Characteristics of experimental design and non-experimental designs | Psychology for UPSC Optional (Notes) PDF Download

Experimental Design

Definition: Experimental design refers to a methodology employed to investigate cause-and-effect relationships by manipulating one or more independent variables and measuring the resulting impact on a dependent variable.

Purpose: The main objective of experimental design is to establish causal relationships between variables within a controlled setting.

True Experimental Design

• Definition: True experimental design is a research approach wherein the researcher randomly assigns participants to different groups or conditions, manipulating the independent variable to examine its effect on the dependent variable.
• Characteristics: True experimental designs commonly involve a control group that does not undergo the manipulation, as well as a treatment group that experiences the manipulation. Moreover, random assignment is employed to mitigate selection bias.

Types of True Experimental Designs

• Pre-test post-test control group design: In this design, all participants are assessed with a pre-test before the manipulation of the independent variable. Subsequently, a post-test is administered to all participants following the manipulation. Participants are randomly assigned to either the control or treatment group. The control group does not receive the independent variable manipulation, whereas the treatment group does. By comparing the post-test scores of the two groups, potential differences are identified. This design is particularly useful for establishing causality.
• Post-test only control group design: This design entails conducting a post-test after manipulating the independent variable, without a pre-test. Participants are randomly assigned to either the control or treatment group. The control group does not experience the independent variable manipulation, while the treatment group does. By comparing the post-test scores of the two groups, any differences can be examined. This design is beneficial when a pre-test might introduce bias.

Advantages of True Experimental Design

• Establishing causal relationships: True experimental designs enable the establishment of cause-and-effect relationships by manipulating independent variables and observing their impact on dependent variables.
• Control over extraneous variables: Researchers can exercise control over extraneous variables that might influence the dependent variable by maintaining constant conditions across groups or randomly assigning participants.
• Investigating unmanipulable variables: True experimental designs permit the manipulation of variables that cannot be altered in real-world scenarios.

Disadvantages of True Experimental Design:

• Practical and ethical limitations: In certain cases, employing true experimental design may prove impractical or unethical. For instance, manipulating a variable that could potentially harm participants would be ethically unacceptable.
• Limited external validity: True experimental designs are implemented within controlled environments, thereby potentially limiting their generalizability to real-world situations.
• Difficulty in controlling all extraneous variables: Despite efforts to control extraneous variables, complete elimination of all potential sources of variability may not be feasible.
• Costs and time consumption: True experimental designs can be costly and time-consuming for both researchers and participants involved in the study.

Non-Experimental Design

Definition: Non-experimental design refers to a research approach that investigates the relationship between variables without manipulating the independent variable. Instead, it relies on observations or self-report data and does not involve random assignment to groups or conditions.

Characteristics: Non-experimental designs are primarily used to describe a phenomenon or a specific population, rather than establishing a causal relationship between variables. The researcher does not exert control or manipulation over any variables, simply examining the relationship between the variables.

Types of Non-Experimental Designs

• Correlational research: Correlational research is a type of non-experimental design that explores the relationship between two or more variables. It can be conducted through various data collection methods, such as surveys, observational studies, or experiments that do not manipulate an independent variable. A correlation coefficient is employed to gauge the strength and direction of the relationship.
• Survey research: Survey research is a non-experimental design method where participants respond to a series of questions. Surveys can be conducted through different means, including mail, phone, online, or in-person interviews. Surveys are particularly valuable when aiming for a large sample size or when participants are geographically dispersed.
• Case study research: Case study research involves an in-depth examination of an individual or group as a non-experimental design. Case studies provide a detailed understanding of specific cases or groups, allowing for the examination of complex phenomena and contextual factors that may not be apparent in other research methods.
• Naturalistic observation: Naturalistic observation is a non-experimental research method where the researcher observes and records participants' behavior in their natural environment without manipulating any variables. Naturalistic observation helps comprehend behavior in real-life situations and can be employed in various settings, such as homes, schools, or workplaces.
• Longitudinal research: Longitudinal research is a non-experimental design where data is collected from the same individuals at different time points over an extended period. It enables the study of change over time and provides a comprehensive understanding of a phenomenon compared to cross-sectional research.
• Cross-sectional research: Cross-sectional research is a non-experimental design where data is collected from different individuals at the same time. This design allows for the comparison of different groups, often based on age or other demographic characteristics, providing insights into the prevalence or distribution of a phenomenon in a specific population.

