research and design tools

Design Research Methods

A Repository of Research Methods for Design

This page represents a growing list of application toolkits and other great resources for conducting design research, organized into General , Specific , and Thematic Tools.

General Tools

General tools provide guidance on an over all approach to user research, or an organized framework of many methods to inform your choice.

Austin Center for Design AC4D Design Library By AC4D. “Practical resources to support the process of design.”

Beginner’s Guide To Design Research By UX Booth. “In this Complete Beginner’s Guide, we’ll look at the many elements of design research, from interviews and observations, to usability testing and A/B testing. Readers will get a head start on how to use these design research techniques in their work, and improve experiences for all users.”

theDesignExchange A joint project led by UC Berkeley and M.I.T. working with the international community of design academics and practitioners. “TheDesignExchange provides a central repository of early design stage methods, engaging all stakeholders in the design community of practice, and integrating online learning with real case studies to demonstrate the methods.”

Design Kit IDEO.org. “Think of these Methods as a step-by-step guide to unleashing your creativity, putting the people you serve at the center of your design process to come up with new answers to difficult problems.”

Design Research Techniques “A simple visual guide to a range of techniques which you may want to further research, when they may be used and a little bit about them. They also have some great case studies with specific techniques for Discovery available here.”

Designing With People By the i-design project. “20 research methods that help designers engage with people during the design process. Some methods are widely used; others represent emerging practice. To help you find the right methods for your project, each method is explored and assessed here from a number of different angles.”

d.school Mixtapes Three “mixtapes” of methods to jumpstart your work: Understand – Experiment – Ideate.

Gamestorming “A toolkit of co-creation tools for innovators, rule-breakers and changemakers.”

IBM Design Research Resources Toolkit “New methods and models created by IBMers, for IBMers.”

IDEO Method Cards “ IDEO Method Cards  are a tool to showcase methods we use to inspire great design and keep people at the center of our design process. Each of the 51 cards describes one method and includes a brief story about how and when to use it.” (Also available as a smartphone app)

LifeHack “Top 10 Design Research Resources”

MakeTools Method Cards By Liz Sanders, 29 Method Cards for Generative Design

Research Toolbox Chart  |   Booklet By Daedalus + Thoughtform. “Twenty-three research methods to discover what your users really want.” Presented in a convenient chart with short definitions of methods, or a compelling illustrated booklet.

Usability.gov “Usability.gov is the leading resource for user experience (UX) best practices and guidelines, serving practitioners and students in the government and private sectors.”

Usability Body of Knowledge – Methods “This section of the Usability BoK presents descriptions of methods, including procedures, resources needed, outcomes, appropriate uses, benefits, and costs. These descriptions form the core of a knowledge base that defines our field. They also help communicate usability methods to clients, project managers, and team members. Usability practitioners will also benefit from cross-referencing of related methods and pointers to outside resources for more details.”

UX Research & Strategy By DesignLab.”Want to make products people love? Start with a deep understanding of your customers. Learn the who, what, why, when and where of customer research to help you create amazing user experiences.”

UX Research Cheat Sheet Nielsen Norman Group. “User research can be done at any point in the design cycle. This list of methods and activities can help you decide which to use when.”

Specific Methods Tools

Specific tools correspond to methods for particular applications, for example, diary studies, cultural probes, card sorting or user testing.

Dscout For mobile diary studies. “dscout’s remote research platform uses a mobile app and +100K eager participants to efficiently capture in-the-moment video and make insights easy to synthesize and share.”

EthOS Ethnographic Observation System “Today we are the go-to platform for anyone wishing to carry out remote qualitative, quantitative and ethnographic research projects anywhere in the world.”

Lego Serious Play Although marketed toward business performance, Lego can be successfully used as a participatory co-design make-tool for inspired creativity.

Optimal Workshop A user research platform with a suite of online usability tools, including “Optimal Sort” card-sorting software.

Probetools Interaction Research Studio. “Our goal is to build on contemporary making and hacking trends to update Probes and make them widely accessible to researchers of any background.”

PremoTool By SusaGroup. “A unique, scientifically validated tool to instantly get insight in consumer emotions! People can report their emotions with the use of expressive cartoon animations instead of relying on the use of words.”

UsabilityHub A set of five online usability tools, geared toward fast testing. “Remote user testing to help you make confident design decisions”

MakeTools: Papers on Participatory Design & Generative Research

By Liz Sanders et al.

Thematic Tools

Thematic tools are designed for research in broad topic areas, such as emotion, behavior change, healthcare, or service design.

Design and Emotion “Since 2005, the Design & Emotion Society has been collecting tools and methods that support the application of design for emotion.”

Design with Intent By Dan Lockton, PhD. “Aims to give practitioners a more nuanced approach to design and behaviour, working with people, people’s understanding, and the complexities of everyday human experience. It’s a collection of design patterns—and a design and research approach—for exploring the interactions between design and people’s behaviour, across products, services and environments, both digital and physical.”

MethodKit for Public Health “A healthy population is the backbone of a sustainable society. We have created a tool that can help you organize, plan and shape the future landscape of patient care, health and wellbeing. This kit is aimed at both professionals and enthusiasts who want to understand more and create new systems for the future of public health.”

Service Design Tools “An open collection of communication tools used in design processes that deal with complex systems.”

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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.

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Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
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• Data Analysis
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Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is design research methodology and why is it important?

What is design research.

Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It’s a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions.

Not to be confused with user experience research – focused on the usability of primarily digital products and experiences – design research is a broader discipline that informs the entire design process across various design fields. Beyond focusing solely on researching with users, design research can also explore aesthetics, cultural trends, historical context and more.

Design research has become more important in business, as brands place greater emphasis on building high-quality customer experiences as a point of differentiation.

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Design research vs. market research

The two may seem like the same thing at face value, but really they use different methods, serve different purposes and produce different insights.

Design research focuses on understanding user needs, behaviors and experiences to inform and improve product or service design.  Market research , on the other hand, is more concerned with the broader market dynamics, identifying opportunities, and maximizing sales and profitability.

Both are essential for the success of a product or service, but cater to different aspects of its lifecycle.

Design research in action: A mini mock case study

A popular furniture brand, known for its sleek and simple designs, faced an unexpected challenge: dropping sales in some overseas markets. To address this, they turned to design research – using quantitative and qualitative methods – to build a holistic view of the issue.

Company researchers visited homes in these areas to interview members of their target audience and understand local living spaces and preferences. Through these visits, they realized that while the local customers appreciated quality, their choices in furniture were heavily influenced by traditions and regional aesthetics, which the company's portfolio wasn’t addressing.

To further their understanding, the company rolled out surveys, asking people about their favorite materials, colors and furniture functionalities. They discovered a consistent desire for versatile furniture pieces that could serve multiple purposes. Additionally, the preference leaned towards certain regional colors and patterns that echoed local culture.

Armed with these insights, the company took to the drawing board. They worked on combining their minimalist style with the elements people in those markets valued. The result was a refreshed furniture line that seamlessly blended the brand's signature simplicity with local tastes. As this new line hit the market, it resonated deeply with customers in the markets, leading to a notable recovery in sales and even attracting new buyers.

When to use design research

Like most forms of research, design research should be used whenever there are gaps in your understanding of your audience’s needs, behaviors or preferences. It’s most valuable when used throughout the product development and design process.

When differing opinions within a team can derail a design process, design research provides concrete data and evidence-based insights, preventing decisions based on assumptions.

Design research brings value to any product development and design process, but it’s especially important in larger, resource intensive projects to minimize risk and create better outcomes for all.

