which are influenced by many genetic factors, have been particularly difficult to interpret. "GSA-MiXeR addresses this by providing a clearer picture of how different genes work together," he explains.

When applied to a variety of complex traits and diseases, including schizophrenia, GSA-MiXeR has been able to highlight specific gene groups that are more closely related to the disease than traditional methods have been able to.

One example is how GSA-MiXeR identified that genes involved in controlling calcium channels in our cells and those involved in dopamine signaling, play a significant role in schizophrenia. "These findings are not just important for understanding the disease—they also may point to potential targets for developing new treatments," Frei says.

Better understanding of complex traits and disorders can lead to precision medicine, where treatments are tailored to the genetic makeup of individual patients. "This approach can improve the effectiveness of treatments and reduce side effects. By translating genetic research into practical insights, GSA-MiXeR can contribute to more personalized and effective health care, ultimately leading to better health outcomes for patients," Frei says.

With GSA-MiXeR, scientists now have a powerful new way to translate genetic research into practical insights that could lead to better treatments for a range of complex diseases.

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Open access
Published: 03 June 2024

Assessing rates and predictors of cannabis-associated psychotic symptoms across observational, experimental and medical research

Tabea Schoeler ORCID: orcid.org/0000-0003-4846-2741 1 , 2 ,
Jessie R. Baldwin 2 , 3 ,
Ellen Martin 2 ,
Wikus Barkhuizen 2 &
Jean-Baptiste Pingault ORCID: orcid.org/0000-0003-2557-4716 2 , 3

Nature Mental Health ( 2024 ) Cite this article

3 Altmetric

Metrics details

Outcomes research
Risk factors

Cannabis, one of the most widely used psychoactive substances worldwide, can give rise to acute cannabis-associated psychotic symptoms (CAPS). While distinct study designs have been used to examine CAPS, an overarching synthesis of the existing findings has not yet been carried forward. To that end, we quantitatively pooled the evidence on rates and predictors of CAPS ( k = 162 studies, n = 210,283 cannabis-exposed individuals) as studied in (1) observational research, (2) experimental tetrahydrocannabinol (THC) studies, and (3) medicinal cannabis research. We found that rates of CAPS varied substantially across the study designs, given the high rates reported by observational and experimental research (19% and 21%, respectively) but not medicinal cannabis studies (2%). CAPS was predicted by THC administration (for example, single dose, Cohen’s d = 0.7), mental health liabilities (for example, bipolar disorder, d = 0.8), dopamine activity ( d = 0.4), younger age ( d = −0.2), and female gender ( d = −0.09). Neither candidate genes (for example, COMT , AKT1 ) nor other demographic variables (for example, education) predicted CAPS in meta-analytical models. The results reinforce the need to more closely monitor adverse cannabis-related outcomes in vulnerable individuals as these individuals may benefit most from harm-reduction efforts.

Do AKT1, COMT and FAAH influence reports of acute cannabis intoxication experiences in patients with first episode psychosis, controls and young adult cannabis users?

Rates and correlates of cannabis-associated psychotic symptoms in over 230,000 people who use cannabis

Measuring the diversity gap of cannabis clinical trial participants compared to people who report using cannabis

Cannabis, one of the most widely used psychoactive substances in the world, 1 is commonly used as a recreational substance and is increasingly taken for medicinal purposes. 2 , 3 As a recreational substance, cannabis use is particularly prevalent among young people 1 who seek its rewarding acute effects such as relaxation, euphoria, or sociability. 4 When used as a medicinal product, cannabis is typically prescribed to alleviate clinical symptoms in individuals with pre-existing health conditions (for example, epilepsy, multiple sclerosis, chronic pain, nausea. 5 )

Given the widespread use of cannabis, alongside the shifts toward legalization of cannabis for medicinal and recreational purposes, momentum is growing to scrutinize both the potential therapeutic and adverse effects of cannabis on health. From a public health perspective, of particular concern are the increasing rates of cannabis-associated emergency department presentations, 6 the rising levels of THC (tetrahydrocannabinol, the main psychoactive ingredient in cannabis) in street cannabis, 7 the adverse events associated with medicinal cannabis use, 8 and the long-term health hazards associated with cannabis use. 9 In this context, risk of psychosis as a major adverse health outcome related to cannabis use has been studied extensively, suggesting that early-onset and heavy cannabis use constitutes a contributory cause of psychosis. 10 , 11 , 12

More recent research has started to examine the more acute cannabis-associated psychotic symptoms (CAPS) to understand better how individual vulnerabilities and the pharmacological properties of cannabis elicit adverse reactions in individuals exposed to cannabis. Indeed, transient psychosis-like symptoms, including hallucinations or paranoia during cannabis intoxication, are well documented. 5 , 13 , 14 In more rare cases, recreational cannabis users experience severe forms of CAPS, 15 requiring emergency medical treatment as a result of acute CAPS. 16 In addition, acute psychosis following THC administration has been documented in medicinal cannabis trials and experimental studies, 17 , 18 , 19 suggesting that CAPS can also occur in more-controlled environments.

While numerous studies have provided evidence on CAPS in humans, no research has yet synthesized and compared the findings obtained from different study designs and populations. More specifically, three distinct study types have focused on CAPS: (1) observational studies assessing the subjective experiences of cannabis intoxication in recreational cannabis users, (2) experimental challenge studies administering THC in healthy volunteers, and (3) medicinal cannabis studies documenting adverse events when testing medicinal cannabis products in individuals with pre-existing health conditions. As such, the availability of these three distinct lines of evidence provides a unique research opportunity as their findings can be synthesized, be inspected for convergence, and ultimately, contribute to more evidence-based harm-reduction initiatives.

