comparative questions in research

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Structure of comparative research questions

There are five steps required to construct a comparative research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the groups you are interested in; (4) identify the appropriate adjoining text; and (5) write out the comparative research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the groups you are interested in

Identify the appropriate adjoining text

Write out the comparative research question

FIRST Choose your starting phrase

Comparative research questions typically start with one of two phrases:

Some of these starting phrases are highlighted in blue text in the examples below:

What is the difference in the daily calorific intake of American men and women?

What is the difference in the weekly photo uploads on Facebook between British male and female university students?

What are the differences in perceptions towards Internet banking security between adolescents and pensioners?

What are the differences in attitudes towards music piracy when pirated music is freely distributed or purchased?

SECOND Identify and name the dependent variable

All comparative research questions have a dependent variable . You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first three examples highlight that while the name of the dependent variable is the same, namely daily calorific intake, the way that this dependent variable is written out differs in each case.

THIRD Identify the groups you are interested in

All comparative research questions have at least two groups . You need to identify these groups. In the examples below, we have identified the groups in the green text .

What is the difference in the daily calorific intake of American men and women ?

What is the difference in the weekly photo uploads on Facebook between British male and female university students ?

What are the differences in perceptions towards Internet banking security between adolescents and pensioners ?

What are the differences in attitudes towards music piracy when pirated music is freely distributed or purchased ?

It is often easy to identify groups because they reflect different types of people (e.g., men and women, adolescents and pensioners), as highlighted by the first three examples. However, sometimes the two groups you are interested in reflect two different conditions, as highlighted by the final example. In this final example, the two conditions (i.e., groups) are pirated music that is freely distributed and pirated music that is purchased. So we are interested in how the attitudes towards music piracy differ when pirated music is freely distributed as opposed to when pirated music in purchased.

FOURTH Identify the appropriate adjoining text

Before you write out the groups you are interested in comparing, you typically need to include some adjoining text. Typically, this adjoining text includes the words between or amongst , but other words may be more appropriate, as highlighted by the examples in red text below:

FIFTH Write out the comparative research question

Once you have these details - (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the groups you are interested in comparing, and (4) any potential adjoining words - you can write out the comparative research question in full. The example comparative research questions discussed above are written out in full below:

In the section that follows, the structure of relationship-based research questions is discussed.

Structure of relationship-based research questions

There are six steps required to construct a relationship-based research question: (1) choose your starting phrase; (2) identify the independent variable(s); (3) identify the dependent variable(s); (4) identify the group(s); (5) identify the appropriate adjoining text; and (6) write out the relationship-based research question. Each of these steps is discussed in turn.

Identify the independent variable(s)

Identify the dependent variable(s)

Identify the group(s)

Write out the relationship-based research question

Relationship-based research questions typically start with one or two phrases:

What is the relationship between gender and attitudes towards music piracy amongst adolescents?

What is the relationship between study time and exam scores amongst university students?

What is the relationship of career prospects, salary and benefits, and physical working conditions on job satisfaction between managers and non-managers?

SECOND Name the independent variable(s)

All relationship-based research questions have at least one independent variable . You need to identify what this is. In the examples that follow, the independent variable(s) is highlighted in the purple text .

What is the relationship of career prospects , salary and benefits , and physical working conditions on job satisfaction between managers and non-managers?

When doing a dissertation at the undergraduate and master's level, it is likely that your research question will only have one or two independent variables, but this is not always the case.

THIRD Name the dependent variable(s)

All relationship-based research questions also have at least one dependent variable . You also need to identify what this is. At the undergraduate and master's level, it is likely that your research question will only have one dependent variable. In the examples that follow, the dependent variable is highlighted in the blue text .

FOURTH Name of the group(s)

All relationship-based research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What is the relationship between gender and attitudes towards music piracy amongst adolescents ?

What is the relationship between study time and exam scores amongst university students ?

What is the relationship of career prospects, salary and benefits, and physical working conditions on job satisfaction between managers and non-managers ?

FIFTH Identify the appropriate adjoining text

Before you write out the groups you are interested in comparing, you typically need to include some adjoining text (i.e., usually the words between or amongst):

Some examples are highlighted in red text below:

SIXTH Write out the relationship-based research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the independent variable, (4) the name of the group(s) you are interested in, and (5) any potential adjoining words ? you can write out the relationship-based research question in full. The example relationship-based research questions discussed above are written out in full below:

STEP FOUR Write out the problem or issues you are trying to address in the form of a complete research question

In the previous section, we illustrated how to write out the three types of research question (i.e., descriptive, comparative and relationship-based research questions). Whilst these rules should help you when writing out your research question(s), the main thing you should keep in mind is whether your research question(s) flow and are easy to read .

Literature Searching

Types of Research Questions

Research questions can be categorized into different types, depending on the type of research to be undertaken.

Qualitative questions concern broad areas or more specific areas of research and focus on discovering, explaining and exploring.  Types of qualitative questions include:

• Exploratory Questions, which seeks to understand without influencing the results.  The objective is to learn more about a topic without bias or preconceived notions.
• Predictive Questions, which seek to understand the intent or future outcome around a topic.
• Interpretive Questions, which tries to understand people’s behavior in a natural setting.  The objective is to understand how a group makes sense of shared experiences with regards to various phenomena.

Quantitative questions prove or disprove a  researcher’s hypothesis and are constructed to express the relationship between variables  and whether this relationship is significant.  Types of quantitative questions include:

• Descriptive questions , which are the most basic type of quantitative research question and seeks to explain the when, where, why or how something occurred. 
• Comparative questions are helpful when studying groups with dependent variables where one variable is compared with another.
• Relationship-based questions try to answer whether or not one variable has an influence on another.  These types of question are generally used in experimental research questions.

References/Additional Resources

Lipowski, E. E. (2008). Developing great research questions . American Journal of Health-System Pharmacy, 65(17), 1667–1670.

Ratan, S. K., Anand, T., & Ratan, J. (2019). Formulation of Research Question - Stepwise Approach .  Journal of Indian Association of Pediatric Surgeons ,  24 (1), 15–20.

Fandino W.(2019). Formulating a good research question: Pearls and pitfalls . I ndian J Anaesth. 63(8) :611-616. 

Beck, L. L. (2023). The question: types of research questions and how to develop them . In Translational Surgery: Handbook for Designing and Conducting Clinical and Translational Research (pp. 111-120). Academic Press. 

Doody, O., & Bailey, M. E. (2016). Setting a research question, aim and objective. Nurse Researcher, 23(4), 19–23.

Plano Clark, V., & Badiee, M. (2010). Research questions in mixed methods research . In: SAGE Handbook of Mixed Methods in Social & Behavioral Research .  SAGE Publications, Inc.,

Agee, J. (2009). Developing qualitative research questions: A reflective process .  International journal of qualitative studies in education ,  22 (4), 431-447. 

Flemming, K., & Noyes, J. (2021). Qualitative Evidence Synthesis: Where Are We at? I nternational Journal of Qualitative Methods, 20.  

Research Question Frameworks

Research question frameworks have been designed to help structure research questions and clarify the main concepts. Not every question can fit perfectly into a framework, but using even just parts of a framework can help develop a well-defined research question. The framework to use depends on the type of question to be researched.   There are over 25 research question frameworks available.  The University of Maryland has a nice table listing out several of these research question frameworks, along with what the acronyms mean and what types of questions/disciplines that may be used for.

The process of developing a good research question involves taking your topic and breaking each aspect of it down into its component parts.

Booth, A., Noyes, J., Flemming, K., Moore, G., Tunçalp, Ö., & Shakibazadeh, E. (2019). Formulating questions to explore complex interventions within qualitative evidence synthesis.   BMJ global health ,  4 (Suppl 1), e001107. (See supplementary data#1)

The "Well-Built Clinical Question“: PICO(T)

One well-established framework that can be used both for refining questions and developing strategies is known as PICO(T). The PICO framework was designed primarily for questions that include interventions and comparisons, however other types of questions may also be able to follow its principles.  If the PICO(T) framework does not precisely fit your question, using its principles (see alternative component suggestions) can help you to think about what you want to explore even if you do not end up with a true PICO question.

A PICO(T) question has the following components:

• P : The patient’s disorder or disease or problem of interest / research object
• I: The intervention, exposure or finding under review / Application of a theory or method
• C: A comparison intervention or control (if applicable- not always present)/ Alternative theories or methods (or, in their absence, the null hypothesis)
• O : The outcome(s) (desired or of interest) / Knowledge generation
• T : (The time factor or period)

Keep in mind that solely using a tool will not enable you to design a good question. What is required is for you to think, carefully, about exactly what you want to study and precisely what you mean by each of the things that you think you want to study.

Rzany, & Bigby, M. (n.d.). Formulating Well-Built Clinical Questions. In Evidence-based dermatology / (pp. 27–30). Blackwell Pub/BMJ Books.  

Nishikawa-Pacher, A. (2022). Research questions with PICO: a universal mnemonic.   Publications ,  10 (3), 21.

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A Short Introduction to Comparative Research

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An Effective Guide to Comparative Research Questions

Table of Contents

Comparative research questions are a type of quantitative research question. It aims to gather information on the differences between two or more research objects based on different variables. 

These kinds of questions assist the researcher in identifying distinctive characteristics that distinguish one research subject from another.

A systematic investigation is built around research questions. Therefore, asking the right quantitative questions is key to gathering relevant and valuable information that will positively impact your work.

This article discusses the types of quantitative research questions with a particular focus on comparative questions.

What Are Quantitative Research Questions?

Quantitative research questions are unbiased queries that offer thorough information regarding a study topic . You can statistically analyze numerical data yielded from quantitative research questions.

This type of research question aids in understanding the research issue by examining trends and patterns. The data collected can be generalized to the overall population and help make informed decisions. 

Types of Quantitative Research Questions

Quantitative research questions can be divided into three types which are explained below:

Descriptive Research Questions

Researchers use descriptive research questions to collect numerical data about the traits and characteristics of study subjects. These questions mainly look for responses that bring into light the characteristic pattern of the existing research subjects.

However, note that the descriptive questions are not concerned with the causes of the observed traits and features. Instead, they focus on the “what,” i.e., explaining the topic of the research without taking into account its reasons.

Examples of Descriptive research questions:

• How often do you use our keto diet app?
• What price range are you ready to accept for this product?

Comparative Research Questions

Comparative research questions seek to identify differences between two or more distinct groups based on one or more dependent variables. These research questions aim to identify features that differ one research subject from another while emphasizing their apparent similarities.

In market research surveys, asking comparative questions can reveal how your product or service compares to its competitors. It can also help you determine your product’s benefits and drawbacks to gain a competitive edge.

The steps in formulating comparative questions are as follows:

• Choose the right starting phrase
• Specify the dependent variable
• Choose the groups that interest you
• Identify the relevant adjoining text
• Compose the comparative research question

Relationship-Based Research Questions

A relationship-based research question refers to the nature of the association between research subjects of the same category. These kinds of research question assist you in learning more about the type of relationship between two study variables.

Because they aim to distinctly define the connection between two variables, relationship-based research questions are also known as correlational research questions.

Examples of Comparative Research Questions

• What is the difference between men’s and women’s daily caloric intake in London?
• What is the difference in the shopping attitude of millennial adults and those born in 1980?
• What is the difference in time spent on video games between people of the age group 15-17 and 18-21?
• What is the difference in political views of Mexicans and Americans in the US?
• What are the differences between Snapchat usage of American male and female university students?
• What is the difference in views towards the security of online banking between the youth and the seniors?
• What is the difference in attitude between Gen-Z and Millennial toward rock music?
• What are the differences between online and offline classes?
• What are the differences between on-site and remote work?
• What is the difference between weekly Facebook photo uploads between American male and female college students?
• What are the differences between an Android and an Apple phone?

Comparative research questions are a great way to identify the difference between two study subjects of the same group.