The benefits of design research

Design research may be perceived as time-consuming, but in reality it’s often a time – and money – saver that can. easily prove to be the difference between strong product-market fit and a product with no real audience.

Deeper customer knowledge

Understanding your audience on a granular level is paramount – without tapping into the nuances of their desires, preferences and pain points, you run the risk of misalignment.

Design research dives deep into these intricacies, ensuring that products and services don't just meet surface level demands. Instead, they can resonate and foster a bond between the user and the brand, building foundations for lasting loyalty .

Efficiency and cost savings

More often than not, designing products or services based on assumptions or gut feelings leads to costly revisions, underwhelming market reception and wasted resources.

Design research offers a safeguard against these pitfalls by grounding decisions in real, tangible insights directly from the target market – streamlining the development process and ensuring that every dollar spent yields maximum value.

New opportunities

Design research often brings to light overlooked customer needs and emerging trends. The insights generated can shift the trajectory of product development, open doors to new and novel solutions, and carve out fresh market niches.

Sometimes it's not just about avoiding mistakes – it can be about illuminating new paths of innovation.

Enhanced competitive edge

In today’s world, one of the most powerful ways to stand out as a business is to be relentlessly user focused. By ensuring that products and services are continuously refined based on user feedback, businesses can maintain a step ahead of competitors.

Whether it’s addressing pain points competitors might overlook, or creating user experiences that are not just satisfactory but delightful, design research can be the foundations for a sharpened competitive edge.

Design research methods

The broad scope of design research means it demands a variety of research tools, with both numbers-driven and people-driven methods coming into play. There are many methods to choose from, so we’ve outlined those that are most common and can have the biggest impact.

This stage is about gathering initial insights to set a clear direction.

Literature review

Simply put, this research method involves investigating existing secondary research, like studies and articles, in your design area. It's a foundational method that helps you understand current knowledge and identify any gaps – think of it like surveying the landscape before navigating through it.

Field observations

By observing people's interactions in real-world settings, we gather genuine insights. Field observations are about connecting the dots between observed behaviors and your design's intended purpose. This method proves invaluable as it can reveal how design choices can impact everyday experiences.

Stakeholder interviews

Talking to those invested in the design's outcome, be it users or experts, is key. These discussions provide first-hand feedback that can clarify user expectations and illuminate the path towards a design that resonates with its audience.

This stage is about delving deeper and starting to shape your design concepts based on what you’ve already discovered.

Design review

This is a closer look at existing designs in the market or other related areas. Design reviews are very valuable because they can provide an understanding of current design trends and standards – helping you see where there's room for innovation or improvement.

Without a design review, you could be at risk of reinventing the wheel.

Persona building

This involves creating detailed profiles representing different groups in your target audience using real data and insights.

Personas help bring to life potential users, ensuring your designs address actual needs and scenarios. By having these "stand-in" users, you can make more informed design choices tailored to specific user experiences.

Putting your evolving design ideas to the test and gauging their effectiveness in the real world.

Usability testing

This is about seeing how real users interact with a design.

In usability testing you observe this process, note where they face difficulties and moments of satisfaction. It's a hands-on way to ensure that the design is intuitive and meets user needs.

Benchmark testing

Benchmark testing is about comparing your design's performance against set standards or competitor products.

Doing this gives a clearer idea of where your design stands in the broader context and highlights areas for improvement or differentiation. With these insights you can make informed decisions to either meet or exceed those benchmarks.

This final stage is about gathering feedback once your design is out in the world, ensuring it stays relevant and effective.

Feedback surveys

After users have interacted with the design for some time, use feedback surveys to gather their thoughts. The results of these surveys will help to ensure that you have your finger on the pulse of user sentiment – enabling iterative improvements.

Remember, simple questions can reveal a lot about what's working and where improvements might be needed.

Focus groups

These are structured, moderator-led discussions with a small group of users . The aim is for the conversation to dive deep into their experiences with the design and extract rich insights – not only capturing what users think but also why.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The 10 best UX research tools to use in 2023

In this guide, we introduce 9 of the best UX research tools on the market right now. We’ll also share some advice on how to choose the most suitable tools for your work.

The State of UX Hiring Report 2024

Learn how to start your UX career with hard facts and practical advice from those who have gone before you. In this report, we look at UX hiring trends in 2024 to help you break into the industry.

All good UX begins with user research—and all good user research relies on the right tools. 

But, with so many tools to choose from, where do you even start? 

Look no further. In this guide, we introduce 9 of the best UX research tools on the market right now. We’ll also share some advice on how to choose the most suitable tools for your work.

What is UX research and why does it matter?

• 9 of the best UX research tools available in 2023

How to choose the right tools for your UX research

Ready to become a user research pro? Let’s begin. 

[GET CERTIFIED IN USER RESEARCH]

Before we explore the best tools for the job, let’s recap on what exactly UX research is—and why it’s so important.

UX design is all about solving a real problem for real, human users. UX research helps you to identify the problem you need to solve, and to understand how best to solve it based on what you know about your users. 

Without user research, you’re basing your work on assumptions. This inevitably leads to a mismatch between the user experience and the people you’re designing it for—i.e. bad UX!

That’s why all good designers start their UX process with research. UX research involves:

• Conducting interviews, surveys, card sorting exercises and focus groups (to name a few!) with real or representative users to see what they expect from the user experience and what pain-points they currently encounter
• Analysing the data gathered to uncover key themes and user problems
• Defining the scope of the problems uncovered and determining what to prioritise
• Sharing your findings with key stakeholders
• Continually testing and iterating on your designs to optimise the user experience

You can learn more about what UX research is in this dedicated guide .

What’s the difference between qualitative and quantitative research?

As you consider what tools to use for your UX research, it’s important to distinguish between quantitative and qualitative research. 

Quantitative user research gathers objective, measurable data that can be quantified (i.e. counted). Some examples of quantitative data might be the number of clicks it takes a user to complete their desired task on a website, or the percentage of users who bounce in a given time frame.

Qualitative user research isn’t concretely measurable, but it can give you much deeper insights into how your users think, feel and behave. For example, if you conduct interviews to find out how your users feel about a particular product, that’s qualitative research. Likewise, if you observe a user trying to navigate an app and note down that they get really frustrated, that’s qualitative data. 

UX designers tend to conduct both qualitative and quantitative research for a broad and detailed picture of their users. 

What’s the difference between moderated and unmoderated user research?

Another distinction to be aware of is that between moderated and unmoderated research. 

Moderated UX research takes place with the user researcher present. If you’re interviewing a user live via video call, or observing them while they complete a certain task and asking follow-up questions, you’re conducting moderated UX research.

Unmoderated UX research takes place without your supervision. This includes things like surveys which the user answers in their own time, or usability tests where the user might record their screen while they interact with your website.

What are the best UX research tools?

Now we know about the different types of user research you might conduct, let’s explore some of the best UX research tools on the market right now. 

1. Optimal Workshop for card sorting, tree testing and first-click testing

Optimal Workshop isn’t just a user research tool—it’s an entire toolbox. You can use it to conduct both qualitative and quantitative user research, and to recruit participants.

Optimal Workshop allows you to see participant responses as they come in, and to view your data in the form of easy-to-understand visualisations—ideal for sharing your insights with others. 

You can use Optimal Workshop to conduct card sorting exercises, tree testing, first-click testing, and surveys. 