In this work, we therefore aim to perform a quantitative synthesis of all existing evidence examining CAPS to advance our understanding concerning the rates and predictors of CAPS: First, it is currently unknown how common CAPS are among individuals exposed to cannabis. While rates of CAPS are reported by numerous studies, estimates vary substantially (for example, from <1% (ref. 20 ) to 70% (ref. 21 )) and may differ depending on the assessed symptom profile (for example, cannabis-associated hallucinations versus cannabis-associated paranoia), the study design (for example, observational versus experimental research), and the population (for example, healthy volunteers versus medicinal cannabis users). Second, distinct study designs have scrutinized similar questions concerning the risks involved in CAPS. As such, comparisons of the results from one study design (for example, observational studies, assessing self-reported cannabis use in recreational users 22 , 23 ) with another study design (for example, experimental studies administering varying doses of THC 24 , 25 ) can be used to triangulate findings on a given risk factor of interest (for example, potency of cannabis). Finally, studies focusing on predictors of CAPS typically assess hypothesized risk factors in isolation. Pooling all existing evidence across different risk factors therefore provides a more complete picture of the relative magnitude of the individual risk factors involved in CAPS.

In summary, this work is set out to synthesize all of the available evidence on CAPS across three lines of research. In light of the increasingly liberal cannabis policies around the world, alongside the rising levels of THC in cannabis, such efforts are key to informing harm-reduction strategies and future research avenues for public health. Considering that individuals presenting with acute cannabis-induced psychosis are at high risk of converting to a psychotic disorder (for example, rates ranging between 18% (ref. 26 ) and 45% (ref. 27 )), a deeper understanding of factors predicting CAPS would contribute to our understanding concerning risk of long-term psychosis in the context of cannabis use.

Of 20,428 published studies identified by the systematic search, 162 were included in this work. The reasons for exclusion are detailed in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram (Fig. 1 ; see Supplementary Fig. 1 for a breakdown of the number of independent participants included in the different analytical models). The PRISMA reporting checklist is included in the Supplementary Results . At the full-text screening stage, the majority of studies were excluded because they did not report data on CAPS (83.88% of all excluded studies). Figure 2 displays the number of published studies included ( k ) and the number of (non-overlapping) study participants ( n ) per study design, highlighting that out of all participants included in this meta-analysis ( n = 201,283), most took part in observational research ( n = 174,300; 82.89%), followed by studies assessing medicinal cannabis products ( n = 33,502; 15.93%), experimental studies administering THC ( n = 2,009; 0.96%), and quasi-experimental studies ( n = 472; 0.22%). Screening of 10% of the studies at the full-text stage by an independent researcher (E.M.) did not identify missed studies.

Flow chart as adapted from the PRISMA flow chart ( http://www.prisma-statement.org/ ). Independent study participants are defined as the maximum number of participants available for an underlying study sample assessed in one or more of the included studies.

Number of included studies per year of publication and study design, including observational research assessing recreational cannabis users, experimental studies administering THC in healthy volunteers, and medicinal studies assessing adverse events in individuals taking cannabis products for medicinal use. Quasi-experimental research involved research testing the effects of THC administration in a naturalistic setting. 23 , 62 k , number of studies; n , number of (non-overlapping) study participants.

Rates of CAPS across the three study designs

A total of 99 studies published between 1971 and 2023 reported data on rates of CAPS and were included in the analysis, comprising 126,430 individuals from independent samples. Convergence of the data extracted by the two researchers (T.S. and W.B.) was high for the pooled rates on CAPS from observational studies (rate DIFF = −0.01%, where rate DIFF = rate TS – rate WB ), experimental studies (rate DIFF = 0%), and medicinal cannabis studies (rate DIFF = 0%). More specifically, we included data from 41 observational studies ( n = 92,888 cannabis users), 19 experimental studies administering THC ( n = 754), and 79 studies assessing efficacy and tolerability of medicinal cannabis products containing THC ( n = 32,821). In medicinal trials, the most common conditions treated with THC were pain ( k = 19 (23.75%)) and cancer ( k = 16 (20%)) (see Supplementary Table 1 for an overview). The age distribution of the included participants was similar in observational studies (mean age = 24.47 years, ranging from 16.6 to 34.34 years) and experimental studies (mean age = 25.1 years, ranging from 22.47 to 27.3 years). Individuals taking part in medicinal trials were substantially older (mean age = 48.16 years, ranging from 8 to 74.5 years).

As summarized in Fig. 3 and Supplementary Table 3 , substantial rates of CAPS were reported by observational studies (19.4%, 95% confidence interval (CI): 14.2%, 24.6%) and THC-challenge studies (21%, 95% CI: 11.3%, 30.7%), but not medicinal cannabis studies (1.5%, 95% CI: 1.1%, 1.9%). The pooled rates estimated for different symptom profiles of CAPS (CAPS – paranoia, CAPS – hallucinations, CAPS – delusions) are displayed in Supplementary Fig. 2 . All individual study estimates are listed in Supplementary Table 2 .

Pooled rates of CAPS across the three different study designs. Estimates on the y axis are the rates (in %, 95% confidence interval) obtained from models pooling together estimates on rates of CAPS (including psychosis-like symptoms, paranoia, hallucinations, and delusions) per study design.