Asking the right questions will help you gain effective and insightful data to conduct your research better . This article discusses the various aspects of quantitative research questions and their types to help you make data-driven and informed decisions when needed.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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3. Comparative Research Methods

This chapter examines the ‘art of comparing’ by showing how to relate a theoretically guided research question to a properly founded research answer by developing an adequate research design. It first considers the role of variables in comparative research, before discussing the meaning of ‘cases’ and case selection. It then looks at the ‘core’ of the comparative research method: the use of the logic of comparative inquiry to analyse the relationships between variables (representing theory), and the information contained in the cases (the data). Two logics are distinguished: Method of Difference and Method of Agreement. The chapter concludes with an assessment of some problems common to the use of comparative methods.

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This chapter examines the ‘art of comparing’ by showing how to relate a theoretically guided research question to a properly founded research answer by developing an adequate research design. It first considers the role of variables in comparative research before discussing the meaning of ‘cases’ and case selection. It then looks at the ‘core’ of the comparative research method: the use of the logic of comparative inquiry to analyse the relationships between variables (representing theory) and the information contained in the cases (the data). Two logics are distinguished: Method of Difference and Method of Agreement. The chapter concludes with an assessment of some problems common to the use of comparative methods.

Rethinking Comparison

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What is comparative analysis? A complete guide

Last updated

18 April 2023

Reviewed by

Jean Kaluza

Short on time? Get an AI generated summary of this article instead

Comparative analysis is a valuable tool for acquiring deep insights into your organization’s processes, products, and services so you can continuously improve them. 

Similarly, if you want to streamline, price appropriately, and ultimately be a market leader, you’ll likely need to draw on comparative analyses quite often.

When faced with multiple options or solutions to a given problem, a thorough comparative analysis can help you compare and contrast your options and make a clear, informed decision.

If you want to get up to speed on conducting a comparative analysis or need a refresher, here’s your guide.

Make comparative analysis less tedious

Dovetail streamlines comparative analysis to help you uncover and share actionable insights

• What exactly is comparative analysis?

A comparative analysis is a side-by-side comparison that systematically compares two or more things to pinpoint their similarities and differences. The focus of the investigation might be conceptual—a particular problem, idea, or theory—or perhaps something more tangible, like two different data sets.

For instance, you could use comparative analysis to investigate how your product features measure up to the competition.

After a successful comparative analysis, you should be able to identify strengths and weaknesses and clearly understand which product is more effective.

You could also use comparative analysis to examine different methods of producing that product and determine which way is most efficient and profitable.

The potential applications for using comparative analysis in everyday business are almost unlimited. That said, a comparative analysis is most commonly used to examine

Emerging trends and opportunities (new technologies, marketing)

Competitor strategies

Financial health

Effects of trends on a target audience

Free AI content analysis generator

Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

• Why is comparative analysis so important? 

Comparative analysis can help narrow your focus so your business pursues the most meaningful opportunities rather than attempting dozens of improvements simultaneously.

A comparative approach also helps frame up data to illuminate interrelationships. For example, comparative research might reveal nuanced relationships or critical contexts behind specific processes or dependencies that wouldn’t be well-understood without the research.

For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.

• Comparative vs. competitive analysis—what’s the difference?

Comparative analysis is generally divided into three subtypes, using quantitative or qualitative data and then extending the findings to a larger group. These include

Pattern analysis —identifying patterns or recurrences of trends and behavior across large data sets.

Data filtering —analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria. 

Decision tree —flowcharting to visually map and assess potential outcomes, costs, and consequences.

In contrast, competitive analysis is a type of comparative analysis in which you deeply research one or more of your industry competitors. In this case, you’re using qualitative research to explore what the competition is up to across one or more dimensions.

For example

Service delivery —metrics like the Net Promoter Scores indicate customer satisfaction levels.

Market position — the share of the market that the competition has captured.

Brand reputation —how well-known or recognized your competitors are within their target market.

• Tips for optimizing your comparative analysis

Conduct original research

Thorough, independent research is a significant asset when doing comparative analysis. It provides evidence to support your findings and may present a perspective or angle not considered previously. 

Make analysis routine

To get the maximum benefit from comparative research, make it a regular practice, and establish a cadence you can realistically stick to. Some business areas you could plan to analyze regularly include:

Profitability

Competition

Experiment with controlled and uncontrolled variables

In addition to simply comparing and contrasting, explore how different variables might affect your outcomes.

For example, a controllable variable would be offering a seasonal feature like a shopping bot to assist in holiday shopping or raising or lowering the selling price of a product.

Uncontrollable variables include weather, changing regulations, the current political climate, or global pandemics.

Put equal effort into each point of comparison

Most people enter into comparative research with a particular idea or hypothesis already in mind to validate. For instance, you might try to prove the worthwhileness of launching a new service. So, you may be disappointed if your analysis results don’t support your plan.

However, in any comparative analysis, try to maintain an unbiased approach by spending equal time debating the merits and drawbacks of any decision. Ultimately, this will be a practical, more long-term sustainable approach for your business than focusing only on the evidence that favors pursuing your argument or strategy.

Writing a comparative analysis in five steps

To put together a coherent, insightful analysis that goes beyond a list of pros and cons or similarities and differences, try organizing the information into these five components:

1. Frame of reference

Here is where you provide context. First, what driving idea or problem is your research anchored in? Then, for added substance, cite existing research or insights from a subject matter expert, such as a thought leader in marketing, startup growth, or investment

2. Grounds for comparison Why have you chosen to examine the two things you’re analyzing instead of focusing on two entirely different things? What are you hoping to accomplish?

3. Thesis What argument or choice are you advocating for? What will be the before and after effects of going with either decision? What do you anticipate happening with and without this approach?

For example, “If we release an AI feature for our shopping cart, we will have an edge over the rest of the market before the holiday season.” The finished comparative analysis will weigh all the pros and cons of choosing to build the new expensive AI feature including variables like how “intelligent” it will be, what it “pushes” customers to use, how much it takes off the plates of customer service etc.

Ultimately, you will gauge whether building an AI feature is the right plan for your e-commerce shop.

4. Organize the scheme Typically, there are two ways to organize a comparative analysis report. First, you can discuss everything about comparison point “A” and then go into everything about aspect “B.” Or, you alternate back and forth between points “A” and “B,” sometimes referred to as point-by-point analysis.

Using the AI feature as an example again, you could cover all the pros and cons of building the AI feature, then discuss the benefits and drawbacks of building and maintaining the feature. Or you could compare and contrast each aspect of the AI feature, one at a time. For example, a side-by-side comparison of the AI feature to shopping without it, then proceeding to another point of differentiation.

5. Connect the dots Tie it all together in a way that either confirms or disproves your hypothesis.

For instance, “Building the AI bot would allow our customer service team to save 12% on returns in Q3 while offering optimizations and savings in future strategies. However, it would also increase the product development budget by 43% in both Q1 and Q2. Our budget for product development won’t increase again until series 3 of funding is reached, so despite its potential, we will hold off building the bot until funding is secured and more opportunities and benefits can be proved effective.”

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415 Research Question Examples Across 15 Disciplines

A research question is a clearly formulated query that delineates the scope and direction of an investigation. It serves as the guiding light for scholars, helping them to dissect, analyze, and comprehend complex phenomena. Beyond merely seeking answers, a well-crafted research question ensures that the exploration remains focused and goal-oriented.

The significance of framing a clear, concise, and researchable question cannot be overstated. A well-defined question not only clarifies the objective of the research but also determines the methodologies and tools a researcher will employ. A concise question ensures precision, eliminating the potential for ambiguity or misinterpretation. Furthermore, the question must be researchable—posing a question that is too broad, too subjective, or unanswerable can lead to inconclusive results or an endless loop of investigation. In essence, the foundation of any meaningful academic endeavor rests on the articulation of a compelling and achievable research question.

Research questions can be categorized based on their intent and the nature of the information they seek. Recognizing the different types is essential for crafting an effective inquiry and guiding the research process. Let's delve into the various categories:

• Descriptive Research Questions: These types of questions aim to outline and characterize specific phenomena or attributes. They seek to provide a clear picture of a situation or context without necessarily diving into causal relationships. For instance, a question like "What are the main symptoms of the flu?" is descriptive as it seeks to list the symptoms.
• Explanatory (or Causal) Research Questions: Explanatory questions delve deeper, trying to uncover the reasons or causes behind certain phenomena. They are particularly common in experimental research where researchers are attempting to establish cause-and-effect relationships. An example might be, "Does smoking increase the risk of lung cancer?"
• Exploratory Research Questions: As the name suggests, these questions are used when researchers are entering uncharted territories. They are designed to gather preliminary information on topics that haven't been studied extensively. A question like "How do emerging technologies impact remote tribal communities?" can be seen as exploratory if there's limited existing research on the topic.
• Comparative Research Questions: These questions are formulated when the objective is to compare two or more groups, conditions, or variables. Comparative questions might look like "How do test scores differ between students who study regularly and those who cram?"
• Predictive Research Questions: The goal here is to forecast or predict potential outcomes based on certain variables or conditions. Predictive research might pose questions such as "Based on current climate trends, how will average global temperatures change by 2050?"

Here are examples of research questions across various disciplines, shedding light on queries that stimulate intellectual curiosity and advancement. In this post, we will delve into disciplines ranging from the Natural Sciences, such as Physics and Biology, to the Social Sciences, including Sociology and Anthropology, as well as the Humanities, like Literature and Philosophy. We'll also explore questions from fields as varied as Health Sciences, Engineering, Business, Environmental Sciences, Mathematics, Education, Law, Agriculture, Arts, Computer Science, Architecture, and Languages. This comprehensive overview aims to illustrate the breadth and depth of inquiries that shape our world of knowledge.

Agriculture and forestry examples

Architecture and planning examples, arts and design examples, business and finance examples, computer science and informatics examples, education examples, engineering and technology examples, environmental sciences examples, health sciences examples, humanities examples, languages and linguistics examples, law examples, mathematics and statistics examples, natural sciences examples, social sciences examples.

• Descriptive: What are the primary factors that influence crop yield in temperate climates?
• Explanatory: Why do certain soil types yield higher grain production than others?
• Exploratory: How might new organic farming techniques influence soil health over a decade?
• Comparative: How do the growth rates differ between genetically modified and traditional corn crops?
• Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal science

• Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?
• Explanatory: Why do certain breeds of chickens have a higher egg production rate?
• Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?
• Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?
• Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

• Descriptive: What are the most commonly farmed fish species in Southeast Asia?
• Explanatory: Why do shrimp farms have a higher disease outbreak rate compared to fish farms?
• Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?
• Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?
• Predictive: What will be the impact of ocean acidification on mollusk farming over the next three decades?
• Descriptive: What tree species dominate the temperate rainforests of North America?
• Explanatory: Why are certain tree species more resistant to pest infestations?
• Exploratory: What are the potential benefits of integrating drone technology in forest health monitoring?
• Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?
• Predictive: Given increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

• Descriptive: What are the common characteristics of plants suitable for urban vertical farming?
• Explanatory: Why do roses require specific pH levels in the soil for optimal growth?
• Exploratory: What potential methods might promote year-round vegetable farming in colder regions?
• Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?
• Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil science

• Descriptive: What are the main components of loamy soil?
• Explanatory: Why does clay-rich soil retain more water compared to sandy soil?
• Exploratory: How might biochar applications transform nutrient availability in degraded soils?
• Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?
• Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over the next 30 years?

Architectural design

• Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?
• Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?
• Exploratory: How might sustainable materials revolutionize the future of architectural design?
• Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?
• Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

• Descriptive: What are the primary components of a successful urban park design?
• Explanatory: Why do certain types of vegetation promote greater biodiversity in urban settings?
• Exploratory: What innovative techniques can be employed to restore and integrate wetlands into urban landscapes?
• Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?
• Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban planning

• Descriptive: What are the main components of a pedestrian-friendly city center?
• Explanatory: Why do certain urban layouts promote more efficient traffic flow than others?
• Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?
• Comparative: How do the air quality levels differ between cities with green belts and those without?
• Predictive: Based on increasing telecommuting trends, how will urban planning strategies adjust to potentially reduced daily commutes in the future?

