The goal of explanatory research is to understand why something happens. This is often done by exploring a cause-and-effect relationship between two variables.
Examples of explanatory research questions include the following:
- Does talking to plants (cause) make them grow faster (effect)?
- Are people more likely to buy chocolate (effect) when they’re sad (cause)?
- Does listening to music while studying (cause) improve students’ exam performance (effect)?
Continue reading: What are some examples of explanatory research questions?
The aim of explanatory research is to determine why a phenomenon occurs. This may be done using correlational or experimental research.
On the other hand, descriptive research captures the characteristics of something as is, without intervention.
Though these approaches may share data collection techniques (e.g., they both might use questionnaires), their overarching purpose is distinct.
Continue reading: What’s the difference between explanatory and descriptive research?
Explanatory research examines why something happens. This is usually done by assessing the statistical relationship between two variables.
Because statistical methods like correlation require quantitative (numeric) values, explanatory research generally involves quantitative data.
However, these quantitative explanations may be supplemented by data from, for example, interviews. This mixed methods approach may offer a more comprehensive explanation of a phenomenon.
Continue reading: Can explanatory research be qualitative?
Thematic content analysis is often defined in different ways, and the term is sometimes used interchangeably with thematic analysis or qualitative content analysis.
Qualitative content analysis focuses on systematically summarizing the prevalence of specific codes in a dataset, whereas thematic analysis not only codes data but also identifies themes and underlying narratives.
Thematic content analysis is sometimes defined as a hybrid between these two methods: much like qualitative content analysis, its purpose is to describe a body of qualitative data, but data are broken down into themes rather than more simple codes.
Because “thematic content analysis” can be interpreted in different ways, it’s important to provide a detailed description of your methodology if you choose to use this term.
Continue reading: What is thematic content analysis?
Reflexive thematic analysis is a type of thematic analysis that centers the researcher’s interpretation of the data.
Reflexive thematic analysis acknowledges the subjective nature of data interpretation—rather than focusing on uncovering some “ground truth” in the data, researchers are encouraged to engage with their data and use their own knowledge and experiences for interpretation and analysis.
Continue reading: What is reflexive thematic analysis?
In their 2006 paper, researchers Virginia Braun and Victoria Clarke outlined the following 6 steps for conducting thematic analysis:
- Familiarization
- Generating codes
- Searching for themes
- Reviewing themes
- Defining and naming themes
- Writing up results
Continue reading: What are Braun and Clarke’s 6 steps to thematic analysis?
A theme is an idea or pattern that recurs throughout a dataset and is related to a specific research question.
The identification of themes is a core component of thematic analysis, which is a research method commonly used to analyze qualitative data.
Continue reading: What is a theme?
Descriptive research is used to describe a person, place, or phenomenon as it naturally occurs. Descriptive research can answer “who,” “what,” “when,” “where,” or “how much” questions but not “why” questions.
Consider the following examples of descriptive research questions:
- Who are the primary caregivers for adults with dementia?
- What are the main barriers to public transit use in Toronto, Canada?
- When do students feel most engaged during online lectures?
- Where do young adults shop for groceries?
- How much sleep do undergraduate students get during final exams?
Continue reading: What are some descriptive research examples?
As its name suggests, descriptive research describes something. A researcher conducting descriptive research does not attempt to manipulate any variables; they measure a phenomenon of interest as it naturally occurs.
Descriptive research cannot establish cause-and-effect relationships, but it can be useful for generating hypotheses or learning more about an unfamiliar topic.
Continue reading: What is the goal of descriptive research?
Consider the following common descriptive research methods.
- Case studies are detailed explorations of a single subject. The purpose of case studies is to gain a deep understanding of a person, place, or thing. Case studies on several subjects are called case series studies.
- Cross-sectional studies capture the characteristics of multiple subjects at a single point in time. Descriptive cross-sectional studies, which describe the prevalence of a trait or a condition (how frequently it occurs) are descriptive; analytical studies, which explore associations between risk factors and outcomes, are not.
- Ecological studies are similar to cross-sectional studies but describe groups of people. For example, a cross-sectional study might assess the prevalence of insomnia in American adults, whereas an ecological study would describe the prevalence of insomnia in each of the 50 American states.
Continue reading: What are the types of descriptive research?