Most models showed significant levels of heterogeneity (Supplementary Table 3 ), highlighting that rates of CAPS differed as a function of study-specific features. Risk of publication bias was indicated ( P Peters < 0.05) for one of the meta-analytical models combining all rates of CAPS (see funnel plots, Supplementary Fig. 2 ). Applying the trim-and-fill method slightly reduced the pooled rate of CAPS obtained from medicinal cannabis studies (rate unadjusted = 1.53%; rate adjusted = 1.18%). Finally, Fig. 4 summarizes rates of CAPS of a subset of studies where CAPS was defined as the occurrence of a full-blown cannabis-associated psychotic episode (as described in Table 1 ). When combined, the rate of CAPS (full episode) was 0.52% (0.42–0.62%) across the three study designs, highlighting that around one in 200 individuals experienced a severe episode of psychosis when exposed to cannabis/THC. Rates of CAPS (full episode) as reported by the individual studies showed high levels of consistency ( I 2 = 8%, P(I 2 ) = 0.45; Fig. 4 ).

Studies reporting rates of cannabis-associated psychosis (full episode). Depicted in violet are the individual study estimates (in %, 95% confidence interval) of studies reporting rates of (full-blown) cannabis-associated psychotic episodes. Included are studies using medicinal cannabis, observational, or experimental samples. The pooled meta-analyzed estimate is colored in blue. The I 2 statistic (scale of 0 to 100) indexes the level of heterogeneity across the estimates included in the meta-analysis.

Predictors of cannabis-associated psychotic symptoms

Assessing predictors of CAPS, we included 103 studies published between 1976 and 2023, corresponding to 80 independent samples ( n = 170,158 non-overlapping individuals). In total, we extracted 381 Cohen’s d that were pooled in 44 separate meta-analytical models. A summary of all extracted study estimates is provided in Supplementary Table 4 . Comparing the P values of the individual Cohen’s d to the original P values as reported in the studies revealed a high level of concordance ( r = 0.96 P = 1.1 × 10 –79 ), indicating that the conversion of the raw study estimates to a common metric did not result in a substantial loss of information. Comparing the results obtained from the data extracted by two researchers (T.S. and W.B.) identified virtually no inconsistencies when inspecting estimates of Cohen’s d , as obtained for severity of cannabis use on CAPS ( d DIFF = 0, where d DIFF = d TS –d WB ), gender ( d DIFF = 0), administration of (placebo controlled) medicinal cannabis ( d DIFF = 0.003), psychosis liability ( d DIFF = 0), and administration of a single dose of THC ( d DIFF = 0).

Figure 5 summarizes the results obtained from the meta-analytical models. We examined whether CAPS was predicted by the pharmacodynamic properties of cannabis, a person’s cannabis use history, demographic factors, mental health/personality traits, neurotransmitters, genetics, and use of other drugs: With respect to the pharmacodynamic properties of cannabis, the largest effect on CAPS severity was present for a single dose of THC ( d = 0.7, 95% CI: 0.52, 0.87) as administered in experimental studies, followed by a significant dose–response effect of THC on CAPS ( d = 0.42, 95% CI: 0.25, 0.59, that is, tested as moderation effects of THC dose in experimental studies). When tested in medicinal randomized controlled trials, cannabis products significantly increased symptoms of CAPS ( d = 0.14, 95% CI: 0.05, 0.23), albeit by a smaller magnitude. Protective effects were present for low THC/COOH levels ( d = −0.22, 95% CI: −0.39, −0.05, that is, the inactive metabolite of cannabis), but not for the THC/CBD (cannabidiol) ratio ( d = −0.19, 95% CI: −0.43, 0.05, P = 0.13).

Summary of pooled Cohen’s d , the corresponding 95% confidence intervals, and P values (two-sided, uncorrected for multiple testing). Positive estimates of Cohen’s d indicate increases in CAPS in response to the assessed predictor. Details regarding the classification and interpretation of each predictor are provided in the Supplementary Information . The reference list of all studies included in this figure is provided in Supplementary Table 4 . NS, neurotransmission.

Less clear were the findings with respect to the cannabis use history of the participants and its effect on CAPS. Here, neither young age of onset of cannabis use nor high-frequency use of cannabis or the preferred type of cannabis (strains high in THC, strains high in CBD) was associated with CAPS. The only demographic factors that significantly predicted CAPS were age ( d = −0.17, 95% CI: −0.292, −0.050) and gender (−0.09, 95% CI: −0.180, −0.001), indicating that younger and female cannabis users report higher levels of CAPS compared with older and male users. With respect to mental health and personality, the strongest predictors for CAPS were diagnosis of bipolar disorder ( d = 0.8, 95% CI: 0.54, 1.06)) and psychosis liability ( d = 0.49, 95% CI: 0.21, 0.77), followed by mood problems (anxiety d = 0.44, 95% CI: 0.03, 0.84; depression d = 0.37, 95% CI: 0.003, 0.740) and addiction liability ( d = 0.26, 95% CI: 0.14, 0.38). Summarizing the evidence from studies looking at neurotransmitter functioning showed that increased dopamine activity significantly predicted CAPS ( d = 0.4, 95% CI: 0.16, 0.64) (for example, reduced CAPS following administration of D2 blockers such as olanzapine 28 or haloperidol 29 ). By contrast, alterations in the opioid system did not reduce risk of CAPS. Similarly, none of the assessed candidate genes showed evidence of altering response to cannabis. Finally, out of 11 psychoactive substances with available data, only use histories of MDMA (3,4-methyl enedioxy methamphetamine) ( d = 0.2, 95% CI: 0.03, 0.36), crack ( d = 0.13, 95% CI: 0.03, 0.23), inhalants ( d = 0.12, 95% CI: 0.03, 0.22), and sedatives ( d = 0.12, 95% CI: 0.02, 0.22) linked to increases in CAPS.