Graphic design

• Descriptive: What are the prevailing typography trends in modern branding?
• Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?
• Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?
• Comparative: How do print and digital designs differ in terms of elements and principles when targeting a young adult audience?
• Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial design

• Descriptive: What characterizes the ergonomic features of leading office chairs in the market?
• Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?
• Exploratory: How might bio-inspired design influence the future of transportation vehicles?
• Comparative: How does user satisfaction differ between traditional versus modular product designs?
• Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

• Descriptive: What techniques define the most popular virtual reality (VR) experiences currently available?
• Explanatory: Why do certain sound designs enhance immersion in video games more effectively than others?
• Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?
• Comparative: How do user engagement levels differ between 2D animations and 3D animations in educational platforms?
• Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing arts

• Descriptive: What styles of dance are currently predominant in global theater productions?
• Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?
• Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?
• Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?
• Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual arts

• Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?
• Explanatory: Why have mixed media installations gained prominence in the 21st-century art scene?
• Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?
• Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?
• Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Entrepreneurship

• Descriptive: What are the main challenges faced by startups in the tech industry?
• Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?
• Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?
• Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?
• Predictive: What sectors are predicted to see the most startup growth in the next decade?
• Descriptive: What are the primary sources of external funding for large corporations?
• Explanatory: Why did the stock market experience a significant drop in Q4 2022?
• Exploratory: How might blockchain technology revolutionize the future of banking?
• Comparative: How do the financial markets in developing countries compare to those in developed countries?
• Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human resources

• Descriptive: What are the most sought-after employee benefits in the tech industry?
• Explanatory: Why is there a high turnover rate in the retail sector?
• Exploratory: How might the rise of remote work affect HR practices in the next decade?
• Comparative: How do HR practices in multinational corporations differ from those in local companies?
• Predictive: What skills will be in highest demand in the workforce by 2030?
• Descriptive: What are the core responsibilities of middle management in large manufacturing firms?
• Explanatory: Why do some management strategies fail in diverse cultural environments?
• Exploratory: How are companies adapting their management structures in response to the gig economy?
• Comparative: How does management style in Eastern companies compare with Western businesses?
• Predictive: How might artificial intelligence reshape management practices in the next decade?
• Descriptive: What are the most effective digital marketing channels for e-commerce businesses?
• Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?
• Exploratory: How might virtual reality change the landscape of product advertising?
• Comparative: How do marketing strategies differ between B2B and B2C sectors?
• Predictive: What consumer behaviors are forecasted to dominate online shopping trends in the next five years?

Operations research

• Descriptive: What are the primary optimization techniques used in supply chain management?
• Explanatory: Why do certain optimization algorithms perform better in specific industries?
• Exploratory: How can quantum computing impact the future of operations research?
• Comparative: How does operations strategy differ between service and manufacturing industries?
• Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?

Artificial intelligence

• Descriptive: What are the primary algorithms used in deep learning?
• Explanatory: Why do certain neural network architectures outperform others in image recognition tasks?
• Exploratory: How might quantum computing influence the development of AI models?
• Comparative: How do reinforcement learning methods compare to supervised learning in game playing scenarios?
• Predictive: Based on current trends, how will AI impact the job market over the next decade?

Cybersecurity

• Descriptive: What are the most common types of cyberattacks reported in 2022?
• Explanatory: Why are certain industries more vulnerable to ransomware attacks?
• Exploratory: How might advances in quantum computing challenge existing encryption methods?
• Comparative: How do open-source software vulnerabilities compare to those in proprietary systems?
• Predictive: Given emerging technologies, what types of cyber threats will likely dominate in the next five years?

Data science

• Descriptive: What are the main tools used by data scientists in large-scale data analysis?
• Explanatory: Why does algorithm X yield more accurate predictions than algorithm Y for certain datasets?
• Exploratory: How can machine learning models improve real-time data processing in IoT devices?
• Comparative: How does the performance of traditional statistical models compare to machine learning models in predicting stock prices?
• Predictive: Based on current data trends, what industries will likely benefit the most from data analytics advancements in the coming decade?

Information systems

• Descriptive: What are the core components of a modern enterprise resource planning (ERP) system?
• Explanatory: Why have cloud-based information systems seen a rapid adoption rate in recent years?
• Exploratory: How might the integration of blockchain technology revolutionize supply chain information systems?
• Comparative: How do information system strategies differ between e-commerce and brick-and-mortar retailers?
• Predictive: Given the rise of remote work, how will information systems evolve to support decentralized teams in the future?

Software engineering

• Descriptive: What are the standard practices in agile software development?
• Explanatory: Why do some software projects face significant delays despite rigorous planning?
• Exploratory: How are emerging programming languages shaping the future of software development?
• Comparative: How does the software development lifecycle in startup environments compare to that in large corporations?
• Predictive: Based on current development trends, which software platforms are forecasted to dominate market share by 2030?

Adult education

• Descriptive: What are the primary motivations behind adults seeking further education later in life?
• Explanatory: Why do some adult education programs have a higher success rate compared to others?
• Exploratory: How might online learning platforms revolutionize adult education in the next decade?
• Comparative: How do adult education methodologies differ from traditional collegiate teaching techniques?
• Predictive: Given current trends, how will the demand for adult education courses change in the upcoming years?

Curriculum studies

• Descriptive: What are the core components of a modern high school curriculum in the United States?
• Explanatory: Why have certain subjects, like financial literacy, become more emphasized in recent curriculum updates?
• Exploratory: How can interdisciplinary studies be better incorporated into traditional curricula?
• Comparative: How does the math curriculum in the US compare to that in other developed countries?
• Predictive: Based on pedagogical research, what subjects are forecasted to gain prominence in curricula over the next decade?

Educational administration

• Descriptive: What are the main responsibilities of a school principal in large urban schools?
• Explanatory: Why do some schools consistently perform better in standardized testing than others, despite similar resources?
• Exploratory: How might emerging technologies shape the administrative tasks of educational institutions in the future?
• Comparative: How does school administration differ between private and public educational institutions?
• Predictive: Given the rise of online education, how will the role of educational administrators evolve in the coming years?

Educational psychology

• Descriptive: What cognitive strategies are commonly used by students to enhance memory retention during studies?
• Explanatory: Why do certain teaching methodologies resonate better with students having specific learning styles?
• Exploratory: How can insights from behavioral psychology improve student engagement in virtual classrooms?
• Comparative: How does the motivation level of students differ between self-paced versus instructor-led courses?
• Predictive: With the increasing integration of technology in education, how will student learning behaviors change in the next decade?

Special education

• Descriptive: What interventions are commonly used to support students with autism spectrum disorders in inclusive classrooms?
• Explanatory: Why do some special education programs yield better academic outcomes for students with specific learning disabilities?
• Exploratory: How can augmented reality technologies be utilized to enhance learning for students with visual impairments?
• Comparative: How does special education support differ between urban and rural school districts?
• Predictive: Based on advancements in assistive technologies, how will the landscape of special education transform in the near future?

Aerospace engineering

• Descriptive: What are the key materials and technologies utilized in modern spacecraft design?
• Explanatory: Why are certain alloys preferred in high-temperature aerospace applications?
• Exploratory: How might advances in propulsion technologies revolutionize space travel in the next decade?
• Comparative: How do commercial aircraft designs differ from military aircraft designs in terms of aerodynamics?
• Predictive: Given current research trends, how will the efficiency of jet engines change in the upcoming years?

Biomedical engineering

• Descriptive: What are the foundational principles behind the design of modern prosthetic limbs?
• Explanatory: Why have bio-compatible materials like titanium become crucial in implantable medical devices?
• Exploratory: How can nanotechnology be leveraged to improve drug delivery systems in the future?
• Comparative: How do MRI machines differ from CT scanners in terms of their underlying technology and application?
• Predictive: Based on emerging trends, how will wearable health monitors evolve in the next decade?

Chemical engineering

• Descriptive: What processes are involved in the large-scale production of ethylene?
• Explanatory: Why is distillation the most common separation method in the petroleum industry?
• Exploratory: How might green chemistry principles transform traditional chemical manufacturing processes?
• Comparative: How does the production of biofuels compare to traditional fossil fuels in terms of yield and environmental impact?
• Predictive: Given global sustainability goals, how will the chemical industry's reliance on fossil resources shift in the future?

Civil engineering

• Descriptive: What are the primary considerations in the structural design of skyscrapers in earthquake-prone regions?
• Explanatory: Why are steel-reinforced concrete beams commonly used in bridge construction?
• Exploratory: How can smart city concepts influence the infrastructure planning of urban centers in the future?
• Comparative: How do tunneling methods differ between soft soil and hard rock terrains?
• Predictive: With the increasing threat of climate change, how will coastal infrastructure design criteria change to account for rising sea levels?

Computer engineering

• Descriptive: What are the main components of a modern central processing unit (CPU) and their functions?
• Explanatory: Why is silicon predominantly used in semiconductor manufacturing?
• Exploratory: How might quantum computing redefine the landscape of traditional computing architectures?
• Comparative: How do solid-state drives (SSDs) compare to traditional hard disk drives (HDDs) in terms of performance and longevity?
• Predictive: Given advancements in chip miniaturization, how will the form factor of consumer electronics evolve in the coming years?

Electrical engineering

• Descriptive: What are the standard stages involved in the transmission and distribution of electrical power?
• Explanatory: Why are transformers essential in the power distribution network?
• Exploratory: How can emerging smart grid technologies improve the efficiency and reliability of electrical distribution systems?
• Comparative: How do AC and DC transmission methods differ in terms of efficiency and infrastructure requirements?
• Predictive: With the rise of renewable energy sources, how will power grid management complexities change in the next decade?

Mechanical engineering

• Descriptive: What are the fundamental principles behind the operation of a four-stroke internal combustion engine?
• Explanatory: Why are certain polymers used as vibration dampeners in machinery?
• Exploratory: How might advancements in materials science impact the design of future automotive systems?
• Comparative: How do hydraulic systems compare to pneumatic systems in terms of energy efficiency and application?
• Predictive: With the push towards sustainability, how will traditional manufacturing methods evolve to reduce their carbon footprint?

Climatology

• Descriptive: What are the primary factors that influence the El Niño and La Niña phenomena?
• Explanatory: Why have certain regions experienced more intense and frequent heatwaves in the past decade?
• Exploratory: How might changing atmospheric CO2 concentrations impact global wind patterns in the future?
• Comparative: How do urban areas differ from rural areas in terms of microclimate conditions?
• Predictive: Given current greenhouse gas emission trends, what will be the average global temperature increase by the end of the century?

Conservation science

• Descriptive: What are the primary threats faced by tropical rainforests around the world?
• Explanatory: Why are certain species more vulnerable to habitat fragmentation than others?
• Exploratory: How can community involvement enhance conservation efforts in protected areas?
• Comparative: How does the effectiveness of in-situ conservation compare to ex-situ conservation for endangered species?
• Predictive: If current deforestation rates continue, how many species are predicted to go extinct in the next 50 years?
• Descriptive: What are the dominant flora and fauna in a temperate deciduous forest biome?
• Explanatory: Why do certain ecosystems, like wetlands, have higher biodiversity than others?
• Exploratory: How might the spread of invasive species alter nutrient cycling in freshwater lakes?
• Comparative: How do the trophic dynamics of grassland ecosystems differ from those of desert ecosystems?
• Predictive: How will global ecosystems change if bee populations continue to decline at current rates?

Environmental health

• Descriptive: What are the major pollutants found in urban air?
• Explanatory: Why do certain pollutants cause respiratory diseases in humans?
• Exploratory: How might green building designs reduce the health risks associated with indoor air pollutants?
• Comparative: How do the health impacts of living near coal-fired power plants compare to living near nuclear power plants?
• Predictive: Given increasing urbanization trends, how will air quality in major cities change over the next two decades?

Marine biology

• Descriptive: What are the primary species that comprise a coral reef ecosystem?
• Explanatory: Why are coral reefs particularly sensitive to changes in sea temperature?
• Exploratory: How might deep-sea exploration reveal unknown marine species and their adaptations?
• Comparative: How do the feeding strategies of pelagic fish differ from benthic fish in oceanic ecosystems?
• Predictive: If ocean acidification trends continue, what will be the impact on shell-forming marine organisms in the next 30 years?
• Descriptive: What are the most common oral health issues faced by elderly individuals?
• Explanatory: Why do sugary foods lead to a higher prevalence of cavities?
• Exploratory: How might emerging technologies revolutionize dental procedures in the coming decade?
• Comparative: How do the effects of electric toothbrushes compare to manual ones in reducing plaque?
• Predictive: Given current trends, how might the prevalence of gum diseases change in populations with increased sugar consumption over the next decade?