Advantages of Non-Experimental Design

• Cost and time efficiency: Non-experimental designs are generally more cost-effective and less time-consuming compared to experimental designs.
• Examination of non-manipulable variables: Non-experimental designs facilitate the exploration of variables that cannot be manipulated, such as past events or traits.
• Hypothesis generation: Non-experimental designs can be valuable for generating hypotheses to guide further research.

Disadvantages of Non-Experimental Design

• Lack of causality: Non-experimental designs cannot establish a cause-and-effect relationship between variables.
• Confounding variables: Non-experimental designs may be susceptible to confounding variables that can influence the relationship between the independent and dependent variables.
• Selection bias: Non-experimental designs often rely on a non-randomly selected sample, leading to selection bias. This bias occurs when the sample does not represent the population accurately, potentially resulting in erroneous conclusions.
• Lack of control: In non-experimental designs, the researcher lacks control over the independent variable and cannot manage extraneous variables that may impact the dependent variable.
• Limited generalizability: Non-experimental designs may have limited generalizability to other populations or settings due to the absence of random assignment and control over extraneous variables.

Quasi-Experimental Design

Definition: Quasi-experimental design refers to an experimental design where the researcher lacks complete control over the assignment of participants to conditions or groups. Unlike true experimental designs that use random assignment, quasi-experimental designs employ alternative methods that may have limitations or biases but still allow the researcher to infer causality between variables.

Characteristics: Quasi-experimental designs rely on statistical techniques to control for extraneous variables and may not include a control group that does not receive the manipulation. They utilize non-randomized assignment, either by using preexisting groups or convenience sampling to form groups.

Types of Quasi-Experimental Designs

• Nonequivalent Control Group Design: This design involves selecting a control group that is not equivalent to the treatment group in terms of the manipulated variable. The researcher strives to match the groups based on relevant characteristics, but true randomization is not achieved.
• Interrupted Time Series Design: This design includes repeated measures of the dependent variable over time, both before and after the manipulation of the independent variable. It enables the researcher to control for temporal trends and infer causality between the variables.
• Nonrandomized Control Group Pretest-Posttest Design: This design employs non-random assignment of participants to groups and utilizes pretest-posttest measures to infer causality between the variables.

Advantages of Quasi-Experimental Design

• Practicality: Quasi-experimental design can be valuable when true experimental design is impractical or ethically challenging.
• Establishing Causality: Despite not employing random assignment, quasi-experimental designs can still establish a causal relationship between variables.
• Study of Non-Manipulable Variables: Quasi-experimental design allows the examination of variables that cannot be manipulated in a true experimental design.

Disadvantages of Quasi-Experimental Design:

• Selection Bias: Quasi-experimental designs may be susceptible to selection bias, which can influence the relationship between the independent and dependent variables.
• Control of Extraneous Variables: The researcher may not have complete control over extraneous variables, potentially leading to inaccurate conclusions.
• Difficulty in Generalizing: Quasi-experimental designs may not generalize well to other populations or settings due to the absence of random assignment and control over extraneous variables.

Matching and Control in Quasi-Experimental Design:

Definition: Matching and control are techniques employed in quasi-experimental designs to mitigate the threat of selection bias, which occurs when the sample is not representative of the studied population. These techniques assist in controlling for extraneous variables, enhancing the internal validity of the study.