Most of the meta-analytical models showed considerable levels of heterogeneity ( I 2 > 80%; Supplementary Table 5 ), notably when summarizing findings from observational studies (for example, severity of cannabis use: I 2 = 98%, age of onset of cannabis use: I 2 = 98%), highlighting that the individual effect estimates varied substantially across studies. By contrast, lower levels of heterogeneity were present when pooling evidence from experimental and medicinal cannabis studies (for example, effects of medicinal cannabis: I 2 = 18%; THC dose–response effects: I 2 = 37%). While risk of publication bias was indicated for four of the meta-analytical models (Egger’s test P < 0.05) (Supplementary Fig. 3 ), an inspection of trim-and-fill adjusted estimates did not alter the conclusions for (1) administration of a single dose of THC ( P Egger < 0.0001, d unadjusted = 0.7, d trim-and-fill = 0.49), (2) CBD administration ( P Egger = 0.0001, d unadjusted = −0.19, d trim-and-fill = −0.14, both P < 0.05), psychosis liability ( P Egger = 0.025, d unadjusted = 0.49, d trim-and-fill = 0.49), and (3) diagnosis of depression ( P Egger = 0.019, d unadjusted = 0.37, d trim-and-fill = 0.54). Outliers were identified for seven meta-analytical models (Supplementary Fig. 4 ). Removing outliers from the models did not substantially alter the conclusions drawn from the models, as indicated for age ( d = −0.18, d corr = −0.14, both P < 0.05); anxiety ( d = 0.61, d corr = 0.47, both P < 0.05), severity of cannabis use ( d = 0.19, d corr = 0.25, both P > 0.05), depression ( d = 0.41, d corr = 0.25, both P > 0.05), gender ( d = −0.09, d corr = −0.12, both P < 0.05), psychosis liability ( d = 0.49, d corr = 0.43, both P < 0.05), and administration of a single dose of THC ( d = 0.6, d corr = 0.56, both P < 0.05). Sensitivity checks assessing whether Cohen’s d changes as a function of within-subject correlation coefficient highlighted that the results were highly concordant (Supplementary Fig. 6 ). Minor deviations from the main analysis were present for the effects of a single dose of THC ( d r =0.3 = 0.64 versus d r =0.5 = 0.69 versus d r =0.7 = 0.77) and dose–response effects of THC ( d r =0.3 = 0.45 versus d r =0.5 = 0.42 versus d r =0.7 = 0.39), but this did not alter the interpretation of the findings.

Finally, we assessed consistency of findings for predictors examined in more than one of the different study designs (observational, experimental, and medicinal cannabis studies), as illustrated for four meta-analytical models in Fig. 6 (see Supplementary Fig. 7 for the complete set of results). Triangulating the results highlighted that consistency with respect to the direction of effects was particularly high for age ( d Experiments = −0.14 versus d Observational = −0.19 versus d Quasi-Experimental = −0.16) and gender ( d Experiments = −0.09 versus d Observational = −0.07 versus d Quasi-Experimental = −0.25) on CAPS. By contrast, little consistency across the different study designs was present with respect to cannabis use histories, notable age of onset of cannabis use ( d Observational = −0.3 versus d Quasi-Experimental = 0.24), and use of high-THC cannabis ( d Observational = 0.12 versus d Quasi-Experimental = −0.13).

Pooled estimates of Cohen’s d when estimated separately for each of the different study designs. The I 2 statistic (scale of 0 to 100) indexes the level of heterogeneity across the estimates included in the meta-analysis.

In this work, we examined rates and predictors of acute CAPS by synthesizing evidence from three distinct study designs: observational research, experimental studies administering THC, and studies testing medicinal cannabis products. Our results led to a number of key findings regarding the risk of CAPS in individuals exposed to cannabis. First, significant rates of CAPS were reported by all three study designs. This indicates that risk of acute psychosis-like symptoms exists after exposure to cannabis, irrespective of whether it is used recreationally, administered in controlled experiments, or prescribed as a medicinal product. Second, rates of CAPS vary across the different study designs, with substantially higher rates of CAPS in observational and experimental samples than in medicinal cannabis samples. Third, not every individual exposed to cannabis is equally at risk of CAPS as the interplay between individual differences and the pharmacological properties of the cannabis likely play an important role in modulating risk. In particular, risk appears most amplified in vulnerable individuals (for example, young age, pre-existing mental health problems) and increases with higher doses of THC (as shown in experimental studies).

Rates of cannabis-associated psychotic symptoms

Summarizing the existing evidence on rates of CAPS, we find that cannabis can acutely induce CAPS in a subset of cannabis-exposed individuals, irrespective of whether it is used recreationally, administered in controlled experiments, or prescribed as a medicinal product. Importantly, rates of CAPS varied substantially across the designs. More specifically, similar rates of CAPS were reported by observational and experimental evidence (around 19% and 21% in cannabis-exposed individuals, respectively), while considerably lower rates of CAPS were documented in medicinal cannabis samples (between 1% and 2%).