Optimal Workshop comprises 5 tools:

• OptimalSort , a card sorting tool that shows you how your users categorise information. This is useful when mapping out the information architecture of a website or app.
• Treejack , a tree testing tool that shows you how easily people can find information on your website or app—and where they get lost.
• Chalkmark for first-click testing. This enables you to test the usability of an existing design. You can upload screenshots, sketches or wireframes and test to see if users are able to navigate with ease.
• Questions for creating and sending out online surveys. You can attach wireframes or sketches for more specific feedback.
• Reframer for note-taking and documentation. This is useful for organising all your qualitative research insights in one place. Reframer is actually number 8 on our list, so more on that later!

Main features at a glance:

• 1 platform, 5 tools for card sorting, tree testing, first-click testing, surveys, and documenting qualitative research insights
• Participant recruitment service (available in 70+ languages)
• View participant responses as they come in
• Data visualisations accessible via the Optimal Workshop dashboard

How much does it cost?

Optimal Workshop offers a free plan with no requirement to upgrade. If you do want more functionality, paid options include:

• The Starter plan for small-scale research projects at $99/month (approx. €88).
• The Pro plan for unlimited studies at $166/month (approx. €150) for 1 user.
• The Team plan for unlimited studies at $153/month per user (approx. €140) for up to 3 users. 

2. Looppanel for user interviews and usability tests

Looppanel is an AI-powered research analysis & repository product that makes it 5x faster to discover and share user insights.

Looppanel acts like your research assistant: it records, transcribes, creates notes, and organizes your data for easy analysis.

Teams like PandaDoc, Huge Inc., Airtel, and others use Looppanel to streamline research analysis and build their insights repository.

Main Features at a glance

• Automatically generated notes for user interviews
• 90%+ accuracy transcription in 8 languages
• Integrations with Zoom, Google Meet, Teams to auto-record calls
• Time-stamped notes taken live during interviews
• Ability to tag and annotate on transcripts
• 1-click to create shareable video clips
• Analysis workspace to view project data by question or tag
• Search across projects

Looppanel offers a free 15-day trial. After that, you can choose from a range of paid plans:

• Starter (for small teams / solo researchers): An affordable starter plan for $30/month that includes 10 transcription hours / month
• Teams: For teams of 3+ researchers, this plan is priced at $350/month and comes with 30 transcription hours / month
• Business: For organizations with large teams or significant security requirements, the business plan costs $1,000/month for 120 transcription hours / month
• Custom: For enterprise teams of larger sizes

3. Lookback for user interviews 

Lookback is a video research platform for conducting both moderated and unmoderated user interviews and usability tests. 

The collaborative dashboard allows you to sync all your research, tag your teammates, and create highlight reels of all the most useful insights. You can set up virtual observation rooms, record users’ screens as they navigate your app or website, and transcribe your user interviews. 

• Moderated and unmoderated video interviews and user testing sessions
• Timestamped notes captured live during sessions
• Virtual observation rooms: Invite stakeholders to observe user research sessions and chat with each other in a separate virtual room
• Screen capturing: Watch and record participant touches on mobile screens during interactions
• Create highlight videos and compile them into highlight reels
• Collaborative dashboard 

Lookback offers a free 14-day trial. After that, there are a range of paid plans to choose from:

• Freelance: An affordable solo plan for $17/month (approx. €15). Includes 10 sessions/year.
• Team: $99/month (approx. €90) for 100 sessions/year. 
• Insights Hub: $229/month (approx. €205) for 300 sessions/year. 

4. Typeform for surveys

Surveys are a UX research staple, offering a quick, easy and inexpensive way to gather user insights. When sending out surveys for UX research, you’ll usually ask questions about the respondents’ attitudes and preferences in relation to the product or service you’re designing. 

Typeform is one of the most popular survey tools among UX designers. With Typeform, you can design your own surveys from scratch or choose from a range of templates. After you’ve distributed your survey, you can see responses and completion rates and generate shareable reports. 

• Dozens of UX research templates, including a user persona survey template , a product research survey template and a product feedback template
• Conditional logic to ensure that users only see relevant follow-up questions based on their previous answer
• Shareable reports after survey completion
• Integrations for Google Sheets, Slack, Airtable and more

Typeform has a free plan with unlimited forms, 10 questions per form, and 10 responses per month. You can stay on the free plan for as long as you like, or upgrade for additional features:

• Basic: €21/month (1 user, unlimited typeforms, up to 100 responses/month)
• Plus: €46/month (3 users, unlimited typeforms, up to 1,000 responses/month)
• Business: €75/month (5 users, unlimited typeforms, up to 10,000 responses/month)

View all price plans and features on the Typeform website .

5. Maze for user surveys, concept validation, and wireframe & prototype testing

Maze is another UX research all-rounder with a focus on rapid testing. You can use it for card sorting, tree testing, 5-second tests, surveys, and to test wireframes and prototypes on real users. 

Maze integrates with all the industry-standard UX tools like Figma, Sketch, Adobe XD and InVision. It’s even got a built-in panel of user testers, promising user insights in less than 2 hours. 

Maze also handles the analytics, presenting your research insights in the form of a visual report. 

• Prototype testing to validate your designs before developing them
• Tree testing to ensure your information architecture is user-friendly
• 5-second testing to assess user sentiment when first interacting with your product
• Surveys to scale your UX research
• Card sorting to help plan or test your product’s information architecture
• Built-in panel of over 70,000 testers
• Analytics and visual reports

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6. UserZoom for surveys, card sorting, click testing, and usability tests

Similar to Optimal Workshop and Maze, UserZoom is a complete UX research toolbox used for card sorting, usability testing, surveys, click testing, tree testing, and user interviews. The platform also includes a fully-integrated participant recruitment engine with over 120 million users worldwide. 

• Moderated and unmoderated usability testing
• Surveys for quickly gathering user feedback at scale
• Click testing to evaluate early stage concepts
• Open and closed card sorting to inform your information architecture and understand your users’ mental models
• Interviews to gather self-reported insights from your users
• Tree testing to assess your information architecture
• Participant recruitment engine with over 120 million users worldwide
• Integrations with Adobe XD, Miro, Jira, Mural, Typeform and more

UserZoom offers custom price plans depending on your needs. Find out more here .

7. dscout for remote user interviews and diary studies

A versatile suite of research tools, dscout is ideal for conducting remote user studies. 

There are four main pillars of the dscout platform: Diary, Live, Recruit, and Express. 

Diary is a remote diary studies tool which allows you to gather contextual, qualitative insights into user behaviour and experiences. If you’re new to diary studies, dscout has put together a helpful guide on how and why to conduct diary studies here . 

Live is a user interview tool, and Express is a flexible user survey solution. Recruit is the final piece in the puzzle: a panel of over 100 thousand users you can enlist for your UX research.

• Diary for conducting remote diary studies
• Live for user interviews with auto-transcribe, real-time note-taking and screen-sharing capabilities
• Express for user surveys
• Recruit, a built-in panel of 100 thousand user research participants
• Research synthesis and analysis: automatically generate charts and word clouds
• Loads of guides, resources and templates to help you get started

dscout offers customisable subscription plans depending on your needs. You can learn more about the different plans and request a quote here .

8. Hotjar for analytics and heatmaps

Hotjar is a powerful behaviour analytics tool that enables you to really see how your users engage with an existing product. 

You can use Hotjar to send out surveys, capture and watch screen recordings of people interacting with your website, create heatmaps, and gather real-time user feedback. Hotjar is all about stepping into your users’ shoes and improving the user experience accordingly!

