The following module describes the use of comparative research in determining causal relationships.
- Define causal-comparative research and discuss its purpose
- Describe the use of the causal-comparative method and provide examples
- Describe the process of conducting and analyzing causal-comparative research
The following SlideShare presentation, Causal-comparative Research, offers an introduction into the basics of causal-comparative research including definitions, examples, data analysis and limitations. Please begin by viewing this SlideShare before moving on to the rest of the module.
Causal-comparative research is used to identify cause-effect relationships or to examine the consequences of differences that already exist between two groups. Causal-comparative research is also sometimes referred to as “ex-post facto” research because the researcher is attempting to determine the cause or reason for differences that already exist among groups of individuals. In other words, the effect and the alleged cause have already occurred and are being studied “after the fact”.
Characteristics of Causal-comparative Research
- Typically starts with the effect and seeks to find possible causes
- Used when studying differences where the variables cannot be manipulated
- Involves selecting two comparison groups to be studied: Experimental Group and Control Group
- Participants are already organized into these groups and groups are pre-existing
- No random selection of participants is possible
- Groups chosen because one group possesses a characteristic or experience and the other group does not
- Involves making comparisons between these pre-existing groups in regard to the variables of interest
- Studies variables that cannot be manipulated for ethical or practical reasons
- Independent variable – the characteristic, experience or differing factor between the groups
- Dependent variable – the effect of the characteristic, experience or differing factor on groups that possess the variable
- Does not provide the researcher with true experimental data because the variables are not manipulated
- Less costly and time consuming than experimental studies
Types of Causal-Comparative Research
- Retrospective Causal-Comparative Research – In this type of comparative research, the researcher is investigating a research question after the effects have already occurred. The researcher studies how one variable may have influenced another variable. This is the most common type of causal-comparative research.
- Prospective Causal-Comparative Research - Researchers using this type of method begin by studying the causes and then investigating the possible effects.
Conducting Causal-Comparative Research
- Select a topic – Researchers commonly look for experiences or situations that have occurred in the real world.
- Review the literature – Reviewing the literature regarding the topic may help researcher identify the independent and dependent variables for the study. It may also help identify extraneous variables that may contribute to a cause-effect relationship.
- Develop a hypothesis – It should describe the impact of the independent variable on the dependent variable.
- Select the comparison groups – Researchers should use caution when selecting groups and attempt to choose groups that only differ in regard to the independent variable if possible. This will help control for extraneous variables and reduce their effect. Some researchers will use techniques, such as matching which is commonly used in experimental research, in an attempt to find corresponding groups that differ primarily by the presence of the independent variable.
- Select tool for measuring variables and collecting data – In causal-comparative research, the researcher does not have to implement a treatment protocol, therefore, it is a matter of collecting data that allows for comparisons to be made between the groups. This could come from existing data sources, surveys, interviews, etc...
- Analyze and Interpret the Results – Both descriptive and inferential statistics are commonly used. For descriptive statistical analysis, researchers typically use the mean, frequency, and the standard deviation. Commonly used inferential statistics include the t-test, the analysis of variance, and the chi square. These tests help the researcher determine if there is a statistically significant difference between the groups. Finally, when interpreting the results, researchers should be cautious about stating that the independent variable caused the dependent variable. Due to the lack of randomization in participant selection and the presences of extraneous variables, it is probably best to state that the results show a possible effect or possible cause. Experimental research designs are really the only methods that can truly establish definitive cause-effect relationships.
Examples of Causal-Comparative Research
- A teacher may have a math class that has done considerably better than another of her math classes and wants to investigate whether it is due to the practice sessions that she implemented with first group. The practice sessions are the independent variable.
- Is school absenteeism impacted by whether both parents work outside the home? The groups are pre-existing, and variables are whether or not both parents work outside the home and the number of absences.
- Does using free weights or exercise machines produce greater changes in body composition within an athletic program? The athletes already have been on the training regimes so there is no variable to be manipulated and the groups are pre-existing. This is an example where extraneous factors such as diet and the amount of aerobic activity may have a big impact and should be considered.
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