A number of factors likely contribute to the apparently different rates of CAPS across the three study designs. First, rates of CAPS are not directly comparable as different, design-specific measures were used: in observational/experimental research, CAPS is typically defined as the occurrence of transient cannabis-induced psychosis-like symptoms, whereas medicinal trials screen for CAPS as the occurrence of first-rank psychotic symptoms, often resulting in treatment discontinuation. 20 , 30 , 31 As such, transient CAPS may indeed occur commonly in cannabis-exposed individuals (as evident in the higher rates in observational/experimental research), while risk of severe CAPS requiring medical attention is less frequently reported (resulting in lower reported rates in medicinal cannabis samples). This converges with our meta-analytic results, showing that severe CAPS (full psychotic episode) may occur in about 1 in 200 (0.5%) cannabis users. Another key difference between medicinal trials and experimental/observational research lies in the demographic profile of participants recruited into the studies. For example, individuals taking part in medicinal trials were substantially older (mean age: 48 years) compared with subjects taking part in observational or experimental studies (mean age: 24 and 25 years, respectively). As such, older age may have buffered some of the adverse effects reported by adolescent individuals. Finally, cannabis products used in medicinal trials contain noticeable levels of CBD (for example, Sativex, with a THC/CBD ratio of approximately 1:1), a ratio different from that typically found in street cannabis (for example, >15% THC and <1% CBD 32 ) and in the experimental studies included in our meta-analyses (pure THC). As such, the use of medicinal cannabis (as opposed to street cannabis) may constitute a somewhat safer option. However, the potentially protective effects of CBD in this context require further investigation as we did not find a consistent effect of CBD co-administration on THC-induced psychosis-like symptoms. While earlier experimental studies included in our work were suggestive of protective effects of CBD, 33 , 34 , 35 two recent studies did not replicate these findings. 36 , 37

Interestingly, lower but significant rates of CAPS were also observed in placebo groups assessed as part of THC-challenge studies (% THC = 25% versus % placebo = 11%) and medicinal cannabis trials (% THC = 3% versus % placebo = 1%), highlighting that psychotic symptoms occur not only in the context of cannabis exposure. This is in line with the notion that cannabis use can increase risk of psychosis but appears to be neither a sufficient nor necessary cause for the emergence of psychotic symptoms. 38

Predictors of CAPS

Summarizing evidence on predictors of CAPS, we found that individual vulnerabilities and the pharmacological properties of cannabis both appear to play an important role in modulating risk. Regarding the pharmacological properties of cannabis, evidence from experimental studies showed that the administration of THC increases risk of CAPS, both in a single-dose and dose-dependent manner. Given the nature of the experimental design, these effects are independent of potential confounders that bias estimates obtained from observational studies. More challenging to interpret are therefore findings on individual cannabis use histories (for example, frequency/severity of cannabis use, age of onset of use, preferred cannabis strain) as assessed in observational studies. Contrary to evidence linking high-frequency and early-onset cannabis use to long-term risk of psychosis, 39 none of these factors associated with CAPS in our study. This discrepancy may indicate that cumulative effects of THC exposure are expressed differently for long-term risk of psychosis and acute CAPS: while users accustomed to cannabis may show a more blunted acute response as a result of tolerance, they are nevertheless at a higher risk of developing the clinical manifestation of psychosis in the long run. 38

We also tested a number of meta-analytical models for predictors tapping into demographic and mental health dimensions. Interestingly, among the assessed demographic factors, only age and gender associated with CAPS, with younger and female individuals reporting increased levels of CAPS. Other factors often linked to mental health, such as education or socioeconomic status, were not related to CAPS. Concerning predictors indexing mental health, we found converging evidence showing that a predisposition to psychosis increased the risk of experiencing CAPS. In addition, individuals with other pre-existing mental health vulnerabilities (for example, bipolar disorder, depression, anxiety, addiction liability) also showed a higher risk of CAPS, indicating that risk may stem partly from a common vulnerability to mental health problems.

These findings align with findings from studies focusing on the biological correlates of CAPS, showing that increases in dopamine activity, a neurotransmitter implicated in the etiology of psychosis, 40 altered sensitivity to cannabis. By contrast, none of the a priori selected candidate genes (chosen mostly to index schizophrenia liability) modulated risk of CAPS. This meta-analytic finding is coherent with results from the largest available genome-wide association study on schizophrenia, 41 where none of the candidate genes reached genome-wide significance ( P < 5 × 10 −8 ) ( Supplementary Information ). Instead, as for any complex trait, genetic risk underlying CAPS is likely to be more polygenic in nature, possibly converging on pathways as yet to be identified. As such, genetic testing companies that screen for the aforementioned genetic variants to provide their customers with an individualized risk profile (such as the Cannabis Genetic Test offered by Lobo Genetics ( https://www.lobogene.com )) are unlikely to fully capture the genetic risk underlying CAPS. Similarly, genetic counseling programs targeting specifically AKT1 allele carriers in the context of cannabis use 42 may be only of limited use when trying to reduce cannabis-associated harms.

Implications for research on cannabis use and psychosis

This work has a number of implications for future research avenues. First, experimental studies administering THC constitute the most stringent available causal inference method when studying risk of CAPS. Future studies should therefore capitalize on experimental designs to advance our understanding of the acute pharmacological effects of cannabis, in terms of standard cannabis units, 43 dose–response risk profiles, 44 the interplay of different cannabinoids, 44 , 45 and building on recent work.