• Heatmaps to see where users click and how they navigate your site. This is helpful for identifying any usability issues or UX flaws
• Screen recordings to see first-hand how people interact with your product
• Real-time user feedback via a suggestion box integrated into your website
• Surveys and survey templates 
• Integrations with Slack, Miro, Jira, Asana and more

Hotjar’s basic free plan is pretty extensive, offering up to 35 daily sessions, unlimited heatmaps, and up to 1,050 recordings per month. For more research capability, paid plans include:

• Plus: €31/month —ideal for small teams
• Business: €79/month —for growing companies and websites
• Scale: €311/month —for large companies and websites

See Hotjar’s price overview for more information.

9. Reframer for analysing qualitative research

Reframer is part of the Optimal Workshop suite of UX research tools (number 1 on our list), but we think it’s worth a special mention. As UX designer Carrie Nusbaum notes in her own review of Reframer : “There are many tools that support the act of actual user testing, and many that facilitate design. Relatively few tools, however, specifically support some important steps that take place in between, namely: data organisation, research synthesis, and presentation of findings.”

Reframer seeks to fill this gap. It’s a unique tool dedicated to capturing all your qualitative research notes in one place, helping you to analyse and make sense of them. It’s your “qualitative research sidekick”, bringing some much-needed structure to the often messy task of qualitative research. 

• Directly capture research observations straight into Reframer; no Post-it notes or separate Google Doc needed
• Theme builder: easily construct a coding system with tags and build out themes for your research findings
• Chord and bubble charts to visualise your findings and easily spot patterns and trends
• XLS export: you can export your research as a .xls file, enabling you to transfer it to other tools and platforms if needed

You can use Reframer as part of the Optimal Workshop toolbox. Optimal Workshop offers a free plan which you can use for as long as you like. For increased functionality, the following paid plans are available:

10. Asana for planning and organising your UX research

Asana isn’t a UX research tool per se, but it’s an excellent tool for organising and keeping track of all your research projects. 

With the Timeline feature, you can create project plans to see exactly what’s happening and when, or visualise your workflow with a Kanban-style board . This allows you to drag and drop cards into different columns depending on their status (e.g. in progress, awaiting feedback, done). 

You can add multiple collaborators to different projects, assign various tasks to individual team members, and provide updates via the commenting function. 

Asana essentially has everything you need to manage your research projects collaboratively from start to finish. 

• Shared team calendar for an overview of who’s working on what, and when
• Visual project management in the form of lists or boards, with the ability to break projects down into smaller subtasks and assign them to different stakeholders
• Project briefs and templates to standardise and streamline your workflows
• In-platform communication via task comments or private messaging
• Integrations with Slack, Google Drive, Dropbox, email, and more

You can use the free basic version of Asana for as long as you like, with extensive capabilities (ideal for individuals and smaller teams). For more robust project management, Asana offers two paid plans:

• Premium at €10.99/user per month
• Business at €24.99/user per month

You’ll find more information on Asana’s pricing overview page .

Ultimately, the tools you choose to work with will depend on the UX research methodologies you want to use, and on the scale of your research. 

If you’re conducting small-scale research with just a few participants, you may not need an entire suite of tools with recruiting and analytics built in—a good survey tool and reliable video conferencing software should suffice. 

But, if you’re conducting large-scale research with dozens or even hundreds of participants, and working as part of a team, you’ll want a set of UX research tools that are collaborative and versatile, covering everything from recruiting to synthesis and analysis. 

You can mix and match your research tools, too: you might use Typeform for surveys, Lookback for user interviews, and Asana to collate all your findings. Before you settle on a specific tool, try it out with a free trial, read up on what other designers have said about their experience with the platform, and compare it to a few alternatives on the market. 

Hopefully this guide has given you a good starting point from which to build out your UX research toolkit. If you’d like to learn more about UX tools, check out this complete guide to the best tools for every stage of the UX design process .

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• Open access
• Published: 24 April 2024

Designing a tool ensuring older patients the right medication at the right time after discharge from hospital– the first step in a participatory design process

• Thorbjørn Hougaard Mikkelsen 1 , 2 , 3 ,
• Jens Søndergaard 3 ,
• Niels Kristian Kjær 3 ,
• Jesper Bo Nielsen 3 ,
• Jesper Ryg 4 , 5 ,
• Lene Juel Kjeldsen 6 &
• Christian Backer Mogensen 1 , 2  

BMC Health Services Research volume  24 , Article number:  511 ( 2024 ) Cite this article

Metrics details

On average, older patients use five or more medications daily, increasing the risk of adverse drug reactions, interactions, or medication errors. Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors. Therefore, it is crucial to identify possible solutions to decrease these risks. Patients, relatives, and healthcare professionals were asked to design the solution they need.

We conducted a participatory design approach to collect information from patients, relatives, and healthcare professionals. The informants were asked to design their take on a tool ensuring that patients received the correct medication after discharge from the hospital. We included two patients using five or more medications daily, one relative, three general practitioners, four nurses from different healthcare sectors, two hospital physicians, and three pharmacists.

The patients’ solution was a physical location providing a medication overview, including side effects and interactions. Healthcare professionals suggested different solutions, including targeted and timely information that provided an overview of the patient’s diagnoses, treatment and medication. The common themes identified across all sub-groups were: (1) Overview of medications, side effects, and diagnoses, (2) Sharing knowledge among healthcare professionals, (3) Timely discharge letters, (4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

All study participants describe the need for a more concise, relevant overview of information. This study describes elements for further elaboration in future participatory design processes aimed at creating a tool to ensure older patients receive the correct medication at the correct time.

Peer Review reports

Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors [ 1 , 2 , 3 ]. Medication of older patients following hospital visits is often seen as particularly complex [ 4 ]. Polypharmacy adds significantly to this complexity due to the uncertainty about how often and for how long medication is needed, challenges in sharing information in sector transitions with different healthcare professionals, and the patients’ and relatives’ cognitive ability and motivation to follow medication plans [ 5 ]. During hospitalisation, 60% of patients receive three or more changes to their medication, and the risk of a harmful event increases significantly with each prescription change [ 6 , 7 ]. Older patients often use five or more prescription medications daily [ 8 , 9 ], but polypharmacy is not always beneficial for the patient [ 10 , 11 , 12 , 13 , 14 , 15 ], and some older patients experience severe side effects [ 16 , 17 , 18 , 19 , 20 ] often due to drug-drug interactions [ 21 , 22 ]. In addition, previous parts of this study have shown that older patients are often concerned about drug-drug interactions and side effects as well as confused about aspects such as names, labels, and when to take the medication [ 23 ]. Therefore, the discharge of elderly patients from the hospital is a complex process where robust tools are needed to support the correct medication at the correct time. For international readers it is important to know a particular artefact in the Danish healthcare system. When the shared Medication Record (SMR) was established to document prescribed medications for a patient over ten years, a new word, “ordineret medicin,” was introduced, which translated means non-prescription medication. This phrase was introduced to distinguish between an active prescription and a passive non-prescription medication. The SMR is a continuously updated and accessible online overview for patients and healthcare professionals regardless of sector, and gives healthcare professionals, and patients access to view current medications, including dose and prescription redemption [ 24 , 25 ]. SMR also enables healthcare professionals to see the patient’s medication history and register changes [ 26 ]. Upon discharge, GPs receive a discharge summary from the hospital describing the treatment and suggesting follow-up. If home care is needed, the municipality receives a patient treatment- and care plan from the hospital so the municipality can prepare for the patient’s return home. The patient treatment- and care plan will among other things include information regarding the hospitalization, diagnoses, medication, and required nursing and homecare support after discharge [ 27 ]. This knowledge is important to understand some of the results of this study. Despite these systems enabling sharing of information improvements are needed to ensure the right medication for older patients [ 23 ].