Kinesiology

• Descriptive: What are the primary physiological changes that occur during aerobic exercise?
• Explanatory: Why do some athletes experience muscle cramps during extensive physical activity?
• Exploratory: How might different stretching routines impact athletic performance?
• Comparative: How do the biomechanics of running on a treadmill differ from running outdoors?
• Predictive: If sedentary lifestyles continue to rise, what could be the potential impact on musculoskeletal health in the next 20 years?
• Descriptive: What are the main symptoms associated with the early stages of Parkinson's disease?
• Explanatory: Why are some viruses, like the flu, more prevalent in colder months?
• Exploratory: How might genetic editing technologies, like CRISPR, be utilized to treat hereditary diseases in the future?
• Comparative: How does the efficacy of traditional chemotherapy compare to targeted therapy in treating certain cancers?
• Predictive: Given advances in telemedicine, how might patient-doctor interactions evolve over the next decade?
• Descriptive: What are the primary responsibilities of nurses in intensive care units?
• Explanatory: Why is there a higher burnout rate among nurses compared to other healthcare professionals?
• Exploratory: How can training programs be improved to better equip nurses for challenges in emergency situations?
• Comparative: How does the patient recovery rate differ when cared for by specialized nurses versus general ward nurses?
• Predictive: How will the role of nurses change with the integration of more AI-based diagnostic tools in hospitals?
• Descriptive: What are the main nutritional components of a Mediterranean diet?
• Explanatory: Why does a diet high in processed sugars lead to increased risks of type 2 diabetes?
• Exploratory: How might gut microbiota be influenced by various diets and what are the potential health implications?
• Comparative: How does the nutritional profile of plant-based proteins compare to animal-based proteins?
• Predictive: If global meat consumption trends continue, what could be the implications for population-wide nutritional health in 30 years?
• Descriptive: What are the primary active ingredients in over-the-counter pain relievers?
• Explanatory: Why do certain medications cause drowsiness as a side effect?
• Exploratory: How might nanoparticle-based drug delivery systems enhance the efficacy of certain treatments?
• Comparative: How do the effects of generic drugs compare to their brand-name counterparts?
• Predictive: Given the rise of antibiotic-resistant bacteria, how might pharmaceutical approaches to bacterial infections change in the future?

Public health

• Descriptive: What are the main factors contributing to public health disparities in urban vs rural areas?
• Explanatory: Why did certain regions have higher transmission rates during the COVID-19 pandemic?
• Exploratory: How can community engagement strategies be optimized for more effective health campaigns?
• Comparative: How do vaccination rates and outcomes differ between countries with public vs private healthcare systems?
• Predictive: Based on current trends, how will global public health challenges evolve over the next 50 years?

Art history

• Descriptive: What are the primary artistic styles observed in the Renaissance era?
• Explanatory: Why did the Baroque art movement emerge after the Renaissance?
• Exploratory: How might newly discovered ancient art pieces reshape our understanding of prehistoric artistic practices?
• Comparative: How does European Romantic art differ from Asian Romantic art of the same period?
• Predictive: Given current trends, how might digital art impact traditional art gallery setups in the next decade?
• Descriptive: What are the primary themes in Homer's "Odyssey"?
• Explanatory: Why did Greek tragedies place a strong emphasis on the concept of fate?
• Exploratory: Are there undiscovered works that might provide more insight into daily life in ancient Rome?
• Comparative: How do Roman epics compare to their Greek counterparts in terms of character development?
• Predictive: How will emerging technologies like virtual reality affect the study of ancient ruins?

Cultural studies

• Descriptive: How is the concept of family portrayed in contemporary American media?
• Explanatory: Why has the influence of Western culture grown in certain Eastern countries over the last century?
• Exploratory: What are the emerging subcultures in the digital age and how do they communicate?
• Comparative: How does the representation of masculinity vary between Eastern and Western films?
• Predictive: In what ways might globalization affect cultural identities in the next two decades?
• Descriptive: What events led to the fall of the Berlin Wall?
• Explanatory: Why did the Industrial Revolution begin in Britain?
• Exploratory: Are there undocumented civilizational interactions in ancient times that new archaeological findings might reveal?
• Comparative: How did the responses to the Black Plague differ between European and Asian nations?
• Predictive: Given historical patterns, how might major global powers react to dwindling natural resources in the future?
• Descriptive: What are the main narrative techniques used in James Joyce's "Ulysses"?
• Explanatory: Why did the Gothic novel become popular in 19th-century England?
• Exploratory: How might translations of ancient texts reveal different interpretations based on the translator's cultural background?
• Comparative: How does the portrayal of war differ between post-WWII American and French literature?
• Predictive: How might the rise of AI-authored literature change the publishing industry?
• Descriptive: What are the core principles of existentialism as described by Jean-Paul Sartre?
• Explanatory: Why did the philosophy of existentialism gain prominence post-WWII?
• Exploratory: How might ancient Eastern philosophies provide insights into modern ethical dilemmas surrounding technology?
• Comparative: How does Nietzsche's concept of the "Ubermensch" compare to Aristotle's "virtuous person"?
• Predictive: As AI becomes more prevalent, how might philosophical discussions around consciousness evolve?

Religious studies

• Descriptive: What are the Five Pillars of Islam?
• Explanatory: Why did Protestantism emerge within Christianity during the 16th century?
• Exploratory: Are there common motifs in creation myths across various religions?
• Comparative: How do concepts of the afterlife compare between Christianity, Buddhism, and Ancient Egyptian beliefs?
• Predictive: How might interfaith dialogue shape religious practices in multi-faith societies over the next decade?

Classic languages

• Descriptive: What are the primary grammatical structures in Ancient Greek?
• Explanatory: Why did Latin play a foundational role in the development of many modern European languages?
• Exploratory: Are there yet-to-be-deciphered scripts from ancient civilizations that might provide insight into lost languages?
• Comparative: How do the verb conjugation patterns in Latin compare to those in Sanskrit?
• Predictive: Given the ongoing research in classical studies, how might our understanding of certain ancient texts change in the next decade?

Comparative literature

• Descriptive: What are the main themes in Japanese Haiku and English Sonnets?
• Explanatory: Why do certain folklore tales appear with variations across different cultures?
• Exploratory: How might newly translated works from lesser-known languages reshape the world literature canon?
• Comparative: How does the role of the tragic hero in French literature differ from its portrayal in Russian literature?
• Predictive: As global communication becomes more interconnected, how might the study of world literature evolve in universities?

Modern languages

• Descriptive: What are the primary tonal patterns observed in Mandarin Chinese?
• Explanatory: Why has English become a dominant lingua franca in international business and diplomacy?
• Exploratory: Which lesser-studied languages might become more prominent due to socio-political changes in their regions?
• Comparative: How do the grammatical complexities of Russian compare to those of German?
• Predictive: Given current global trends, which languages are predicted to become more widely spoken in the next two decades?
• Descriptive: What are the primary articulatory features of plosive sounds?
• Explanatory: Why do certain accents develop specific pitch fluctuations and intonations?
• Exploratory: How do various environmental factors affect vocal cord vibrations and sound production?
• Comparative: How does the pronunciation of fricatives differ between Spanish and Portuguese speakers?
• Predictive: How might advancements in voice recognition technology influence phonetics research in the next decade?
• Descriptive: What are the primary signs and symbols used in American road signage?
• Explanatory: Why do red roses universally symbolize love or passion in many cultures?
• Exploratory: Are there emerging symbols in digital communication that could become universally recognized signs in the future?
• Comparative: How do the semiotic structures in print advertisements differ between Western and Eastern cultures?
• Predictive: As emoji usage becomes more widespread, how might they impact written language semantics in the coming years?
• Descriptive: What are the key statutes governing tenant rights in residential leases?
• Explanatory: Why do personal injury claims vary significantly in settlement amounts even under similar circumstances?
• Exploratory: How might alternative dispute resolution mechanisms evolve in civil law contexts over the next decade?
• Comparative: How do defamation laws differ between jurisdictions that adopt the British common law system versus the Napoleonic code?
• Predictive: How might the rise of online transactions affect the volume and nature of civil law cases related to contract disputes?

Constitutional law

• Descriptive: What are the main principles enshrined in the First Amendment of the U.S. Constitution?
• Explanatory: Why have some constitutional rights been subject to varying interpretations over time?
• Exploratory: Are there emerging debates around digital rights and freedoms that might reshape constitutional interpretations in the future?
• Comparative: How does the protection of freedom of speech differ between the U.S. Constitution and the German Basic Law?
• Predictive: Given global socio-political trends, how might constitutional democracies adjust their foundational texts in the next two decades?

Corporate law

• Descriptive: What are the primary duties and liabilities of a board of directors in a publicly traded company?
• Explanatory: Why do mergers and acquisitions often involve extensive due diligence processes?
• Exploratory: How might the rise of digital currencies impact the regulatory landscape for corporations in the finance sector?
• Comparative: How does the legal framework for shareholder rights in the U.S. compare to that of Japan?
• Predictive: How might changing global trade dynamics influence corporate structuring and international partnerships?

Criminal law

• Descriptive: What constitutes first-degree murder in the majority of jurisdictions?
• Explanatory: Why are certain offenses classified as misdemeanors while others are felonies?
• Exploratory: Are there emerging patterns in cybercrime that suggest new areas of legal vulnerability?
• Comparative: How does the treatment of juvenile offenders differ between Scandinavian countries and the U.S.?
• Predictive: Given advancements in technology, how might criminal law evolve to address potential misuses of artificial intelligence?

International law

• Descriptive: What are the foundational principles of the Geneva Conventions?
• Explanatory: Why have some nations refused to recognize or be bound by certain international treaties?
• Exploratory: How might global climate change reshape international agreements and treaties in the coming years?
• Comparative: How do regional trade agreements in Africa compare to those in Southeast Asia in terms of provisions and enforcement mechanisms?
• Predictive: How might geopolitical shifts influence the role and effectiveness of international courts in resolving state disputes?

Applied mathematics

• Descriptive: What are the primary mathematical models used to predict the spread of infectious diseases?
• Explanatory: Why does the Navier–Stokes equation play a pivotal role in fluid dynamics?
• Exploratory: How might new computational methods enhance the efficiency of existing algorithms in applied mathematics?
• Comparative: How do optimization techniques in operations research differ from those in machine learning applications?
• Predictive: Given the rapid growth of quantum computing, how might it reshape the landscape of applied mathematical problems in the next decade?

Applied statistics

• Descriptive: What are the standard procedures for handling missing data in a large-scale survey?
• Explanatory: Why do statisticians use bootstrapping techniques in hypothesis testing?
• Exploratory: How might emerging data sources, like wearables and IoT devices, introduce new challenges and opportunities in applied statistics?
• Comparative: How does the performance of Bayesian methods compare to frequentist methods in complex hierarchical models?
• Predictive: With the increasing availability of big data, how might the role of applied statisticians evolve in the next five years?

Pure mathematics

• Descriptive: What are the axioms underpinning Euclidean geometry?
• Explanatory: Why is Gödel's incompleteness theorem considered a foundational result in the philosophy of mathematics?
• Exploratory: Are there newly emerging areas of study within number theory due to advancements in computational mathematics?
• Comparative: How do algebraic structures differ between rings and fields?
• Predictive: Considering current research trends, what areas of pure mathematics are poised for significant breakthroughs in the next decade?

