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?
Descriptive research describes something as it naturally occurs. Because researchers do not manipulate any variables, descriptive research cannot be used to characterize “cause-and-effect” relationships.
In contrast, experimental research involves manipulating an independent variable to see how it impacts a dependent variable. Experimental research can be used to investigate “why” questions and establish cause-and-effect relationships.
Continue reading: What is the difference between descriptive and experimental research?
Longitudinal studies are broadly categorized as prospective (researchers recruit participants and collect their data over the following years) or retrospective (researchers use an existing longitudinal dataset to “look back in time” at factors contributing to an outcome of interest).
Specific longitudinal study designs include the following.
- Repeated cross-sectional study: Researchers collect the same data from distinct samples of the same population over time (essentially a cross-sectional study that is repeated with different people).
- Cohort study: Participants who share one or more characteristics (or “exposures”) are followed over time to observe which outcomes they develop.
- Panel study: A group of participants, often randomly selected, is studied over time to observe changes in their attitudes or behaviors.
Continue reading: What are the types of longitudinal studies?
There is no minimum duration for longitudinal studies. Some are only a few days or weeks long, but many last for years or even decades.
The only requirement for a study to be considered longitudinal is that data are collected repeatedly from participants.
Continue reading: How long is a longitudinal study?