Despite the value of experimental studies in causal inference, observational studies are essential to identify predictors of CAPS that cannot be experimentally manipulated (for example, age, long-term/chronic exposure to cannabis) and to strengthen external validity. However, a particular challenge for inference from observational studies results from bias due to confounding and reverse causation. Triangulating and comparing findings across study designs can therefore help to identify potential sources of bias that are specific to the different study designs. 46 For example, we observed that, despite THC dosing being robustly associated with CAPS in experimental studies, we did not find an association between cannabis use patterns (for example, high-THC cannabis strain) in observational and quasi-observational studies. This apparent inconsistency may result from THC effects that are blunted by long-term, early-onset and heavy cannabis use. For other designs, reverse causation may bias the association between cannabis use patterns and CAPS: as individuals may reduce cannabis consumption as a result of adverse acute effects, 47 the interpretation of cross-sectional estimates concerning different cannabis exposure and risk of CAPS is particularly challenging. Future observational studies should therefore exploit more robust causal inference methods (for example, THC administration in naturalistic settings 48 or within-subject comparisons controlling for time-invariant confounds 49 ) to better approximate the experimental design. In particular, innovative designs that can provide a higher temporal resolution on cannabis exposures and related experiences (for example, experience sampling, 50 assessing daily reactivity to cannabis 51 ) are a valuable addition to the causal inference toolbox for cannabis research. Applying genetically informed causal inferences such as Mendelian randomization analyses 52 can further help to triangulate findings, which would be possible once genome-wide summary results for both different cannabis use patterns and CAPS become available.

With respect to medicinal trials, it is important to note that an assessment of CAPS has not been a primary research focus. Although psychotic events are recognized as a potential adverse reaction to medicinal cannabis, 53 data on CAPS are rarely reported by medicinal trials, considering that only about 20% of medicinal cannabis randomized controlled trials screen for psychosis as a potential adverse effects. 5 As such, trials should systematically monitor CAPS, in addition to longer-term follow-ups assessing the risk of psychosis as a result of medicinal cannabis use. In particular, the use of validated instruments designed to capture more-subtle changes in CAPS should be included in trials to more adequately assess adverse reactions associated with medicinal cannabis products.

Second, with respect to factors associated with risk of CAPS, we find that these are similar to factors associated with onset of psychosis, notably pre-existing mental health vulnerabilities, 54 dose–response effects of cannabis, 55 and young age. 12 The key question deserving further attention is therefore whether CAPS constitutes, per se, a risk maker for long-term psychosis. Preliminary evidence found that in individuals with recent-onset psychosis, 37% reported to have experienced their first psychotic symptoms during cannabis intoxication. 56 Future longitudinal evidence building on this is required to determine whether subclinical cannabis-associated psychotic symptoms can help to identify users at high risk of developing psychosis in the long run. Follow-up research should also examine longitudinal trajectories of adverse cannabis-induced experiences and the distress associated with these experiences, given research suggesting that high levels of distress/persistence may constitute a marker of clinical relevance of psychotic-like experiences. 57 While few studies have explored this question in the context of CAPS, there is, for example, evidence suggesting that the level of distress caused by acute adverse reactions to cannabis may depend on the specific symptom dimension. 58 Here the highest levels of distress resulted from cannabis-associated paranoia and anxiety, rather than cannabis-associated hallucinations or experiences tapping into physical sensations (for example, body humming, numbness). In addition, some evidence highlights the re-occurring nature of CAPS in cannabis-exposed individuals. 22 , 58 Further research focusing on individuals with persisting symptoms of CAPS may therefore help to advance our knowledge concerning individual vulnerabilities underlying the development of long-term psychosis in the context of cannabis use.

Importantly, our synthesizing analysis is not immune to the sources of bias that exist for the different study designs, and our findings should therefore be considered in light of the aforementioned limitations (for example, residual confounding or reverse causation in observational studies, limited external validity in experimental studies). Nevertheless, comparing findings across the different study designs allowed us to pin down areas of inconsistency, which existed mostly with regard to cannabis-related parameters (for example, age of onset, frequency of use) and CAPS. In addition, we observed large levels of heterogeneity among most meta-analysis models, highlighting that study-specific findings may vary as a result of different sample characteristics and study methodologies. Future studies aiming to further discern potential sources of variation such as study design features (for example, treatment length in medicinal trials, route of THC administration in experimental studies), statistical modeling (for example, the type of confounding factors considered in observational research), and sample demographics (for example, age of the participants, previous experience with cannabis) are therefore essential when studying CAPS.

Conclusions

Our results demonstrate that cannabis can induce acute psychotic symptoms in individuals using cannabis for recreational or medicinal purposes. Some individuals appear to be particularly sensitive to the adverse acute effects of cannabis, notably young individuals with pre-existing mental health problems and individuals exposed to high levels of THC. Future studies should therefore monitor more closely adverse cannabis-related outcomes in vulnerable individuals as these individuals may benefit most from harm-reduction efforts.