Theoretical statistics

• Descriptive: What foundational principles underlie the Central Limit Theorem?
• Explanatory: Why is the concept of sufficiency crucial in the design of statistical tests?
• Exploratory: How might advances in artificial intelligence influence theoretical developments in statistical inference?
• Comparative: How do likelihood-based inference methods compare to Bayesian methods in terms of theoretical underpinnings?
• Predictive: As data generation mechanisms evolve, how might the theoretical foundations of statistics need to adapt in the future?
• Descriptive: What are the key features and behaviors of black holes?
• Explanatory: Why does the expansion of the universe appear to be accelerating?
• Exploratory: What potential insights might the study of exoplanets provide about the conditions necessary for life?
• Comparative: How do the properties of spiral galaxies differ from those of elliptical galaxies?
• Predictive: Based on current data, what are the projected future behaviors of our sun as it ages?
• Descriptive: What are the primary functions and structures of ribosomes in a cell?
• Explanatory: Why does DNA replication occur semi-conservatively?
• Exploratory: How might emerging technologies like CRISPR redefine our understanding of genetic engineering?
• Comparative: How do the metabolic processes of prokaryotic cells differ from those of eukaryotic cells?
• Predictive: Given the current trajectory of climate change, how might the biodiversity in tropical rainforests be affected over the next century?
• Descriptive: What are the key properties and uses of the noble gases?
• Explanatory: Why do exothermic reactions release heat?
• Exploratory: How might advances in nanochemistry influence drug delivery systems?
• Comparative: How do ionic bonds differ in strength and characteristics from covalent bonds?
• Predictive: Considering the rise in antibiotic-resistant bacteria, how might the field of medicinal chemistry adapt to produce effective treatments in the future?

Earth science

• Descriptive: What are the primary layers of Earth's atmosphere and their respective characteristics?
• Explanatory: Why do certain regions experience more seismic activity than others?
• Exploratory: How might the study of ancient ice cores provide insights into past climate conditions?
• Comparative: How do the processes of weathering differ between arid and humid climates?
• Predictive: Given current data on deforestation, what could be its impact on global soil quality and erosion patterns over the next 50 years?
• Descriptive: What are the fundamental principles underlying quantum mechanics?
• Explanatory: Why does the speed of light in a vacuum remain constant regardless of the observer's frame of reference?
• Exploratory: How might studies in string theory reshape our understanding of the universe at the smallest scales?
• Comparative: How do the effects of general relativity contrast with predictions from Newtonian physics under extreme gravitational conditions?
• Predictive: With advancements in particle physics, what potential new particles or phenomena might be discovered in the next decade?

Anthropology

• Descriptive: What are the primary rituals and customs of the indigenous tribes of the Amazon?
• Explanatory: Why did the ancient Mayan civilization collapse?
• Exploratory: How might modern urbanization impact the preservation of ancient burial sites?
• Comparative: How do hunter-gatherer societies differ from agricultural societies in terms of social structures?
• Predictive: Given global trends, how might indigenous cultures evolve over the next century?

Communication

• Descriptive: What are the main modes of communication used by millennials compared to baby boomers?
• Explanatory: Why has the usage of social media platforms surged in the last two decades?
• Exploratory: How might advancements in virtual reality reshape interpersonal communication in the future?
• Comparative: How do written communication skills differ between those educated in traditional schools versus online schools?
• Predictive: How might the nature of journalism change with the rise of automated content generation?
• Descriptive: What are the primary components of a nation's gross domestic product (GDP)?
• Explanatory: Why did the economic recession of 2008 occur?
• Exploratory: How might the concept of universal basic income impact labor market dynamics?
• Comparative: How do free market economies differ from command economies in terms of resource allocation?
• Predictive: Based on current global economic trends, which industries are predicted to boom in the next decade?
• Descriptive: What are the geographical features of the Himalayan mountain range?
• Explanatory: Why do desert regions exist on the western coasts of continents, such as the Atacama in South America?
• Exploratory: How might rising sea levels reshape the world's coastlines over the next century?
• Comparative: How does urban planning in European cities differ from that in American cities?
• Predictive: Given current urbanization rates, which cities are poised to become megacities by 2050?

Political science

• Descriptive: What are the foundational principles of a parliamentary democracy?
• Explanatory: Why do certain nations adopt federal systems while others prefer unitary systems?
• Exploratory: How might the rise of populism influence global diplomatic relations in the 21st century?
• Comparative: How do the rights of citizens in liberal democracies differ from those in authoritarian regimes?
• Predictive: Based on current political trends, which nations might see significant shifts in governance models over the next two decades?
• Descriptive: What are the primary stages of cognitive development in children according to Piaget?
• Explanatory: Why do certain individuals develop phobias?
• Exploratory: How might emerging neuroscientific tools, like fMRI, alter our understanding of human emotions?
• Comparative: How do coping mechanisms differ between individuals with high resilience versus those with low resilience?
• Predictive: Given the rise in digital communication, how might human attention spans evolve in future generations?

Social work

• Descriptive: What are the core principles and practices in child protective services?
• Explanatory: Why do certain communities have higher rates of child neglect and abuse?
• Exploratory: How might the integration of artificial intelligence in social work affect decision-making in child welfare cases?
• Comparative: How do intervention strategies for substance abuse differ between urban and rural settings?
• Predictive: Based on current societal trends, what challenges might social workers face in the next decade?
• Descriptive: What are the defining characteristics of Generation Z as a social cohort?
• Explanatory: Why have nuclear families become less prevalent in Western societies?
• Exploratory: How might the widespread adoption of virtual realities impact social interactions and community structures in the future?
• Comparative: How do the roles and perceptions of elderly individuals differ between Eastern and Western societies?
• Predictive: Given the rise in remote work, how might urban and suburban living patterns change over the next three decades?

In synthesizing the vast range of research questions posed across diverse disciplines, it becomes clear that every academic field, from the humanities to the social sciences, offers unique perspectives and methodologies to uncover and understand various facets of our world. These questions, whether descriptive, explanatory, exploratory, comparative, or predictive, serve as guiding lights, driving scholarship and innovation. As academia continues to evolve and adapt, these inquiries not only define the boundaries of current knowledge but also pave the way for future discoveries and insights, emphasizing the invaluable role of continuous inquiry in the ever-evolving tapestry of human understanding.

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Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Comparative Research

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Although not everyone would agree, comparing is not always bad. Comparing things can also give you a handful of benefits. For instance, there are times in our life where we feel lost. You may not be getting the job that you want or have the sexy body that you have been aiming for a long time now. Then, you happen to cross path with an old friend of yours, who happened to get the job that you always wanted. This scenario may put your self-esteem down, knowing that this friend got what you want, while you didn’t. Or you can choose to look at your friend as an example that your desire is actually attainable. Come up with a plan to achieve your  personal development goal . Perhaps, ask for tips from this person or from the people who inspire you. According to the article posted in  brit.co , licensed master social worker and therapist Kimberly Hershenson said that comparing yourself to someone successful can be an excellent self-motivation to work on your goals.

Aside from self-improvement, as a researcher, you should know that comparison is an essential method in scientific studies, such as experimental research and descriptive research . Through this method, you can uncover the relationship between two or more variables of your project in the form of comparative analysis .

What is Comparative Research?

Aiming to compare two or more variables of an experiment project, experts usually apply comparative research examples in social sciences to compare countries and cultures across a particular area or the entire world. Despite its proven effectiveness, you should keep it in mind that some states have different disciplines in sharing data. Thus, it would help if you consider the affecting factors in gathering specific information.

Quantitative and Qualitative Research Methods in Comparative Studies

In comparing variables, the statistical and mathematical data collection, and analysis that quantitative research methodology naturally uses to uncover the correlational connection of the variables, can be essential. Additionally, since quantitative research requires a specific research question, this method can help you can quickly come up with one particular comparative research question.

The goal of comparative research is drawing a solution out of the similarities and differences between the focused variables. Through non-experimental or qualitative research , you can include this type of research method in your comparative research design.

13+ Comparative Research Examples

Know more about comparative research by going over the following examples. You can download these zipped documents in PDF and MS Word formats.

1. Comparative Research Report Template

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2. Business Comparative Research Template

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3. Comparative Market Research Template

Size: 172 KB

4. Comparative Research Strategies Example

5. Comparative Research in Anthropology Example

Size: 192 KB

6. Sample Comparative Research Example

Size: 516 KB

7. Comparative Area Research Example

8. Comparative Research on Women’s Emplyment Example

Size: 290 KB

9. Basic Comparative Research Example

Size: 19 KB

10. Comparative Research in Medical Treatments Example

11. Comparative Research in Education Example

Size: 455 KB

12. Formal Comparative Research Example

Size: 244 KB

13. Comparative Research Designs Example

Size: 259 KB

14. Casual Comparative Research in DOC

Best Practices in Writing an Essay for Comparative Research in Visual Arts

If you are going to write an essay for a comparative research examples paper, this section is for you. You must know that there are inevitable mistakes that students do in essay writing . To avoid those mistakes, follow the following pointers.

1. Compare the Artworks Not the Artists

One of the mistakes that students do when writing a comparative essay is comparing the artists instead of artworks. Unless your instructor asked you to write a biographical essay, focus your writing on the works of the artists that you choose.

2. Consult to Your Instructor

There is broad coverage of information that you can find on the internet for your project. Some students, however, prefer choosing the images randomly. In doing so, you may not create a successful comparative study. Therefore, we recommend you to discuss your selections with your teacher.

3. Avoid Redundancy

It is common for the students to repeat the ideas that they have listed in the comparison part. Keep it in mind that the spaces for this activity have limitations. Thus, it is crucial to reserve each space for more thoroughly debated ideas.

4. Be Minimal

Unless instructed, it would be practical if you only include a few items(artworks). In this way, you can focus on developing well-argued information for your study.

5. Master the Assessment Method and the Goals of the Project

We get it. You are doing this project because your instructor told you so. However, you can make your study more valuable by understanding the goals of doing the project. Know how you can apply this new learning. You should also know the criteria that your teachers use to assess your output. It will give you a chance to maximize the grade that you can get from this project.

Comparing things is one way to know what to improve in various aspects. Whether you are aiming to attain a personal goal or attempting to find a solution to a certain task, you can accomplish it by knowing how to conduct a comparative study. Use this content as a tool to expand your knowledge about this research methodology .

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Comparative Analysis

• First Online: 01 January 2022

Cite this chapter

• Sergey K. Aityan 2  

Part of the book series: Classroom Companion: Business ((CCB))

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Comparative research is essential for making right decisions in business. Decisions are always associated with the comparison and analysis of choices. Each choice, typically, presents multiple features for comparison and analysis depending on the goals, purpose, scope, priorities, resources, capabilities, constraints, available information, and many other factors and conditions.

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About this chapter

Aityan, S.K. (2022). Comparative Analysis. In: Business Research Methodology. Classroom Companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-030-76857-7_18

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Causal Comparative Research: Definition, Types & Benefits

Within the field of research, there are multiple methodologies and ways to find answers to your needs, in this article we will address everything you need to know about Causal Comparative Research, a methodology with many advantages and applications.

What Is Causal Comparative Research?

Causal-comparative research is a methodology used to identify cause-effect relationships between independent and dependent variables.

Researchers can study cause and effect in retrospect. This can help determine the consequences or causes of differences already existing among or between different groups of people.

When you think of Casual Comparative Research, it will almost always consist of the following:

• A method or set of methods to identify cause/effect relationships
• A set of individuals (or entities) that are NOT selected randomly – they were intended to participate in this specific study
• Variables are represented in two or more groups (cannot be less than two, otherwise there is no differentiation between them)
• Non-manipulated independent variables – *typically, it’s a suggested relationship (since we can’t control the independent variable completely)

Types of Casual Comparative Research

Casual Comparative Research is broken down into two types:

• Retrospective Comparative Research
• Prospective Comparative Research

Retrospective Comparative Research: Involves investigating a particular question…. after the effects have occurred. As an attempt to see if a specific variable does influence another variable.

Prospective Comparative Research: This type of Casual Comparative Research is characterized by being initiated by the researcher and starting with the causes and determined to analyze the effects of a given condition. This type of investigation is much less common than the Retrospective type of investigation.

LEARN ABOUT: Quasi-experimental Research

Causal Comparative Research vs Correlation Research

The universal rule of statistics… correlation is NOT causation! 