Different Techniques for Matching and Control in Quasi-Experimental Designs:

• Propensity Score Matching: Propensity score matching is a statistical technique that balances treatment and control groups by using a numerical score predicting the likelihood of group assignment. Researchers use statistical models to compute propensity scores and then match participants in the treatment group with similar scores to those in the control group.
• Covariate Control: Covariate control involves including extraneous variables as covariates in the statistical analysis to control their effects. Researchers can incorporate demographic, background, and pre-existing characteristics as covariates to help account for their influence on the dependent variable.
• Block Randomization: Block randomization divides participants into blocks based on pre-existing characteristics (e.g., age, gender) and then randomly assigns participants within each block to the treatment and control groups. This technique aids in controlling the effects of these characteristics on the dependent variable.
• Restriction: Restriction involves limiting the recruited sample to specific participant criteria, reducing the impact of extraneous variables on the dependent variable.

How Matching and Control Reduce the Threat of Selection Bias in Quasi-Experimental Designs:

• By employing these techniques to match or control for extraneous variables, researchers can diminish the threat of selection bias in their study. Through controlling extraneous variables, researchers enhance the internal validity of their study and make more accurate causal inferences.
• For example, propensity score matching allows researchers to match participants in the treatment group with those in the control group who share similar characteristics. This helps balance the groups and reduces the risk of selection bias.
• By utilizing techniques like block randomization or restriction, researchers can limit the participant population or randomly assign participants within defined groups, thus mitigating the threat of selection bias.

Analysis in Experimental and Quasi-Experimental Design

Definition: Analysis in experimental and quasi-experimental designs involves the use of statistical techniques to explore the relationship between the independent and dependent variables and to test hypotheses regarding their causal relationship.

Different Types of Statistical Analyses Used in Experimental and Quasi-Experimental Designs:

• T-test: A t-test is a statistical test employed to determine if there is a significant difference between the means of two groups. It can be utilized in both experimental and quasi-experimental designs to compare the means of a treatment group and a control group.
• Analysis of Variance (ANOVA): ANOVA is a statistical test used to determine if there is a significant difference among the means of more than two groups. It can be employed in experimental and quasi-experimental designs to compare the means of multiple treatment groups and a control group.
• Multiple Regression: Multiple regression is a statistical technique used to examine the relationship between multiple independent variables and a single dependent variable. In experimental and quasi-experimental designs, it can be employed to explore the relationship between several independent variables and a single dependent variable while controlling for the effects of other variables.
• Propensity Score Analysis: Propensity score analysis is a statistical technique used to examine the relationship between an independent variable and a dependent variable while controlling for the effects of other variables. It is specifically utilized in the matching process of propensity score matching in quasi-experimental designs.
• Covariate Analysis: Covariate analysis is a technique used to control for extraneous variables by including them as covariates in the statistical analysis. Researchers can incorporate demographic, background, and pre-existing characteristics as covariates in their analyses to control for their effects on the dependent variable.

How to Properly Interpret the Results of These Analyses:

• For t-tests and ANOVA, researchers should examine the p-value, which indicates the probability that the results are due to chance. A p-value less than .05 is typically considered statistically significant, suggesting that the results are unlikely to be attributed to chance.
• For multiple regression and propensity score analysis, researchers should examine the coefficients of the independent variables, which represent the relationship between the independent variables and the dependent variable. A positive coefficient indicates a positive relationship, while a negative coefficient indicates a negative relationship.
• For all statistical analyses, it is important to consider the effect size, which represents the magnitude of the relationship between the independent and dependent variables. Effect sizes can be reported as Cohen's d, indicating the standardized mean difference between the treatment group and the control group, or as r-squared, indicating the proportion of variance in the dependent variable explained by the independent variables.
• In the case of quasi-experimental designs, careful attention should be given to how the groups were formed, how the researcher controlled for extraneous variables, and how the results are interpreted considering the limitations of the design.