To develop a solution for solving major medication challenges facing polypharmacy patients when discharged from the hospital, we invited relevant actors to design their vision of the most suitable and robust tool. In this study, we will explore the first step in this design process of a future hopefully robust tool to be used, when patients cross healthcare sectors. Previous studies were typically based on input from only one stakeholder, whereas our study invited both clinicians from both healthcare sectors and patients into the same participatory design process with the purpose of developing a tool to be shared, appreciated and seen as useful for all stakeholders.

This study aims to provide knowledge about key elements a future solution should include to ensure correct medication treatment for older patients transitioning between secondary and primary healthcare sectors.

• Participatory design

This study focuses on older patients treated with five or more medications and we used a participatory design (PD) process including patients, relatives and healthcare professionals. PD is beneficial when exploring informants’ wishes and creating new solutions [ 28 ].

PD studies combine the use of different methods and activities running simultaneously during the entire process: Literature studies, field studies, design and development, and testing [ 29 ]. Within the health sciences, PD is typically broadly divided into 3 phases. In phase 1, the users’ needs are identified and discussed in this study using FGIs. In phase 2, a prototype is developed and designed through workshops. Mock-ups and proposed solutions are designed, tested, and retested to develop a prototype that can be pilot-tested [ 29 , 30 , 31 ]. In phase 3, the prototype is tested [ 29 , 30 ]. This study reports the first step in phase 2 aiming at laying the foundation for developing prototypes and Mock-ups in future studies if financed.

We asked participants to create a tool or solution, to enhance adherence. We let the participants think, discuss, and report their reflections on the best solution [ 28 , 32 ].

Our object was not defined a priori. Hence, we began the process with a brainstorm, where the participants were asked to list essential aspects to ensure all patients received the correct medication. The factors identified from the brainstorm were discussed in smaller groups of peer participants. The small groups were asked to design a solution, later presenting to the group how it would work [ 28 , 32 ].

Brainstorming generates ideas emphasizing many solutions without consideration for practicalities [ 33 , 34 ]. The participants were instructed not to be critical of ideas but to describe any additional ideas that come to mind, no matter how wild. It was emphasized that the brainstorm aimed to generate many ideas, and participants were encouraged to be innovative after hearing other’s ideas [ 34 ]. Many studies described brainstorming in groups as suboptimal in productivity compared to brainstorming individually beforehand [ 34 , 35 , 36 ]. Hence, individual brainstorming was conducted before participants shared ideas and initiated the design process.

Setting and participants

Participant recruitment aimed at achieving rich and diverse perspectives [ 29 ]. GPs and nurses were invited to the participatory design process through one of the co-authors (NK) professional network and GPs associated with Hospital Sønderjylland, University Hospital of Southern Denmark. Homecare nurses were invited through their local municipality and hospital nurses through their departments. Pharmacists were invited through a local pharmacist. Patients and relatives were invited following admission to the emergency department if 72 years or older and managing five or more medications themselves or with the help of a relative and able to transport themselves to the PD process at the hospital. Patients with dementia were excluded. The participating patients and relatives have previously participated in focus group interviews (FGI) reported elsewhere [ 23 ] and were subsequently invited to participate in the participatory design process. The inclusion of patients invited to the FGIs was based on consecutive sampling among patients admitted to the Emergency Department at Hospital Sønderjylland. The patients were invited while admitted to the department during ten days in April, May and June 2021 [ 23 ]. Overall 31 patients were eligible for the FGIs. A total of 10 patients, here of three with a spouse, accepted the invitation to the FGIs. One died before the FGI and another did not show up [ 23 ]. Patients and relatives participating in the FGIs were invited to participate in the making process. The three pharmacists could not attend the participatory design process on the same day as the other actors and were invited to participate on an alternative day. All participants, except one hospital physician and one pharmacist, were Danish by ethnicity (ethnicity not stated due to anonymity aspects).

Six following groups of similar participants were created: (1) Three GPs, (2) Two chief physicians, (3) Three pharmacists, (4) Two nurses employed in general practice, (5) One participating hospital nurse was grouped with the two homecare nurses. (6) Two patients aged 73 and 78 years and one relative. In total, 16 informants with different backgrounds participated.

Data collection

The participatory design process took place at the hospital. The first author (THM) prepared a generative toolkit (Picture 1), in addition, the participants had access to a wide range of other remedies such as paper and cardboard in many colors.

The material was presented at the beginning of the participatory design process and included a short statement about the ambition of the process, which was also stated in the invitation. In addition, the informants were informed verbally and in writing about the study’s details and asking them to sign a consent form highlightning that participating was voluntary and anonymous and that their participation would have no influence on their subsequent treatment as well as explaining that the purpose of the research study.

The participatory design process

Firstly the Informants were welcomed individually and seated in groups with peer participants, e.g. GPs together, nurses together. The agenda was as follows:

Short outline of the workshop.

Presentation of the program.

Brainstorm about important aspects of ensuring the right medication at all times.

The task as presented to the participants and visible on PowerPoint during the whole process: “Your task is to design the perfect tool to ensure you always get the right medication in the right place. Focus on the solutions and functions of the tool. Build the tool with the remedies we have gathered here. The things you decide to add to the solution must have a function corresponding to a need you or others have- how it looks doesn’t matter, but remember the function of the different parts because we will ask you to present your new tool to the larger group.”

Presentation of the generative toolkit.

Making a “thing” that can ensure the right medication at all times.

All groups present their solution to the other groups.

The workshop was facilitated by THM and lasted 2 h and 15 min. There was approximately 1 ½ hours for the making process and half an hour for the presentation of the solutions. The participants had access to refreshments during and after the workshop. The atmosphere was good and empathic addressing the participants own everyday problems and at the same time acknowledging other participants’ situations and working conditions during the presentations.

During the participatory design process, the informants undisturbed generated, tested and elaborated on ideas until the presentation of the models. Data was captured during the presentations to the larger group and were recorded and subsequently transcribed in full, coded, and sorted by THM, JS, and CBM.

The analysis of data follows methods often applied in participatory design studies [ 28 , 29 , 31 ]. We applied an inductive approach focusing on the informants’ descriptions, perceptions, understandings, and ideas. We also applied a deductive analytic strategy based on the themes presented by other informants and identified through the literature. The group discussions were analyzed phenomenally, focusing on the informants’ experiences and perceptions [ 37 , 38 ].

The participants were asked to design a tool to illustrate how to ensure patients always get the right medication at the right time. Their solutions were diverse. The patients built a health centre (Additional file 2 ), the chief physicians a health card containing all key information about the patient (Additional file 3), the general practitioners a communication channel to the hospital (Additional file 4 ), the nurses employed in general a solution ensuring that the same information is available to all health professionals (Additional file 5 ), the pharmacists designed a combined database and communication channel (Additional file 6 ) and hospital- and homecare nurses design the good discharge process (Additional file 7 ). However, the common factor for all solutions was the focus on an overview of the patient’s diagnoses and treatment. During the analysis, the following themes were identified: (1) Overview of medications, side effects, and diagnoses, (2) Sharing knowledge among healthcare professionals, (3) Timely discharge letters, (4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

Overview of medications, side effects, and diagnoses

All participants strived for solutions that created an overview. The patients asked for an overview of their medication, side effects, and interactions. The healthcare professionals aimed for an overview of the patient’s diagnoses and elements important for treatment, such as the presence of a pacemaker. This information should be available in a single solution.