Casual Comparative Research does not rely on relationships. Instead, they’re comparing two groups to find out whether the independent variable affected the outcome of the dependent variable

When running a Causal Comparative Research, none of the variables can be influenced, and a cause-effect relationship has to be established with a persuasive, logical argument; otherwise, it’s a correlation.

Another significant difference between both methodologies is their analysis of the data collected. In the case of Causal Comparative Research, the results are usually analyzed using cross-break tables and comparing the averages obtained. At the same time, in Causal Comparative Research, Correlation Analysis typically uses scatter charts and correlation coefficients.

Advantages and Disadvantages of Causal Comparative Research

Like any research methodology, causal comparative research has a specific use and limitations to consider when considering them in your next project. Below we list some of the main advantages and disadvantages.

• It is more efficient since it allows you to save human and economic resources and to do it relatively quickly.
• Identifying causes of certain occurrences (or non-occurrences)
• Thus, descriptive analysis rather than experimental

Disadvantages

• You’re not fully able to manipulate/control an independent variable as well as the lack of randomization
• Like other methodologies, it tends to be prone to some research bias , the most common type of research is subject- selection bias , so special care must be taken to avoid it so as not to compromise the validity of this type of research.
• The loss of subjects/location influences / poor attitude of subjects/testing threats….are always a possibility

Finally, it is important to remember that the results of this type of causal research should be interpreted with caution since a common mistake is to think that although there is a relationship between the two variables analyzed, this does not necessarily guarantee that the variable influences or is the main factor to influence in the second variable.

LEARN ABOUT: ANOVA testing

QuestionPro can be your ally in your next Causal Comparative Research

QuestionPro is one of the platforms most used by the world’s leading research agencies, thanks to its diverse functions and versatility when collecting and analyzing data.

With QuestionPro you will not only be able to collect the necessary data to carry out your causal comparative research, you will also have access to a series of advanced reports and analyses to obtain valuable insights for your research project.

We invite you to learn more about our Research Suite, schedule a free demo of our main features today, and clarify all your doubts about our solutions.

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Methods in Comparative Effectiveness Research

Katrina armstrong.

From the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Comparative effectiveness research (CER) seeks to assist consumers, clinicians, purchasers, and policy makers to make informed decisions to improve health care at both the individual and population levels. CER includes evidence generation and evidence synthesis. Randomized controlled trials are central to CER because of the lack of selection bias, with the recent development of adaptive and pragmatic trials increasing their relevance to real-world decision making. Observational studies comprise a growing proportion of CER because of their efficiency, generalizability to clinical practice, and ability to examine differences in effectiveness across patient subgroups. Concerns about selection bias in observational studies can be mitigated by measuring potential confounders and analytic approaches, including multivariable regression, propensity score analysis, and instrumental variable analysis. Evidence synthesis methods include systematic reviews and decision models. Systematic reviews are a major component of evidence-based medicine and can be adapted to CER by broadening the types of studies included and examining the full range of benefits and harms of alternative interventions. Decision models are particularly suited to CER, because they make quantitative estimates of expected outcomes based on data from a range of sources. These estimates can be tailored to patient characteristics and can include economic outcomes to assess cost effectiveness. The choice of method for CER is driven by the relative weight placed on concerns about selection bias and generalizability, as well as pragmatic concerns related to data availability and timing. Value of information methods can identify priority areas for investigation and inform research methods.

INTRODUCTION

The desire to determine the best treatment for a patient is as old as the medical field itself. However, the methods used to make this determination have changed substantially over time, progressing from the humoral model of disease through the Oslerian application of clinical observation to the paradigm of experimental, evidence-based medicine of the last 40 years. Most recently, the field of comparative effectiveness research (CER) has taken center stage 1 in this arena, driven, at least in part, by the belief that better information about which treatment a patient should receive is part of the answer to addressing the unsustainable growth in health care costs in the United States. 2 , 3

The emergence of CER has galvanized a re-examination of clinical effectiveness research methods, both among researchers and policy organizations. New definitions have been created that emphasize the necessity of answering real-world questions, where patients and their clinicians have to pick from a range of possible options, recognizing that the best choice may vary across patients, settings, and even time periods. 4 The long-standing emphasis on double-blinded, randomized controlled trials (RCTs) is increasingly seen as impractical and irrelevant to many of the questions facing clinicians and policy makers today. The importance of generating information that will “assist consumers, clinicians, purchasers, and policy makers to make informed decisions” 1 (p29) is certainly not a new tenet of clinical effectiveness research, but its primacy in CER definitions has important implications for research methods in this area.

CER encompasses both evidence generation and evidence synthesis. 5 Generation of comparative effectiveness evidence uses experimental and observational methods. Synthesis of evidence uses systematic reviews and decision and cost-effectiveness modeling. Across these methods, CER examines a broad range of interventions to “prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care.” 1 (p29)

EXPERIMENTAL METHODS

RCTs became the gold standard for clinical effectiveness research soon after publication of the first RCT in 1948. 6 An RCT compares outcomes across groups of participants who are randomly assigned to different interventions, often including a placebo or control arm ( Fig 1 ). RCTs are widely revered for their ability to address selection bias, the correlation between the type of intervention received and other factors associated with the outcome of interest. RCTs are fundamental to the evaluation of new therapeutic agents that are not available outside of a trial setting, and phase III RCT evidence is required for US Food and Drug Administration approval. RCTs are also important for evaluating new technology, including imaging and devices. Increasingly, RCTs are also used to shed light on biology through correlative mechanistic studies, particularly in oncology.

Experimental and observational study designs. In a randomized controlled trial, a population of interest is screened for eligibility, randomly assigned to alternative interventions, and observed for outcomes of interest. In an observational study, the population of interest is assigned to alternative interventions based on patient, provider, and system factors and observed for outcomes of interest.

However, traditional approaches to RCTs are increasingly seen as impractical and irrelevant to many of the questions facing clinicians and policy makers today. RCTs have long been recognized as having important limitations in real-world decision making, 7 including: one, RCTs often have restrictive enrollment criteria so that the participants do not resemble patients in practice, particularly in clinical characteristics such as comorbidity, age, and medications or in sociodemographic characteristics such as race, ethnicity, and socioeconomic status; two, RCTs are often not feasible, either because of expense, ethical concerns, or patient acceptance; and three, given their expense and enrollment restrictions, RCTs are rarely able to answer questions about how the effect of the intervention may vary across patients or settings.

Despite these limitations, there is little doubt that RCTs will be a major component of CER. 8 Furthermore, their role is likely to grow with new approaches that increase their relevance in clinical practice. 9 Adaptive trials use accumulating evidence from the trials to modify trial design of the trial to increase efficiency and the probability that trial participants benefit from participation. 10 These adaptations can include changing the end of the trial, changing the interventions or intervention doses, changing the accrual rate, or changing the probability of being randomly assigned to the different arms. One example of an adaptive clinical trial in oncology is the multiarm I-Spy2 trial, which is evaluating multiple agents for neoadjuvant breast cancer treatment. 11 The I-Spy2 trial uses an adaptive approach to assigning patients to treatment arms (where patients with a tumor profile are more likely to be assigned to the arm with the best outcomes for that profile), and data safety monitoring board decisions are guided by Bayesian predicted probabilities of pathologic complete response. 12 , 13 Other examples of adaptive clinical trials in oncology include a randomized trial of four regiments in metastatic prostate cancer, where patients who did not respond to their initial regimen (selected based on randomization) were then randomly assigned to the remaining three regimens, 14 and the CALGB (Cancer and Leukemia Group B) 49907 trial, which used Bayesian predictive probabilities of inferiority to determine the final sample size needed for the comparison of capecitabine and standard chemotherapy in elderly women with early-stage breast cancer. 15 Pragmatic trials relax some of the traditional rules of RCTs to maximize the relevance of the results for clinicians and policy makers. These changes may include expansion of eligibility criteria, flexibility in the application of the intervention and in the management of the control group, and reduction in the intensity of follow-up or procedures for assessing outcomes. 16

OBSERVATIONAL METHODS

The emergence of comparative effectiveness has led to a renewed interest in the role of observational studies for assessing the benefits and harms of alternative interventions. Observational studies compare outcomes between patients who receive different interventions through some process other than investigator randomization. Most commonly, this process is the natural variation in clinical care, although observational studies also can take advantage of natural experiments, where higher-level changes in care delivery (eg, changes in state policy or changes in hospital unit structure) lead to changes in intervention exposure between groups. Observational studies can enroll patients by exposure (eg, type of intervention) using a cohort design or outcome using a case-control design. Cohort studies can be performed prospectively, where participants are recruited at the time of exposure, or retrospectively, where the exposure occurred before participants are identified.

The strengths and limitations of observational studies for clinical effectiveness research have been debated for decades. 7 , 17 Because the incremental cost of including an additional participant is generally low, observational studies often have relatively large numbers of participants who are more representative of the general population. Large, diverse study populations make the results more generalizable to real-world practice and enable the examination of variation in effect across patient subgroups. This advantage is particularly important for understanding effectiveness among vulnerable populations, such as racial minorities, who are often underrepresented in RCT participants. Observational studies that take advantage of existing data sets are able to provide results quickly and efficiently, a critical need for most CER. Currently, observational data already play an important role in influencing guidelines in many areas of oncology, particularly around prevention (eg, nutritional guidelines, management of BRCA1/2 mutation carriers) 18 , 19 and the use of diagnostic tests (eg, use of gene expression profiling in women with node-negative, estrogen receptor–positive breast cancer). 20 However, observational studies also have important limitations. Observational studies are only feasible if the intervention of interest is already being used in clinical practice; they are not possible for evaluation of new drugs or devices. Observational studies are subject to bias, including performance bias, detection bias, and selection bias. 17 , 21 Performance bias occurs when the delivery of one type of intervention is associated with generally higher levels of performance by the health care unit (ie, health care quality) than the delivery of a different type of intervention, making it difficult to determine if better outcomes are the result of the intervention or the accompanying higher-quality health care. Detection bias occurs when the outcomes of interest are more easily detected in one group than another, generally because of differential contact with the health care system between groups. Selection bias is the most important concern in the validity of observational studies and occurs when intervention groups differ in characteristics that are associated with the outcome of interest. These differences can occur because a characteristic is part of the decision about which treatment to recommend (ie, disease severity), which is often termed confounding by indication, or because it is correlated with both intervention and outcome for another reason. A particular concern for CER of therapies is that some new agents may be more likely to be used in patients for whom established therapies have failed and who are less likely to be responsive to any therapy.

There are two main approaches for addressing bias in observational studies. First, important potential confounders must be identified and included in the data collection. Measured confounders can be addressed through multivariate and propensity score analysis. A telling example of the importance of adequate assessment of potential confounders was found through examination of the observational studies of hormone replacement therapy (HRT) and coronary heart disease (CHD). Meta-analyses of observational studies had long estimated a substantial reduction in CHD risk with the use of postmenopausal HRT. However, the WHI (Women's Health Initiative) trial, a large, double-blind RCT of postmenopausal HRT, found no difference in CHD risk between women assigned to HRT or placebo. Although this apparent contradiction is often used as general evidence against the validity of observational studies, a re-examination of the observational studies demonstrated that studies that adjusted for measures of socioeconomic status (a clear confounder between HRT use and better health outcomes) had results similar to those of the WHI, whereas studies that did not adjust for socioeconomic status found a protective effect with HRT 22 ( Fig 2 ). The use of administrative data sets for observational studies of comparative effectiveness is likely to become increasingly common as health information technology spreads, and data become more accessible; however, these data sets may be particularly limiting in their ability to include data on potential confounders. In some cases, the characteristics that influence the treatment decision may not be available in the data (eg, performance status, tumor gene expression), making concerns about confounding by indication too high to proceed without adjusting data collection or considering a different question.

Meta-analysis of observational studies of hormone replacement therapy (HRT) and coronary artery disease incidence comparing studies that did and did not adjust for socioeconomic status (SES). Data adapted. 22

Second, several analytic approaches can be used to address differences between groups in observational studies. The standard analytic approach involves the use of multivariable adjustment through regression models. Regression allows the estimation of the change in the outcome of interest from the difference in intervention, holding the other variables in the model (covariates) constant. Although regression remains the standard approach to analysis of observational data, regression can be misleading if there is insufficient overlap in the covariates between groups or if the functional forms of the variables are incorrectly specified. 23 Furthermore, the number of covariates that can be included is limited by the number of participants with the outcome of interest in the data set.