Systematic search

A systematic literature search was performed in three databases (MEDLINE, EMBASE, and PsycInfo) following the PRISMA guidelines. 59 The final search was conducted on 6 December 2023 using 26 search terms indexing cannabis/THC and 20 terms indexing psychosis-like outcomes or cannabis-intoxication experiences (see Supplementary Information for a complete list of search terms). Search terms were chosen on the basis of terminology used in studies assessing CAPS, including observational studies (self-reported cannabis-induced psychosis-like experiences), THC-challenge studies (testing change in psychosis-like symptoms following THC administration), and medicinal studies testing the efficacy and safety of medicinal cannabis products (adverse events related to medicinal cannabis). Before screening the identified studies for inclusion, we removed non-relevant article types (reviews, case reports, comments, guidelines, editorials, letters, newspaper articles, book chapters, dissertations, conference abstracts) and duplicates using the R package revtools 60 . A senior researcher experienced in meta-analyses on cannabis use (T.S.) then reviewed all titles and abstracts for their relevance before conducting full-text screening. To reduce the risk of wrongful inclusion at the full-text screening stage, 10% of the articles selected for full-text screening were cross-checked for eligibility by a second researcher (E.M.).

Data extraction

We included all study estimates that could be used to derive rates of CAPS (the proportion of cannabis-exposed individuals reporting CAPS) or effect sizes (Cohen’s d ) for factors predicting CAPS. CAPS was defined as the occurrence of hallucinations, paranoia, and/or delusions during cannabis intoxication. These symptom-level items have been identified as the most reliable self-report measures screening for psychosis when validated against clinical interview measures. 61 Table 1 provides examples of CAPS as measured across the three different study designs. In brief, from observational studies, we extracted data if CAPS was assessed in cannabis-exposed individuals on the basis of self-report measures screening for subjective experiences while under the influence of cannabis. From experimental studies administering THC, CAPS was measured as the degree of psychotic symptom change in response to THC, either estimated from a between-subject (placebo groups versus THC group) or within-subject (pre-THC versus post-THC assessment) comparison. We also included data from natural experiments (referred to as quasi-experimental studies hereafter), where psychosis-like experiences were monitored in recreational cannabis users before and after they consumed their own cannabis products. 23 , 62 Finally, with respect to trials testing the efficacy and/or safety of medicinal cannabis products containing THC, we extracted data on adverse events, including the occurrence of psychosis, hallucinations, delusions, and/or paranoia during treatment with medicinal cannabis products. Medicinal studies that tested the effects of cannabis products not containing THC (for example, CBD only, olorinab, lenabasum) were not included.

For 10% of the included studies, data on rates and predictors of CAPS were extracted by a second researcher (W.B.), and agreement between the two extracted datasets was assessed by comparing the pooled estimates on rates and predictors of CAPS. In addition, following recommendations for improved reproducibility and transparency in meta-analytical works, 63 we provide all extracted data, the corresponding analytical scripts, and transformation information in the study repository.

Statistical analysis

Rates of caps.

We extracted the raw estimates of rates of CAPS as reported by observational, experimental, and medicinal cannabis studies. Classification of CAPS differs across the three study designs. In observational studies, occurrence of CAPS is typically defined as the experience of psychotic-like symptoms while under the influence of cannabis. In experimental studies administering THC, CAPS is commonly defined as a clinically significant change in psychotic symptom severity (for example, ≥3 points increase in Positive and Negative Syndrome Scale positive scores following THC 33 ). Finally, in medicinal cannabis samples, a binary measure of CAPS indicates whether psychotic symptoms occurred as an adverse event throughout the treatment with medicinal cannabis products. We derived rates of CAPS ( R CAPS = X Count of CAPS / N Sample size ) and the corresponding confidence intervals using the function BinomCI and the Clopper–Pearson method as implemented in the R package DescTools. 64 To estimate the pooled proportions, we fitted random-effects models or multilevel random-effects models as implemented in the R package metafor. 65 Multilevel random-effects models were used whenever accounting for non-independent sampling errors was necessary (further described in the following). Risk of publication bias was assessed using Peters’ test 66 and funnel plots and, if indicated ( P Peters < 0.05), corrected using the trim-and-fill method ( Supplementary Methods ).

To derive the pooled effects of factors predicting CAPS, we converted study estimates to the standardized effect size Cohen’s d as a common metric. For studies reporting mean differences, two formulas were used for the conversion. First, for studies reporting mean differences from between-subject comparisons (independent samples), we used the following formula:

where M E and M C are the mean scores on a continuous scale (severity of CAPS), reported for individuals exposed ( M E ) and unexposed ( M C ) to a certain risk factor (for example, cannabis users with pre-existing mental health problems versus cannabis users without pre-existing mental health problems). The formula used to derive the pooled standard deviations, SD P , and the variance of Cohen’s d are listed in the Supplementary Methods . Second, an extension of the preceding formula was used to derive Cohen’s d from within-subject comparisons, comparing time-point one ( M T1 ) with time-point two ( M T2 ).The formula takes into account the dependency between the two groups: 67

where r indexes the correlation between the pairs of observations, such as the correlation between the pre- and post-THC condition in the same set of individuals for a particular outcome measure. The correlation coefficient was set to be r = 0.5 for all studies included in the meta-analysis, on the basis of previous research. 13 We also assessed whether varying within-person correlation coefficients altered the interpretation of the results by re-estimating the pooled Cohen’s d for predictors of CAPS for two additional coefficients ( r = 0.3 and r = 0.7). The results were then compared with the findings obtained from the main analysis ( r = 0.5).

