Random Assignment Vs Matching

I know that textbooks tell us matching is an alternative to random assignment when it comes to quasi-experimental research. It allows us to make treatment and control group similar to each other.

However, another question arises: Can't I use matching not just for that but also to actually achieve quasi-independent(or non-manipulated if you will) variable?

For example, I want to see the effect of party membership on survey score. However, it's impossible to manipulate party membership variable in reality because of numerous reasons, so it should be regarded as a natural trait. If I wanna acquire treatment and control group which are similar to each other except for the party membership only, I randomly assign samples to treatment and control group, and the ask them their party membership, and then finally leave the same number of sample of each party membership category for both treatment and control group.

I tried to support or find a rationale for my idea, but I couldn't. Anybody has some knowledge or opinion on this? Please help.


After studying Chapter 7, you should know and understand the following key points:

Why Psychologists Conduct Experiments
Researchers conduct experiments to test hypotheses about the causes of behavior. Experiments allow researchers to decide whether a treatment or program effectively changes behavior.
Logic of Experimental Research
Researchers manipulate an independent variable in an experiment to observe the effect on behavior, as assessed by the dependent variable.
Experimental Control
Control is the essential ingredient of experiments; experimental control is gained through manipulation, holding conditions constant, and balancing. Experimental control allows researchers to make the causal inference that the independent variable caused the observed changes in the dependent variable. An experiment has internal validity when it fulfills the three conditions required for causal inference: covariation, time-order relationship, and elimination of plausible alternative causes. When confounding occurs, a plausible alternative explanation for the observed covariation exists, and therefore, the experiment lacks internal validity. Plausible alternative explanations are ruled out by holding conditions constant and balancing.
Random Groups Design
Random Assignment
In the random groups design, comparable groups of individuals are formed and the groups are treated the same in all respects except that each group receives only one level of the independent variable. The logic of the random groups design allows researchers to make causal inferences about the effect of the independent variable on the dependent variable. Random assignment to conditions is used to form comparable groups by balancing or averaging subject characteristics across the conditions of the independent variable manipulation. Block randomization balances subject characteristics and potential confoundings that occur during the time in which the experiment is conducted and creates groups of equal size.
Challenges to Internal Validity
Randomly assigning intact groups to different conditions of the independent variable creates a potential confounding due to pre-existing differences among participants in the intact groups. Block randomization increases internal validity by balancing extraneous variables across conditions of the independent variable. Whether extraneous variables are controlled by balancing or by holding conditions constant influences the external validity and sensitivity of an experiment. Subjective subject loss, but not mechanical subject loss, threatens the internal validity of an experiment. Placebo control groups are used to control for the problem of demand characteristics, and double-blind experiments control both demand characteristics and experimenter effects.
Analysis and Interpretation of Experimental Findings
The Role of Data Analysis in Experiments
Data analysis and statistics play a critical role in researchers' ability to make the claim that an independent variable has had an effect on behavior. The best way to determine whether the findings of an experiment are reliable is to replicate the experiment.
Describing the Results
The two most common descriptive statistics used to summarize the results of experiments are the mean and standard deviation. Measures of effect size indicate the strength of the relationship between the independent and dependent variables, but they are not affected by sample size. One commonly used measure of effect size, d, examines the difference between two group means relative to the average variability in the experiment. Meta-analysis uses measures of effect size to summarize the results of many experiments investigating the same independent variable or dependent variable.
Confirming What the Results Reveal
Researchers use inferential statistics to determine whether an independent variable has a reliable effect on a dependent variable. Two methods to make inferences about sample data are null hypothesis testing and confidence intervals. Researchers use null hypothesis testing to determine whether mean differences among groups in an experiment are greater than the differences that are expected simply because of error variation. A statistically significant outcome is one that has a small likelihood of occurring if the null hypothesis is true. Researchers determine whether an independent variable has had an effect on behavior by examining whether the confidence intervals for different samples in an experiment overlap.
Establishing the External Validity of Experimental Findings
The findings of an experiment have external validity when they can be applied to other individuals, settings, and conditions beyond the scope of the specific environment. In some investigations (e.g., theory-testing), researchers may choose to emphasize internal validity over external validity; other researchers may choose to increase external validity using sampling or replication. Partial replication is a useful method for establishing the external validity of research findings. Researchers often seek to generalize results about conceptual relationships among variables rather than specific conditions, manipulations, settings, and samples.
Alternative Independent Groups Designs
Matched Groups Design
A matched groups design may be used to create comparable groups when there are too few subjects available for random assignment to work effectively. Matching subjects on the dependent variable task is the best approach for creating matched groups, but performance on any matching task must correlate with the dependent variable task. After subjects are matched on the matching task, they should then be randomly assigned to the conditions of the independent variable.
Natural Groups Design
Individual differences variables (or subject variables) are selected rather than manipulated to form natural groups designs. The natural groups design represents a type of correlational research in which researchers look for covariations between natural groups variables and dependent variables. Causal inferences cannot be made regarding the effects of natural groups variables because plausible alternative explanations for group differences exist.

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