Propensity score analysis is another approach to the estimation of an intervention effect in observational data that enables the inclusion of a large number of covariates and a transparent assessment of the balance of covariates after adjustment. 23 – 26 Propensity score analysis uses a two-step process, first estimating the probability of receiving a particular intervention based on the observed covariates (the propensity score) and estimating the effect of the intervention within groups of patients who had a similar probability of receiving the intervention (often grouped as quintiles of propensity score). The degree to which the propensity score is able to represent the differences in covariates between intervention groups is assessed by examining the balance in covariates across propensity score categories. In an ideal situation, after participants are grouped by their propensity for being treated, those who receive different interventions have similar clinical and sociodemographic characteristics—at least for the characteristics that are measured ( Table 1 ). Rates of the outcomes of interest are then compared between intervention groups within each propensity score category, paying attention to whether the intervention effect differs across patients with a different propensity for receiving the intervention. In addition, the propensity score itself can be included in a regression model estimating the effect of the intervention on the outcome, a method that also allows for additional adjustment for covariates that were not sufficiently balanced across intervention groups within propensity score categories.

Hypothetic Example of Propensity Score Analysis Comparing Two Intervention Groups, A and B

The use of propensity scores for oncology clinical effectiveness research has become increasingly popular over the last decade, with six articles published in Journal of Clinical Oncology in 2011 alone. 27 – 32 However, propensity score analysis has limitations, the most important of which is that it can only include the variables that are in the available data. If a factor that influences the intervention assignment is not included or measured accurately in the data, it cannot be adequately addressed by a propensity score. For example, in a prior propensity score analysis of the association between active treatment and prostate cancer mortality among elderly men, we were able to include only the variables available in Surveillance, Epidemiology, and End Results–Medicare linked data in our propensity score. 33 The data included some of the factors that influence treatment decisions (eg, age, comorbidities, tumor grade, and size) but not others (eg, functional status, prostate-specific antigen score). Furthermore, the measurement of some of the available factors was imperfect—for example, assessment of comorbidities was based on billing codes, which can underestimate actual comorbidity burden and provide no information about the severity of the comorbidity. Thus, although the final result demonstrating a fairly strong association between active treatment and reduced mortality was quite robust based on the data that were available, it is still possible that the association represents unaddressed selection factors where healthier men underwent active treatment. 34

Instrumental variable methods are a third analytic approach that estimate the effect of an intervention in observational data without requiring the factors that differ between the intervention groups to be available in the data, thereby addressing both measured and unmeasured confounders. 35 The goal underlying instrumental variable analysis is to identify a characteristic (called the instrument) that strongly influences the assignment of patients to intervention but is not associated with the outcomes of interest (except through the intervention). In essence, an instrumental variable approach is an attempt to replicate an RCT, where the instrument is randomization. 36 Common instruments include the patterns of treatment across geographic areas or health care providers, the distance to a health care facility able to provide the intervention of interest, or structural characteristics of the health care system that influence what interventions are used, such as the density of certain types of providers or facilities. The analysis involves two stages: first, the probability of receiving the intervention of interest is estimated as a function of the instrument variable and other covariates; second, a model is built predicting the outcome of interest based on the instrument-based intervention probability and the residual from the first model.

Instrumental variable analysis is commonly used in economics 37 and has increasingly been applied to health and health care. In oncology, instrumental variable approaches have been used to examine the effectiveness of treatments for lung, prostate, bladder, and breast cancers, with the most common instruments being area-level treatment patterns. 38 – 42 One recent analysis of prostate cancer treatment found that multivariable regression and propensity score methods resulted in essentially the same estimate of effect for radical prostatectomy, but an instrumental variable based on the treatment pattern of the previous year found no benefit from radical prostatectomy, similar to the estimate from a recently published trial. 41 , 43 However, concerns also exist about the validity of instrumental variable results, particularly if the instrument is not strongly associated with the intervention, or if there are other potential pathways by which the instrument may influence the outcome. Although the strength of the association between the instrument and the intervention assignment can be tested in the analysis, alternative pathways by which the instrument may be associated with the outcome are often not identified until after publication. A recent instrumental variable analysis used annual rainfall as the instrument to demonstrate an association between television watching and autism, arguing that annual rainfall is associated with the amount of time children watch television but is not otherwise associated with the risk of autism. 44 The findings generated considerable controversy after publication, with the identification of several other potential links between rainfall and autism. 45 Instrumental variable methods have traditionally been unable to examine differences in effect between patient subgroups, but new approaches may improve their utility in this important component of CER. 46 , 47

SYSTEMATIC REVIEWS

For some decisions faced by clinicians and policy makers, there is insufficient evidence to inform decision making, and new studies to generate evidence are needed. However, for other decisions, evidence exists but is sufficiently complex or controversial that it must be synthesized to inform decision making. Systematic reviews are an important form of evidence synthesis that brings together the available evidence using an organized and evaluative approach. 48 Systematic reviews are frequently used for guideline development and generally include four major steps. 49 First, the clinical decision is identified, and the analytic framework and key questions are determined. Sometimes the decision may be straightforward and involve a single key question (eg, Does drug A reduce the incidence of disease B?), but other times the question may be more complicated (eg, Should gene expression profiling be used in early-stage breast cancer?) and involve multiple key questions. 50 Second, the literature is searched to identify the relevant studies using inclusion and exclusion criteria that may include the timing of the study, the study design, and the location of the study. Third, the identified studies are graded on quality using established criteria such as the CONSORT criteria for RCTs 51 and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria for observational studies. 52 Studies that do not meet a minimum quality threshold may be excluded because of concern about the validity of the results. Fourth, the results of all the studies are collated in evidence tables, often including key characteristics of the study design or population that might influence the results. Meta-analytic techniques may be used to combine results across studies when there is sufficient homogeneity to make a single-point estimate statistically valid. Alternatively, models may be used to identify the study or population factors that are associated with different results.

Although systematic reviews are a key component of evidence-based medicine, their role in CER is still uncertain. The traditional approach to systematic reviews has often excluded observational studies because of concerns about internal validity, but such exclusions may greatly limit the evidence available for many important comparative effectiveness questions. CER is designed to inform real-world decisions between available alternatives, which may include multiple tradeoffs. Inclusion of information about harms in comparative effectiveness systematic reviews is desirable but often challenging because of limited data. Finally, systematic reviews are rarely able to examine differences in intervention effects across patient characteristics, another important step for achieving the goals of CER.

DECISION AND COST-EFFECTIVENESS ANALYSIS

Another evidence synthesis method that is gaining increasing traction in CER is decision modeling. Decision modeling is a quantitative approach to evidence synthesis that brings together data from a range of sources to estimate expected outcomes of different interventions. 53 The first step in a decision model is to lay out the structure of the decision, including the alternative choices and the clinical and economic outcomes of those alternatives. 54 Ensuring that the structure of the model holds true to the clinical scenario of interest without becoming overwhelmed by minor possible variations is critical for the eventual impact of the model. 55 Once the decision structure is determined, a decision tree or simulation model is created that incorporates the probabilities of different outcomes over time and the change in those probabilities from the use of different interventions. 56 , 57 To calculate the expected outcomes, a hypothetic cohort of patients is run through each of the decision alternatives in the model. Estimated outcomes are generally assessed as a count of events in the cohort (eg, deaths, cancers) or as the mean or median life expectancy among the cohort. 58

Decision models can also include information about the value placed on each of the outcomes (often referred to as utility) as well as the health care costs incurred by the interventions and the health outcomes. A decision model that includes cost and utility is often referred to as a cost-benefit or cost-effectiveness model and is used in some settings to compare value across interventions. The types of costs that are included depend on the perspective of the model, with a model from the societal perspective including both direct and indirect medical costs (eg, loss of productivity), a model from a payer (ie, insurer) perspective including only direct medical costs, and a model from a patient perspective including the costs experienced by the patient. Future costs are discounted to address the change in monetary value over time. 59 Sensitivity analyses are used to explore the impact of different assumptions on the model results, a critical step for understanding how the results should be used in clinical and policy decisions and for the development of future evidence-generation research. These sensitivity analyses often use a probabilistic approach, where a distribution is entered for each of the inputs and the computer samples from those distributions across a large number of simulations, thereby creating a confidence interval around the estimated outcomes of the alternative choices.

Decision models have several strengths in CER. They can link multiple sources of information to estimate the effect of different interventions on health outcomes, even when there are no studies that directly assess the effect of interest. Because they can examine the effect of variation in different probability estimates, they are particularly useful for understanding how patient characteristics will affect the expected outcomes of different interventions. Decision models can also estimate the impact of an intervention across a population, including the effect on economic outcomes. Decision and cost-effectiveness analyses have been used frequently in oncology, particularly for decisions with options that include the use of a diagnostic or screening test (eg, bone mineral density testing for management of osteoporosis risk), 60 involve significant tradeoffs (eg, adjuvant chemotherapy), 61 or have only limited empirical evidence (eg, management strategies in BRCA mutation carriers). 62

However, decision models also have several limitations that have limited their impact on clinical and policy decision making in the United States to date and are likely to constrain their role in future CER. Often, model results are highly sensitive to the assumptions of the model, and removing bias from these assumptions is difficult. The potential impact of conflicts of interest is high. Decision models require data inputs. For many decisions, data are insufficient for key inputs, requiring the use of educated guesses (ie, expert opinion). The measurement of utility has proven particularly challenging and can lead to counterintuitive results. In the end, decision analysis is similar to other comparative effectiveness methods—useful for the right question as long as results are interpreted with an understanding of the methodologic limitations.

SELECTION OF CER METHODS

The choice of method for a comparative effectiveness study involves the consideration of multiple factors. The Patient-Centered Outcomes Research Institute Methods Committee has identified five intrinsic and three extrinsic factors ( Table 2 ), including internal validity, generalizability, and variation across patient subgroups as well as the feasibility and time urgency. 63 The importance of these factors will vary across the questions being considered. For some questions, the concern about selection bias will be too great for observational studies, particularly if a strong instrument cannot be identified. Many questions about aggressive versus less aggressive treatments may fall into this category, because the decision is often correlated with patient characteristics that predict survival but are rarely found in observational data sets (eg, functional status, social support). For other questions, concern about selection bias will be less pressing than the need for rapid and efficient results. This scenario may be particularly relevant for the comparison of existing therapies that differ in cost or adverse outcomes, where the use of the therapy is largely driven by practice style. In many cases, the choice will be pragmatic based on what data are available and the feasibility of conducting an RCT. These choices will increasingly be informed by the value of information methods 64 – 66 that use economic modeling to provide guidance about where and how investment in CER should be made.

Factors That Influence Selection of Study Design for Patient-Centered Outcome Research

In reality, the questions of CER are not new but are simply more important than ever. Nearly 50 years ago, Sir Austin Bradford Hill spoke about the importance of a broad portfolio of methods in clinical research, saying “To-day … there are many drugs that work and work potently. We want to know whether this one is more potent than that, what dose is right and proper, for what kind of patient.” 7 (p109) This call has expanded beyond drugs to become the charge for CER. To fulfill this charge, investigators will need to use a range of methods, extending the experience in effectiveness research of the last decades “to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.” 1 (p29)

Supported by Award No. UC2CA148310 from the National Cancer Institute.

The content is solely the responsibility of the author and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Author's disclosures of potential conflicts of interest and author contributions are found at the end of this article.

AUTHOR'S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The author(s) indicated no potential conflicts of interest.

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Comparative research.

A specific comparative research methodology is known in most social sciences. Its definition often refers to countries and cultures at the same time, because cultural differences between countries can be rather small (e.g., in Scandinavian countries), whereas very different cultural or ethnic groups may live within one country (e.g., minorities in the United States). Comparative studies have their problems on every level of research, i.e., from theory to types of research questions, operationalization, instruments, sampling, and interpretation of results.