Chief physician: We are affected by the same fatigue as the other groups have expressed, we do not have the information we need, not even from you (general practice ed.) when you send the patients in, then we face fragmented knowledge and we need to collate and update the information. Is it possible to summarise the information using one solution, preferably a solution that the patient has e.g. a chip or something?

For patients, the most important thing is to get an overview of the medication, the associated diagnosis, and interactions. Therefore, the patients/relative group suggested a healthcare centre to provide answers regarding medications and health issues.

Patient: Medication is a huge issue. I’m so uneasy about being sent from one hospital to another. Every time you talk to a doctor, you get a new medication. How does the new medication affect the other medications?

The patients request contact with a physician responsible for an overview:

Spouse: there are many people who need to know about the medication, how to take medication, how to act if you get the wrong medication because you can also experience adverse drug reactions.

In this way, the patients request access to a central healthcare information centre with profound knowledge of the patient’s diagnosis and medications, including side effects and interactions with other medications, and responsibility for the patient’s treatment.

The patients built a health centre that collated information, provided an overview of diagnoses and medications, and gave knowledge about side effects and interactions.

Patient: When you come to this house, you get an answer you can understand. When you are discharged from hospital, you are often left with new medications, and you are left to your own devices or you have to contact your GP. We request closer cooperation between the hospital and the general practitioners or health care centres. Because sometimes, when you come home, you realise it is difficult to understand the mixed medication you have been given.

Thus, patients ask for a solution that collates information about diagnoses, medications, and interactions and can explain it to the patients. However, it is a prerequisite for healthcare professionals to be able to create an overview of diagnoses and treatments.

Sharing knowledge among healthcare professionals

All participating healthcare professionals asked for additional information from other parts of the healthcare sector. All of them have access to SMR, showing the patients’ current prescriptions and medications prescribed within the last ten years, giving profound information regarding the patient’s medication.

The participating homecare and hospital-employed nurses build an illustration of the good discharge illustrating their principal wishes:

Hospital-employed nurse: We have looked into the available communication tools to see how they can ensure that the medication and the medication management are handled in the best way. […] We have tried to illustrate the path to a good discharge. And the cornerstones […] were that the SMR is updated and were the patient given a sufficient amount of medication to take hom e until the new medication could be retrieved or delivered from the pharmacy, […] and that there are prescriptions for the new and previous medication […], and then; who collects it (at the pharmacy ed.) […] - we have our treatment and care plans, we can send them out to each other, but (the homecare nurses ed.): It’s fine that you (the physician ed.) prescribe a new medication, but we also need to know the indication/purpose… .

As the quote shows, there are many aspects regarding a good discharge. An important part is that the SMR is updated, ensuring primary healthcare the relevant and updated information regarding the medication. It is also important to ensure that the patient has the right and sufficient amount of medication at home and if not, a plan to ensure how the patient can access more or new medication, as well as a plan for a follow-up consultation when needed. Finally, they request information about the diagnoses leading to a new prescription.

Timely discharge summaries

The GP receives a discharge summary from the hospital when a patient is discharged. However, the GPs also requested more information such as diagnosis, what information was given to the patients, and timely discharge summaries:

GP: What we lack in this communication channel is that the discharge summay arrives on time and contains the necessary information. If there have been changes in medication, we need to know why. […] The medication that may have been prescribed; is the patient informed well enough about it? […] If they receive dose dispensing, […] then we must also have a home nurse over so that we can get them dosed up as a supplement to their usual medication.

The GPs ask for different types of information, including that the discharge letters are received quickly. However, this can be logistically difficult for hospital doctors as hospital secretaries are given three days to prepare discharge letters.

The participatory design process shows that the discharge letters are important for the GPs and that it is important that they are received shortly after discharge so that they can contribute to ensuring that the patient always receives the right medication at the right time.

4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional?

As described above, SMR contains all medication prescribed to the patient within the last ten years. The diagnosis is stated in the discharge letter, although the citations below indicate they don’t always fully meet the wishes of the GPs.

Chief physician: Do you receive discharge letters that you find informative and make you feel well-prepared (for resuming the treatment of the patient ed)? GP: The problem is if they are the standardized ones, then there will be far too much unnecessary information, and then we will go straight to the conclusion. And then, unfortunately, you may sometimes overlook some important information.

Thus, too little but also too much irrelevant information can be problematic. The challenge with too much irrelevant information is that the general practitioner cannot form a quick overview of the patient’s treatment at the hospital. Likewise, the chief physician does not want to provide too much information. As a chief physician said when presenting their model:

Chief physician: That is also why we propose… you have to define what is common because there is no reason for us to know everything that happens out there, because it will not be relevant and focused, and it will require too much sorting work. But there are some common things of mutual benefit that we all should all know.

In summary, all participating groups request targeted information. They did not request the same information showing that some information should be available to all the participating groups while other information should target specific groups. In this way, a solution/tool to ensure that the patient always gets the right medication should collect the relevant information to allow an overview, and ensure targeted information to the relevant actors to prevent information overload and loss for the healthcare professionals, but also avoid insecurity and confusion for the patients.

The participating healthcare professionals requested targeted information corresponding to the patient’s preferences and expectations. The patients requested one integrated service or a healthcare professional who has the overview of the patient’s diagnosis and medications, including side effects and interactions with other medications, and responsibility for the patient’s treatment. This could be a physician, clinical pharmacist at the hospital, or GP. This corresponds with a systematic review of interventions to increase medication adherence showing that verbal and verbal/written information was the most effective [ 39 ]. This study adds that even though the different stakeholders ask for different information, this different information can be contained in one shared tool to be developed ensuring useful and targeted information to all stakeholder groups.

All the informants want a better overview of the patient’s treatment, medication, and diagnoses despite the fact that that medications prescribed to patients are already accessible online to all groups of informants in SMR [ 24 , 25 , 26 ] and that GPs already receive a discharge summary from the hospital with suggested follow-up. If needed, the municipalities homecare, receive a patient treatment- and care plan from the hospital typically including information regarding the hospitalization, diagnoses, medication, and required nursing and homecare support after discharge [ 27 ]. In summary, all groups already have a large degree of access to information. To ensure the right medication at the right time these data need to be targeted and presented in a way that makes it easy to ensure the patient the right medication at the right time, targeting the different groups and their responsibilities. Hence it may be more important to be able to provide the right information to different groups at the right time, rather than synthesizing the results at this point and, risking not addressing some of the issues presented in further PD processes. Although the participatory design process was about how to ensure that the patient always gets the right medication, the process showed that the stakeholders also want to know and share other related health information, including the diagnosis / medical indication for medication, also described elsewhere [ 40 , 41 ] and, in the case of the participating patients, side effects, and interactions, aspects also described elsewhere [ 23 , 42 , 43 ]. The GPs also requested what information was given to the patients, as described elsewhere [ 40 ], which also should be in a language understandable to the patient, also described elsewhere [ 17 , 44 ]. The different groups of participants built solutions related to their tasks and their tools. For example, nurses focused on the perfect patient discharge using tools in place with additional elements for improvement. This example demonstrates the relevance and strength of PD and why it is important to invite many different actors in an iterative PD process [ 31 ] before a final solution is fully developed which also will allow other possible aspects and solutions to emerge.