From experimental studies reporting multiple time points of psychosis-like experiences following THC administration (for example, refs. 68 , 69 , 70 , 71 , 72 ), we selected the most immediate time point following THC administration. Of note, whenever studies reported test statistics instead of means (for example, t -test or F -test statistics), the preceding formula was amended to accommodate these statistics. In addition, to allow for the inclusion of studies reporting metrics other than mean comparisons (for example, regression coefficients, correlations coefficients), we converted the results to Cohen’s d using existing formulas. All formulas used in this study are provided in the Supplementary Information . Whenever studies reported non-significant results without providing sufficient data to estimate Cohen’s d ( for example, results reported only as P > 0.05 ) , we used a conservative estimate of P = 1 and the corresponding sample size as the input to derive Cohen’s d . Finally, if studies reported estimates in figures only, we used WebPlotDigitizer ( https://automeris.io/WebPlotDigitizer ) to extract the data. Since the conversion of estimates from one metric to another may result in loss of precision, we also extracted the original P -value estimates (whenever reported as numerical values) and assessed the level of concordance with the P values corresponding to the estimated Cohen’s d .

Next, a series of meta-analytical models were fitted, each pooling estimates of Cohen’s d that belonged to the same class of predictors (for example, estimates indexing the effect of dopaminergic function on CAPS; estimates indexing the effect of age on CAPS). A detailed description of the classification of the included predictors is provided in the Supplementary Methods . Cohen’s d estimates were pooled if at least two estimates were available for one predictor class, using one of the following models:

Aggregation models (pooling effect sizes coming from the same underlying sample)

Random-effects models (pooling effect sizes coming from independent samples)

Multilevel random-effects models (pooling effect sizes coming from both independent and non-independent samples)

Predictors that could not meaningfully be grouped were not included in meta-analytical models but are, for completeness, reported as individual study estimates in the Supplementary Information . Levels of heterogeneity for each meta-analytical model were explored using the I 2 statistic, 73 indexing the contribution of study heterogeneity to the total variance. Here, I 2 > 30% represents moderate heterogeneity and I 2 > 50% represents substantial heterogeneity. Risk of publication bias was assessed visually using funnel plots alongside the application of Egger’s test to test for funnel-plot asymmetry. This test was performed for meta-analytical models containing at least six effect estimates. 74 The trim-and-fill 75 method was used whenever risk of publication bias was indicated ( P Egger < 0.05). To assess whether outliers distorted the conclusions of the meta-analytical models, we applied leave-one-out and outlier analysis 76 as implemented in the R package dmetar, 77 where a pooled estimate was re-calculated after omitting studies that deviated from the pooled estimate. Further details on all applied sensitivity analyses are provided in the Supplementary Methods .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data are publicly available via GitHub at github.com/TabeaSchoeler/TS2023_MetaCAPS .

Code availability

All analytical code used to analyze, summarize, and present the data is accessible via GitHub at github.com/TabeaSchoeler/TS2023_MetaCAPS .

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Acknowledgments

This research was funded in whole, or in part, by the Wellcome Trust (grant nos. 218641/Z/19/Z (to T.S.) and 215917/Z/19/Z (to J.R.B.)). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. J.-B.P. is funded by the Medical Research Foundation 2018 Emerging Leaders First Prize in Adolescent Mental Health (MRF-160-0002-ELP-PINGA (to J.-B.P.)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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T.S., J.R.B., and J.-B.P. conceived and designed the study. T.S., E.M., and W.B. acquired the data. T.S. analyzed the data and drafted the paper. All authors (T.S., J.R.B., E.M., W.B., and J.-B.P.) reviewed and approved the manuscript.

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Schoeler, T., Baldwin, J.R., Martin, E. et al. Assessing rates and predictors of cannabis-associated psychotic symptoms across observational, experimental and medical research. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00261-x

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Analytical Methods

Advances in colorimetric aptasensors for heavy metal ion detection utilizing nanomaterials: a comprehensive review.

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b Yunnan Dali Research Institute of Shanghai Jiao Tong University, Yunnan 671000, China

c School of Big Data, Yunnan Agricultural University, Kunming 650201, China

d Eryuan County Inspection and Testing Institute, Yunnan 671299, China

Heavy metal ion contamination poses significant environmental and health risks, necessitating rapid and efficient detection methods. In the last decade, colorimetric aptasensors have emerged as powerful tools for heavy metal ion detection, owing to their notable attributes such as high specificity, facile synthesis, adaptability to modifications, long-term stability, and heightened sensitivity. This comprehensive overview summarizes the key developments in this field over the past ten years. It discusses the principles, design strategies, and innovative techniques employed in colorimetric aptasensors using nanomaterials. Recent advancements in enhancing sensitivity, selectivity, and on-site applicability are highlighted. The review also presents application studies of successful heavy metal ion detection using colorimetric aptasensors, underlining their potential for environmental monitoring and health protection. Finally, future directions and challenges in the continued evolution of these aptasensors are outlined.

Graphical abstract: Advances in colorimetric aptasensors for heavy metal ion detection utilizing nanomaterials: a comprehensive review

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J. Zhu, D. Wang, H. Yu, H. Yin, L. Wang, G. Shen, X. Geng, L. Yang, Y. Fei and Y. Deng, Anal. Methods , 2023, 15 , 6320 DOI: 10.1039/D3AY01815F

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig. 1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

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Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig. 2 .

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Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig. 3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

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From data collection to data analysis

Attributions for icons: see Fig. Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig. 4 .

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Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

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