Question 1

Describe the defining characteristics of a true experiment.

Accepted Answer

A true experiment is characterized by several defining characteristics. These include the presence of an independent variable that is manipulated by the researcher, a control group that does not receive the manipulation, a random assignment of participants to different conditions, and the ability to establish cause-and-effect relationships between the independent variable and the dependent variable. Additionally, true experiments often involve the use of a pre-test and post-test to measure changes in the dependent variable, as well as the use of statistical analysis to determine the significance of the results. Overall, the defining characteristics of a true experiment allow researchers to make strong conclusions about the effects of the independent variable on the dependent variable.

Question 2

When conducting a true experiment, how can you ensure that all participants are equivalent with respect to the dependent variable before they take part in the study?

Accepted Answer

To ensure that all participants are equivalent with respect to the dependent variable before they take part in a true experiment, researchers employ several strategies:

1. Random Assignment: This is the most critical technique used to ensure equivalence among participants. By randomly assigning participants to either the control or experimental groups, researchers can assume that each group is statistically similar at the start of the experiment. Random assignment helps to balance out any individual differences across the groups.

2. Matching: In some cases, researchers may use matching to pair participants in the experimental and control groups based on certain characteristics that are relevant to the study. This can help control for those characteristics and make the groups more equivalent with respect to the dependent variable.

3. Pretesting: Conducting a pretest on all participants before the experiment begins can provide baseline data on the dependent variable. This allows the researcher to check for initial equivalence and to statistically control for any differences that might exist.

4. Control Groups: Using a control group that does not receive the experimental treatment allows researchers to compare outcomes against a standard baseline. This helps to determine the effect of the independent variable on the dependent variable.

5. Covariate Analysis: If there are known variables that might influence the dependent variable, researchers can measure these covariates and use statistical methods to control for their impact. Techniques such as analysis of covariance (ANCOVA) can adjust the effects of the independent variable on the dependent variable by accounting for variance associated with the covariates.

6. Standardization of Procedures: Ensuring that all participants undergo the same procedures and conditions (except for the experimental manipulation) helps to maintain equivalence. This includes standardizing instructions, environment, and timing of the experiment.

7. Blind or Double-Blind Designs: In a blind experiment, participants do not know whether they are in the control or experimental group, which helps to prevent bias. In a double-blind experiment, neither the participants nor the researchers know who is in which group until after the data is collected, further reducing potential biases.

By employing these strategies, researchers can increase the likelihood that any observed differences in the dependent variable after the experimental manipulation are due to the independent variable and not pre-existing differences between the groups.

Question 3

What are parametric statistics? Give an example.

Accepted Answer

Parametric statistics refer to a branch of statistics that assumes that the data has a specific type of distribution, usually a normal distribution, and that the parameters of this distribution—such as the mean and the variance—are known or can be estimated from the data. These methods rely on certain assumptions about the underlying population from which the sample data are drawn.

The key feature of parametric statistics is the use of parameters (hence the name) to define the distribution of the data. These parameters are numbers that summarize the characteristics of the entire population from which the samples are taken. For example, in a normal distribution, the parameters are the mean (average) and the standard deviation (a measure of spread).

Parametric statistical methods are powerful because they allow for making inferences and predictions about the population, and they can be more efficient (requiring smaller sample sizes) than non-parametric methods when the assumptions hold true. However, if the assumptions are violated, the results can be misleading or incorrect.

An example of parametric statistics is the use of a t-test to determine whether there are significant differences between the means of two groups. For instance, researchers might want to test whether a new teaching method leads to better test scores than a traditional method. They would collect scores from a group of students taught with the new method and another group taught with the traditional method. The t-test would allow the researchers to determine if the difference in the average scores between the two groups is statistically significant, assuming that the scores are normally distributed and that other assumptions of the t-test are met.

Another example is linear regression, where the relationship between a dependent variable and one or more independent variables is modeled. The parameters in this case are the regression coefficients that represent the change in the dependent variable for a one-unit change in an independent variable, assuming that the relationship between the variables is linear.

In summary, parametric statistics are a set of statistical techniques based on assumptions about the distribution of the underlying population data. They are used to make inferences or predictions about population parameters, and they are most effective when the assumptions on which they are based are valid.

Question 4

When inferential statistics are used, into what categories is the variance on a dependent variable partitioned? How are these categories compared statistically?

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Question 5

According to your text, conducting a true experiment can rule out the possibility that differences between the experimental and control group were due to factors other than the independent variable. Describe the two of these factors.

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Question 6

What are nonspecific treatment effects? Give an example.

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Question 7

Describe how a manipulation check can be used to demonstrate the convergent validity of the manipulation of an independent variable.

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Question 8

Describe two types of information that researchers can collect and examine as part of a manipulation check.

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Question 9

Describe four possible reasons why an experimental manipulation might fail.

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Question 10

What is satisficing? What procedures can a researcher use to test whether this factor is influencing participants' responses?

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Question 11

Name and define three characteristics of a good manipulation of an independent variable.

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Question 12

Describe a study, based on your own interests, that uses a good experimental manipulation. What factors does your manipulation have that suggest it would be effective?

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Question 13

What is simple random assignment to conditions of an independent variable? Why is it a central aspect of a true experiment?

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Question 14

What is quasi-random assignment to conditions of an independent variable? Why is it a questionable procedure?

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Question 15

What is matched random assignment to conditions of an independent variable? Under what conditions is it most likely to be useful in controlling extraneous variance?

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Question 16

What is extraneous variance? Describe two ways it can be reduced in a true experiment.

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Question 17

Distinguish between within- and between-subjects designs. Describe two advantages and two disadvantages of each type of design.

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Question 18

Distinguish between practice effects and carry over effects in within-subjects designs.

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Question 19

Name and define the three types of order effects that can influence participants' responses in a within-subjects design.

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Question 20

What are sensitization effects and how might they result in experimental demand?

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Quiz 9: True Experiments