Researchers examine which factors appear to be connected to particular events, states, or aspects of behavior. Ex post facto research is a method used to identify potential causes of events that have already occurred and cannot be anticipated or manipulated by the investigator. It entails looking at an existing situation or state of affairs and tracing back in time to find likely causes.
Ex post facto research is a design in which the investigator does not get involved until after the event has happened. Ex post facto research designs are the basis of much social research that does not allow for manipulating the characteristics of human participants.
Ex-Post-Facto Research
Ex-Post-Facto Research, or retrospective research, is a type of study in which the researcher seeks to understand the potential causes of a previously observed outcome.
For instance, if a young individual has engaged in delinquent behavior, the researcher would investigate various factors and events that might have contributed to this delinquent behavior. These factors could encompass aspects like the lack of discipline in school, family history, peer influence, neighborhood environment, or socialization.
However, ex-post facto research has its limitations. The groups involved might exhibit unforeseen differences. For example, individuals who learned a new method of mathematics in elementary school could be different from those who learned the old approach. This divergence could be because students were chosen based on their mathematical abilities or other criteria. Additionally, disparities might arise if only wealthier or more progressive school districts introduced innovative math curricula. These factors create uncertainty about why some students were exposed to new math while others were taught using the old math.
Due to the absence of random allocation of students into groups, there is a possibility of systematic bias among participants. This means that the average math competency scores of the two groups might differ for reasons other than the type of math curriculum they were exposed to. To conduct a true experiment, researchers would need to randomly assign students to new or old math classes while controlling for all other variables. Subsequently, they would assess math competency when these students reach college age.
In ex-post facto research, understanding causality and eliminating potential biases can be challenging due to the absence of randomization and other control measures commonly used in experimental research.
Example of Ex-Post-Facto Research
Let’s illustrate the concept of an ex post facto experiment with an example. Imagine we want to study how individuals with varying levels of anxiety respond to a sudden, loud noise. It’s essential to clarify that the “loud noise” itself is not considered a treatment or an independent variable in this scenario because it is administered to all participants as a common task.
The task that all participants perform, which is their reaction to the loud noise, serves as our dependent variable. The key factor in this study is the division of participants into two groups based on specific traits of interest to the researcher, which are anxious and nonanxious individuals.
These traits, anxiety levels in this case, constitute the basis of the experimental setup.
Characteritics of Ex Post Facto Research
- Inclusion of a Control or Comparison Group: Ex-post facto research involves the use of a control or comparison group. This group is crucial for making comparisons with the actual experimental group to understand the reasons behind an event or outcome that has already transpired.
- Lack of Control or Modification of Variables: The behavior, activities, events, treatments, or independent variables under study cannot be controlled or modified by the researcher. Ex-post facto research aims to analyze causes based on past actions, and these actions cannot be altered.
- Focus on the Outcome: The primary focus of ex-post facto research is on the outcome or phenomenon that has already occurred. Researchers first examine the phenomenon or event itself before delving into the reasons behind it.
- Investigation of “How” and “What”: Ex-post facto research seeks to understand the causal implications of an event or phenomenon. It emphasizes the “how” and “what” aspects, aiming to uncover the reasons for the occurrence.
- Examination of Potential Effects and Causes: Researchers in ex-post facto research investigate cause-and-effect relationships related to an event, action, or behavior to gain insights into the underlying factors.
Procedures for ExPost Facto Research
ExPost Facto Research involves a systematic series of steps to investigate and understand the causal factors behind a particular observed effect. The following is the sequence of steps typically followed in this type of research:
- Define the Problem: The first step is to clearly define the research problem or the specific effect that you intend to study.
- Examine the Relevant Literature: Review existing literature and research related to the problem to gain insights and background information.
- Formulate Hypotheses: Develop hypotheses or educated guesses about possible solutions or factors that might have contributed to the observed effect.
- List Assumptions: Identify and document the underlying assumptions that serve as the foundation for your hypotheses and the research procedure.
- Data Collection Techniques: Select appropriate data collection techniques, such as questionnaires, interviews, literature search, surveys, or experiments, to gather relevant information.
- Participant Selection: Choose two groups of participants who differ concerning the phenomena, behavior, event, or situation being studied. These groups are essential for making comparisons.