The major problem in comparative research, regardless of the discipline, is that all aspects of the analysis from theory to datasets may vary in definitions and/or categories. As the objects to compare usually belong to different systemic contexts, the establishment of equivalence and comparability is thus a major challenge of comparative research. This is often “operationalized” as functional equivalence, i.e., the functionality of the research objects within the different system contexts must be equivalent. Neither equivalence nor its absence, “bias,” can be presumed. It has to be analyzed and tested for on all the different levels of the research process.

Equivalence And Bias

Equivalence has to be analyzed and established on at least three levels: on the levels of the construct, the item, and the method (van de Vijver & Tanzer 1997). Whenever a test on any of these levels shows negative results, a cultural bias is supposable. Thus, bias on these three levels can be described as the opposite of equivalence. Van de Vijver and Leung (1997) define bias as the variance within certain variables or indicators that can only be caused by culturally unspecific measurement. For example, a media content analysis could examine the amount of foreign affairs coverage in one variable, by measuring the length of newspaper articles. If, however, newspaper articles in country A are generally longer than they are in country B, irrespective of their topic, the result of a sum or mean index of foreign affairs coverage would almost inevitably lead to the conclusion that the amount of foreign affairs coverage in country A is higher than in country B. This outcome would be hardly surprising and not in focus with the research question, because the countries’ average amount of foreign affairs coverage is not related to the national average length of articles. To avoid cultural bias, the results must be standardized or weighted, for example by the mean article length.

To find out whether construct equivalence can be assumed, the researcher will generally require external data and rather complex procedures of culture-specific construct validation(s). Ideally, this includes analyses of the external structure, i.e., theoretical references to other constructs, as well as an examination of the latent or internal structure. The internal structure consists of the relationships between the construct’s sub-dimensions. It can be tested using confirmatory factor analyses, multidimensional scaling, or item analyses. Equivalence can be assumed if the construct validation for every culture has been successful and if the internal and external structures are identical in every country. However, it has to be stated that it is hardly possible to prove construct equivalence beyond any doubts (Wirth & Kolb 2004).

Even with a given construct equivalence, bias can still occur on the item level. The verbalization of items in surveys and of definitions and categories in content analyses can cause bias due to culture-specific connotations. Item bias is mostly evoked by bad, in the sense of nonequivalent, translation or by culture-specific questions and categories (van de Vijver & Leung 1997). Compared to the complex procedures discussed in the case of construct equivalence, the testing for item bias is rather simple (once construct equivalence has been established): Persons from different cultures who take the same positions or ranks on an imaginary construct scale must show the same answering attitude toward every item that measures the construct. Statistically, the correlation of the single items with the total (sum) score have to be identical in every culture, as test theory generally uses the total score to estimate the position of any individual on the construct scale. In brief, equivalence on the item level is established whenever the same sub-dimensions or issues can be used to explain the same theoretical construct in every country (Wirth & Kolb 2004).

When the instruments are ready for application, method equivalence comes to the fore. Method equivalence consists of sample equivalence, instrument equivalence, and administration equivalence. Violation of any of these equivalences produces a method bias. Sample equivalence refers to an equivalent selection of subjects or units of analysis. Instrument equivalence deals with the examination of whether people in every culture agree to take part in the study equivalently, and whether they are used to the instruments equivalently (Lauf & Peter 2001). Finally, a bias on the administration level can occur due to culturespecific attitudes of the interviewers that might produce culture-specific answers. Another source of administration bias could be found in socio-demographic differences between the various national interviewer teams (van de Vijver & Tanzer 1997).

The Role Of Theory

Theory plays a major role in three dimensions when looking for a comparative research strategy: theoretical diversity, theory drivenness, and contextual factors (Wirth & Kolb 2004). Swanson (1992) distinguishes between three principal strategies of dealing with international theoretical diversity. A common possibility is called the avoidance strategy. Many international comparisons are made by teams that come from one culture or nation only. Usually, their research interests are restricted to their own (scientific) socialization. Within this monocultural context, broad approaches cannot be applied and “intertheoretical” questions cannot be answered. This strategy includes atheoretical and unitheoretical (referring to one national theory) studies with or without contextualization (van den Vijver & Leung 2000; Wirth & Kolb 2004).

The pretheoretical strategy tries to avoid cultural and theoretical bias in another way: these studies are undertaken without a strict theoretical background until results are to be interpreted. The advantage of this strategy lies in the exploration, i.e., in developing new theories. Although, following the strict principles of critical rationalism, because of the missing theoretical background the proving of theoretical deduced hypotheses is not applicable (Popper 1994). Most of the results remain on a descriptive level and never reach theoretical diversity. Besides, the instruments for pretheoretical studies must be almost “holistic,” in order to integrate every theoretical construct conceivable for the interpretation. These studies are mostly contextualized and can, thus, become rather extensive (Swanson 1992).

Finally, when a research team develops a meta-theoretical orientation to build a framework for the basic theories and research questions, the data can be analyzed using different theoretical backgrounds. This meta-theoretical strategy allows the extensive use of all data and contextual factors, producing, however, quite a variety of often very different results, which are not easily summarized in one report (Swanson 1992). It is obvious that the higher is the level of theoretical diversity, the greater has to be the effort for construct equivalence.

Research Questions

Van de Vijver and Leung (1996, 1997) distinguish between two types of research questions: structure-oriented questions are mostly interested in the relationship between certain variables, whereas level-oriented questions focus on the parameter values. If, for example, a knowledge gap study analyzes the relationship between the knowledge gained from television news by recipients with high and low socio-economic status (SES) in the UK and the US, the question is structure oriented, because the focus is on a national relationship of knowledge indices and the mean gain of knowledge is not taken into account. Usually, structure-oriented data require correlation or regression analyses. If the main interest of the study is a comparison of the mean gain of knowledge of people with low SES in the UK and the US, the research question is level oriented, because the two knowledge indices of the two nations are to be compared. In this case, one would most probably use analyses of variance. The risk for cultural bias is the same for both kinds of research questions.

Emic And Etic Strategies Of Operationalization

Before the operationalization of an international comparison, the research team has to analyze construct equivalence to prove comparability. In the case of missing internal construct equivalence, the construct cannot be measured equivalently in every country. The decision of whether or not to use the same instruments in every country does not have any impact on this problem of missing construct equivalence. An emic approach could solve this problem. The operationalization for the measurement of the construct(s) is developed nationally, so that the culture-specific adequacy of each of the national instruments will be high. Comparison on the construct level remains possible, even though the instruments vary culturally, because functional equivalence has been established on the construct level by the culture-specific measurement. In general, this procedure will even be possible if national instruments already exist.

As measurement differs from culture to culture, the integration of the national results can be very difficult. Strictly speaking, this disadvantage of emic studies only allows for the interpretation of a structure-oriented outcome with a thorny validation process. It has to be proven that the measurements with different indicators on different scales really lead to data on equivalent constructs. By using external reference data from every culture, complex weighting and standardization procedures can possibly lead to valid equalization of levels and variance (van de Vijver & Leung 1996, 1997). In research practice, emic measuring and data analysis is often used to cast light on cultural differences.

If construct equivalence can be assumed after an in-depth analysis, an etic modus operandi could be recommended. In this logic, approaching the different cultures by using the same or a slightly adapted instrument is valid because the constructs are “functioning” equally in every culture. Consequently, an emic proceeding should most probably come to similar instruments in every culture. Reciprocally, an etic approach must lead to bias and measurement artifacts when applied under the circumstances of missing construct equivalence.

It is obvious that the advantages of emic proceedings are not only the adequate measurement of culture-specific elements, but also the possible inclusion of, e.g., idiographic elements of each culture. Thus, this approach can be seen as a compromise of qualitative and quantitative methodologies. Sometimes comparative researchers suggest analyzing cultural processes in a holistic way without crushing them into variables; psychometric, quantitative data collection would be suitable for similar cultures only. As an objection to this simplification, one should remember the emic approach’s potential to provide the researchers with comparable data, as described above. In contrast, holistic analyses produce culture-specific outcomes that will not be comparable; the problem of equivalence and bias has only been moved to the interpretation of results.

Adaptation Of The Instruments

Difficulties in establishing equivalence are regularly linked to linguistic problems. How can a researcher try to establish functional equivalence without knowledge of every language of the cultures under examination? For a linguistic adaptation of the theoretical background as well as for the instruments, one can again discriminate between “more etic” and “more emic” approaches.

Translation-oriented approaches produce two translated versions of the text: one in the “foreign” language and one after retranslation into the original language. The latter version can be compared to the original version to evaluate the translation. Note that this method produces eticly formed instruments, which can only work whenever functional equivalence has been established on every superior level. Van de Vijver and Tanzer (1997) call this procedure application of an instrument in another language. In a “more emic” cultural adaptation, cultural singularities can be included if, e.g., culture-specific connotations are counterbalanced by a different item formulation.

Purely emic approaches develop entirely culture-specific instruments without translation. Two assembly approaches are available (van de Vijver & Tanzer 1997). First, in order to maintain the committee approach, an international interdisciplinary group of experts of the cultures, languages, and research field decides whether the instruments are to be formed culture-specifically or whether a cultural adaptation will be sufficient. Second, the dual-focus approach tries to find a compromise between literal, grammatical, syntactical, and construct equivalence. Native speakers and/or bilinguals arrange the different language versions together with the research team in a multistep procedure (Erkut et al. 1999).

Usually, researchers use personal preference and accessibility of data to select the countries or cultures to study. This kind of forming of an atheoretical sample avoids many problems (but not cultural bias!). At the same time, it ignores some advantages. Przeworski and Teune (1970) suggest two systematic and theory-driven approaches. The quasiexperimental most similar systems design tries to stress cultural differences. To minimize the possible causes for the differences, those countries are chosen that are the “most similar,” so that the few dissimilarities between these countries are most likely to be the reason for the different outcomes. Whenever the hypotheses highlight intercultural similarities, the most different systems design is appropriate. Here, in a kind of turned-around quasi-experimental logic, the focus lies on similarities between cultures, even though these differ in the greatest possible way (Kolb 2004; Wirth & Kolb 2004).

Random sampling and representativeness play a minor role in international comparisons. The number of states in the world is limited and a normal distribution for the social factors under examination, i.e., the precondition of random sampling, cannot be assumed. Moreover, many statistical methods meet problems when applied under the condition of a low number of cases (Hartmann 1995).

Data Analysis And Interpretation Of Results

Given the presented conceptual and methodological problems of international research, special care must be taken over data analysis and the interpretation of results. As the implementation of every single variable of relevance is hardly accomplishable in international research, the documentation of methods, work process, and data analysis is even more important here than in single-culture studies. Thus, the evaluation of the results must ensue in additional studies. At any rate, an intensive use of different statistical analyses beyond the “general” comparison of arithmetic means can lead to further validation of the results and especially of the interpretation. Van de Vijver and Leung (1997) present a widespread summary of data analysis procedures, including structureand level-oriented approaches, examples of SPSS syntax, and references.

Following Przeworski’s and Teune’s research strategies (1970), results of comparative research can be classified into differences and similarities between the research objects. For both types, Kohn (1989) introduces two separate ways of interpretation. Intercultural similarities seem to be easier to interpret, at first glance. The difficulties emerge when regarding equivalence on the one hand (i.e., there may be covert cultural differences within culturally biased similarities), and when regarding the causes of similarities on the other. The causes will be especially hard to determine in the case of “most different” countries, as different combinations of different indicators can theoretically produce the same results. Esser (2000) refers to diverse theoretical backgrounds that will lead either to differences (e.g., action-theoretically based micro-research) or to similarities (e.g., system-theoretically oriented macro-approaches). In general, the starting point of Przeworski and Teune (1970) seems to be the easier way to come to interesting results and interpretations, using the quasi-experimental approach for “most similar systems with different outcome.” In addition to the advantages of causal interpretation, the “most similar” systems are likely to be equivalent from the top level of the construct to the bottom level of indicators and items. “Controlling” other influences can minimize methodological problems and makes analysis and interpretation more valid.

References:

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