Perspectives

Despite many years of research in sharing information between healthcare professionals [ 40 , 41 , 44 , 45 , 46 ] and the fact, that it is already possible to gather and share information within the framework of Danish legislation through SMR and the discharge summary to the GPs as well as the patient treatment and care plan to the homecare nurses [ 27 ] this study show, that targeted information is still requested by the participants. There is a need to ensure that the information is present, that it is easy to find, and does not disappear in an irrelevant information overload.

These findings can strengthen the focus on cross-sectoral communication when combined with other available experiences such as further studies on the use and shortcomings of discharge summaries. The findings can be used for the future process of optimizing existing communication channels between healthcare sectors. Hopefully, the findings can also contribute to developing a “tool” or platform that provides a fast, sufficient, and safe overview for all the health professionals engaged in the individual patient’s care.

The first step towards a solution ensuring that the patient always receives the right medication is to create an overview of what information the different actors want, especially the patients and relatives. This study is the first in a repetitive, iterative PD process to find a solution. The fact that the participants built different solutions shows that different needs can coincide. Therefore, future PD processes must be split between professionals and patients in parallel paths to focus on the professionals’ wishes for an online solution, in combination with a solution that can support citizens’ wishes for a physical location or a possible app that may be of interest among younger and future older generations as described elsewhere [ 47 , 48 ]. This knowledge can be used to develop a solution during future repeated iterative PD processes developing several prototypes, testing, and developing the common solution gradually [ 31 ].

Strength and limitations

It is a limitation that a relatively small number of different participants attended. However, it is a strength that all central actors are represented; GPs, the hospital, and the municipality as well as some patients and a relative participated in the PD process. We recognize the limited number of patients in our sample, but the Danish society especially among older citizens is quite homogeneous, all have free access to health care, and data was sampled though interviews, so we are not particularly worried about representability among the patient group. In addition PD processes are often conducted with a rather small number of participants, including patients [ 30 , 49 , 50 ] and is known as a reliable method [ 31 , 49 , 50 ].

Bias may occur if the informants do not express their actual attitudes if they feel insecure in the setting. Hence the actors were grouped with like-minded participants to ensure an environment where they did not restrain themselves out of respect for others. All the participants, including the patients and the relative participated and spoke freely, and the atmosphere was friendly and relaxed. The participation of patients and a relative is considered a strength as they enriched the discussion. The first author (THM) is a trained researcher in qualitative methods and ensured that all voices, experiences, and opinions were heard and presented. As a sociologist, THM had no prior knowledge regarding patients’ medications and the problems facing older patients after discharge from the hospital or knowledge of the problems of healthcare professionals.

A further strength of this study is that the participating patients and relatives managed multiple medications daily and were well-functioning. The participants had a high degree of knowledge about their illness and were willing to discuss central issues about managing the disease. Patients are probably the best informants to highlight the factors preoccupying this target group. A further limitation of the study is that frail senior citizens may be underrepresented, and patients taking no particular interest in their medication might be expected to decline participation in the focus group interviews. However, the participation of healthcare professionals enabled the perspectives related to frail patients or patients with no particular interest in their medication to be included.

All participants in this study state that they lack an overview of patient-related information. Patients lack an overview of their medication, side effects, and interactions. Health professionals lack an overview of the patient’s diagnoses and other factors of importance for the treatment. While the patients wish that the service are available in one physical location, the healthcare professionals wish that important information is gathered, sorted, and accessible to the relevant healthcare professionals online at all times. These two wishes are not mutually exclusive, but important elements should be elaborated upon in future PD processes to ensure that older patients receive the right medication at the right time.

Data availability

The datasets are not publicly available due to regulations from The Danish Data Protection Agency.

Abbreviations

General practitioner

Odense University Hospital

an online Shared Medication Record that can be accessed by the patient and healthcare professionals across sectors. In Danish called Fælles Medicinkort (FMK)

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Acknowledgements

The authors thank the participating informants for participating and sharing their knowledge, experiences, and ideas.

A grant from VELUX FONDEN supported this study (grant no. 34175). VELUX FONDEN did not participate in or influence the design of the study as well as the collection, analysis, and interpretation of data or in writing the manuscript.

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T.H.M. designed the study, collected the data, analyzed and interpreted the data, and drafted the manuscript. J.S. and C.B.M. designed the study, analyzed and interpreted the data, and commented critically on the manuscript. J.B.N., J.R., L.K. and N.K. contributed to the design of the study, the interpretation of data, and commented critically on the manuscript. All authors reviewed the manuscript.

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This study was conducted according to the guidelines of the Declaration of Helsinki. The project was sent to the Regional Committee of Health Ethics in the Region of Southern Denmark, Denmark, for approval (case no. 20212000-69). According to the committee, the project falls outside the scope of a notifiable Health Science research project as it is based on interviews. Therefore, the principles of consolidated criteria for reporting qualitative research [ 51 ] were followed as well as the guidelines of the Declaration of Helsinki. Storage management of the data fulfilled the European General Data Protection Regulations. All Informants gave informed consent and signed a consent form. Informants were informed that they were free to withdraw their consent at any time and that the findings would be anonymous.

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Mikkelsen, T.H., Søndergaard, J., Kjær, N.K. et al. Designing a tool ensuring older patients the right medication at the right time after discharge from hospital– the first step in a participatory design process. BMC Health Serv Res 24 , 511 (2024). https://doi.org/10.1186/s12913-024-10992-3

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Interior Design Through the Decades

Over the years, interior home designs have reflected personal styles and events of each era. Many design trends make comebacks multiple times.

NEW YORK – From decade to decade, our personal spaces have reflected the times, impacted by the events of each era that influence not only the nation's temperament, but also the availability of materials and objects to furnish homes.

To relive the most memorable design movements over the last 100 years, Living Spaces traveled back in time via archival home images in magazines, libraries, museum collections, and Instagram accounts. Historical overviews by Architectural Digest, Interior Design, and Better Homes & Gardens also provided insights into the pivotal events of the era that helped shape American interior design.

Each era brought us something fresh — the post-World War I glamour and optimism of the Roaring '20s that embraced the extravagance of art deco design, the technology boom after World War II, the earthy palette and handcrafted furnishings of the '70s after the first Earth Day, and the influence of technology and 21st-century home design stars making interior design accessible to all.

Step into our time machine and learn of the many trends that may have you waxing nostalgic about your childhood home or first apartment. Thankfully, many of these trends make comebacks time and again.

Luxe and more in the 1920s

There was much to celebrate during the Roaring '20s: a booming economy, women's right to vote, steamship and air travel. There was a feeling that anything was possible following the victory of the Allied powers in World War I, and art deco embodied this.

Introduced at the 1925 Exposition Internationale des Arts Décoratifs et Industriels Modernes in Paris, designers expressed art deco through luxurious upholstery and heavily ornamented decor. Glamour was key. Bold patterns and stylized interpretations of natural wonders like sun rays and geometric ones like chevrons abound in everything from wood inlays to chandeliers and doors. But not everyone embraced or could afford such extravagance.

Enter German Bauhaus design, which was more accessible with mass-produced furniture. It saw beauty in simplicity, stripping furniture down to its essence in form and shape. Marcel Breuer's revolutionary Wassily Chair, designed in 1925, exemplified this: no cushioning, heavy upholstery, or wood base—just strips of leather stretched tight across a tubular steel frame.

Paring down during the 1930s

The country had just entered the Great Depression, which would last through the decade. Americans had little, if any, disposable income. Basic necessities became the priority, reflected in the move toward a more minimalist, industrial-influenced approach to furnishings.