- Establish Categories: Create categories to classify the collected data. These categories should align with the research objectives and should be capable of revealing significant relationships or similarities.
- Data Analysis: Analyze, describe, and interpret the findings from the collected data. Evaluate whether the hypotheses developed in step 3 are accepted or rejected based on the research results.
Pros of Ex Post Facto Research
- Relevance for Unmanipulated Variables: Ex post facto research is particularly relevant for studying behavioral and social phenomena where variables cannot be manipulated intentionally.
- Analysis of Cause and Effect: It is valuable for analyzing the causes of effects, providing an advantage over experimental research, which often focuses on the manipulation of variables.
- Time and Cost Efficiency: This research design is generally less time-consuming and cost-effective compared to other research methodologies.
- Researcher’s Subjectivity: Ex post facto research allows the researcher’s insights and opinions to play a significant role in the research process.
Cons of Ex Post Facto Research
- Lack of Random Assignment: Researchers cannot randomly assign research subjects to different groups, which limits their control over variables.
- Inability to Manipulate Variables: Since the variables cannot be manipulated, researchers must work with existing data or conditions.
- Lack of Random Group Assignment: Participants cannot be randomly assigned to different groups, affecting the research’s internal validity.
- Unclear Causation: Ex post facto research may not establish a clear cause-and-effect relationship between independent and dependent variables, potentially lacking a feasible explanation for this relationship.
Other articles
Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.
Citation Styles
- APA Reference Page
- MLA Citations
- Chicago Style Format
- “et al.” in APA, MLA, and Chicago Style
- Do All References in a Reference List Need to Be Cited in Text?
Comparision
- Basic and Applied Research
- Cross-Sectional vs Longitudinal Studies
- Survey vs Questionnaire
- Open Ended vs Closed Ended Questions
- Experimental and Non-Experimental Research
- Inductive vs Deductive Approach
- Null and Alternative Hypothesis
- Reliability vs Validity
- Population vs Sample
- Conceptual Framework and Theoretical Framework
- Bibliography and Reference
- Stratified vs Cluster Sampling
- Sampling Error vs Sampling Bias
- Internal Validity vs External Validity
- Full-Scale, Laboratory-Scale and Pilot-Scale Studies
- Plagiarism and Paraphrasing
- Research Methodology Vs. Research Method
- Mediator and Moderator
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- Parametric and Non-Parametric Test
Comparision
- Independent vs. Dependent Variable – MIM Learnovate
- Research Article and Research Paper
- Proposition and Hypothesis
- Principal Component Analysis and Partial Least Squares
- Academic Research vs Industry Research
- Clinical Research vs Lab Research
- Research Lab and Hospital Lab
- Thesis Statement and Research Question
- Quantitative Researchers vs. Quantitative Traders
- Premise, Hypothesis and Supposition
- Survey Vs Experiment
- Hypothesis and Theory
- Independent vs. Dependent Variable
- APA vs. MLA
- Ghost Authorship vs. Gift Authorship
Research
- Research Methods
- Quantitative Research
- Qualitative Research
- Case Study Research
- Survey Research
- Conclusive Research
- Descriptive Research
- Cross-Sectional Research
- Theoretical Framework
- Conceptual Framework
- Triangulation
- Grounded Theory
- Quasi-Experimental Design
- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ethnography
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
- Ex-Post-Facto
Research
- Table of Contents
- Dissertation Topic
- Synopsis
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Variables
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Questionnaire
- Abstract
- Validity
- Reliability
- Measurement of Scale
- Sampling Techniques
- Acknowledgements
Statistics
- PLS-SEM model
- Principal Components Analysis
- Multivariate Analysis
- Friedman Test
- Chi-Square Test (Χ²)
- T-test
- SPSS
- Effect Size
- Critical Values in Statistics
- Statistical Analysis
- Calculate the Sample Size for Randomized Controlled Trials
- Covariate in Statistics
- Avoid Common Mistakes in Statistics
- Standard Deviation
- Derivatives & Formulas
- Build a PLS-SEM model using AMOS
- Principal Components Analysis using SPSS
- Statistical Tools
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- One-tailed and Two-tailed Test
- Parametric and Non-Parametric Test