Art deco was still in play with its geometric patterns and sleek, showy materials such as lacquer, glass, and mirrors, as well as skyscraper-esque furnishings. (The Chrysler Building and Empire State Building both opened at this time, in 1930 and 1931, respectively.)

Then streamline moderne arrived with the Chicago World Fair's "A Century of Progress." This style was a pared-down version of art deco and heavily used the latest engineering technologies. Streamline moderne was characterized by long, clean lines and transportation design-influenced, aerodynamic shapes such as ships or bullets.

Instead of angled tables and appliances, rounded edges became more prevalent, along with the use of steel, glass, and less expensive materials. Colors and patterns were also more muted than in the previous decade.

Tech on the rise in the 1940s

America entered World War II at the end of 1941, shifting factories' priorities from consumer demands to supporting the war effort. During the war, technology developed, allowing for the mass production of furniture at more affordable prices and using materials such as plastic and fiberglass.

Form, function, and optimism were the driving forces of one of the most popular design styles: modernism. Glass, metal, and wood were still heavily used but with even less ornamentation than in the 1930s.

During this time, one of the most enduring Charles and Ray Eames lounge chair designs, the plywood LCM featuring metal legs, was produced in Los Angeles. Also notable in the late '40s was the launch of the iconic womb chair by Eero Saarinen, an upholstered and pillow-cushioned chair that hugged the body and sat on a metal frame.

Modernism in full bloom during the 1950s

People had more money to spend with the financial prosperity that followed WWII. The lush interiors of old Europe still found their way into upper-class Americans' homes: decorative upholstered chairs, grand chandeliers, gaudy gold frames, and antique tables worthy of royalty. But a more contemporary aesthetic started to emerge in contrast. Fewer frills, an emphasis on function, and clean lines defined the popular midcentury modern and Scandinavian styles.

Many iconic pieces still in demand today arrived, such as the Eames molded fiberglass chairs and Saarinen's pedestal tulip fiberglass chairs and iconic dining tables. The single-story ranch home reigned in terms of residential architecture, with most featuring open-floor plans with dining areas bleeding into living rooms versus being separated, allowing for larger communal spaces to entertain.

The pastel color palette of the era, including lilac and soft pink, was calm yet uplifting, with more showy colors (dusky red and turquoise) also making a statement in homes.

Future-forward in the 1960s

A rejection of the status quo embodied in counterculture, antiwar movements, and the excitement of the Space Race influenced '60s home interiors — vibrantly colored flower-powered patterns on walls and upholstery celebrated self-expression.

Widespread recreational drug use accompanied the counterculture movement, with lava lamps and black lights (and psychedelic posters specifically designed for them) illuminating rooms and entertaining the eyes with trippy colors and shapes.

Stretching people's imagination also came in the form of futuristic lighting such as the rippling Splügen Braü aluminum pendant light and the swooping Viscontea lamp, both by the Castiglioni brothers, as Americans sought to venture into worlds beyond Earth's atmosphere.

Scandinavian design also brought stylish open wood shelving units and shelves to display brag-worthy collections and Verner Panton's curvy, legless plastic chair into homes.

Inspired by environmentalism in the 1970s

The first Earth Day celebrated in 1970 marked a move toward environmentalism, and an earthy color palette followed to dominate the decade. Designers applied rust, brown, avocado green, and mustard yellow to everything, including furnishings and appliances.

Wood paneling, comfy seating, and macrame galore were some of the features that spoke to the casual, laid-back interiors of the '70s. With sunken living rooms, shaggy carpet, and furniture you could sink into, like bean-bag-like seating, waterbeds, and low-back couches, the theme was relaxation.

Home design also showed less restraint, favoring open plans and natural materials, like wood, leather, and rattan, over steel and handcrafted items over machine-made.

Postmodern party times in the 1980s

By the mid-1980s, nearly every American household had a TV — MTV was on 24 hours a day—and, on average, people were glued to their sets for 7 hours a day. The decade's most popular shows heavily influenced how Americans furnished their homes.

Many took inspiration from their beloved characters' home decor, such as the neon color palette and glass-block walls of "Miami Vice," the luxurious, drape-heavy rooms and plush seating of "Dallas" and "Dynasty," and the frill and tropical-inspired furnishings of the "Golden Girls" (remember Blanche's banana leaf wallpaper and bedspread?).

For the more rebellious set, a group of Italian designers and architects, the Memphis Group, introduced the world to their postmodern eponymous design style of vibrant colors, contrasting palettes, edgy patterns, and asymmetrical furniture pieces—a commingling of art deco and pop art influences, which would in turn shape graphic, fashion, and product design. Think wild geometric and confetti prints, the MTV logo, and Swatch watches.

Good fortune in the 1990s

Progress and prosperity were felt across the nation: The Cold War ended, American household incomes rose, and violent crime was down, among other positive developments. This mood encouraged people to experiment, play, and show individualism in their homes.

Sponge-painted and decal-covered walls, floral and gingham decor, shabby chic interiors filled with frilly fabrics and worn pieces—anything went during the era. Upcycled pieces were in vogue with the sustainability movement gaining ground, while others embraced maximalist tendencies with clashing, colorful-patterned furniture.

Influenced by a NASA study that suggested houseplants could help filter air pollutants, homeowners added greenery to their interiors, hanging plants from walls and placing them on shelves with vines trailing in all directions,

During this time of relative peace, people looked to extend that sense of calm or positive energy to their immediate surroundings—feng shui principles and zen design determined the flow and placement of items in the home.

Well-being during the 2000s

Just as the anxiety over the arrival of Y2K abated, the country suffered the deadliest terrorist attack in U.S. history on 9/11. Architects and urban designers responded with designs of buildings and public spaces to make people feel more secure and at ease. Likewise, Americans sought safety and comfort in their homes following the attacks.

A green living movement encouraged using natural (and repurposed) materials and plants as people started reflecting more on their impact on Mother Earth.

McMansion dwellers found warmth and Old World charm in the Tuscan design aesthetic, particularly in the kitchen, emphasizing earth tones, decorative wood cabinetry, and other ornamentation.

Security fell by the wayside, however, when the Great Recession hit. The era saw stunning job losses—15 million-plus people were unemployed by decade's end — and foreclosures, forcing millions to lose their homes.

Flaunting one's wealth, whether in one's clothes or the interior design of one's home, was frowned upon, keeping the minimalist aesthetic alive. Others opted for secondhand furniture versus purchasing new items to refresh their living spaces.

DIY in the 2010s

America was still feeling the effects of the recession into the 2010s, so budgets were tight. But there was also excitement over the design possibilities with the rise of apps like Pinterest and Instagram, which provide an endless supply of visual inspiration that fits every budget.

Even if you couldn't afford a high-end item, retailers offered inexpensive knockoffs (of midcentury modern furniture, for instance) or DIY solutions to reimagine existing pieces featured in blogs, YouTube videos, and home design shows.

Being more connected than ever globally brought cultural objects from abroad—Moroccan poufs, carved wood cabinetry and tables from India, and African patterned textiles — to decorate homes more than ever. More sustainable practices in furniture production would see a rise in reclaimed wood use and lasting pieces versus disposable ones.

"Fixer Upper" hosts Chip and Joanna Gaines helped popularize modern farmhouse features like shiplap and worn wood furniture, while maximalists enjoyed layering different styles and objects. Decorators put together flea-market finds with new big-box store items. Whether one wanted a boho- or industrial loft-style look, there was no shortage of options.

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