Research methods for the behavioral sciences 6th edition gravetter solutions manual

Research Methods for the Behavioral Sciences 6th Edition Gravetter Solutions Manual Visit to download the full and correct content document: https://testbankdeal.com/dow nload/research-methods-for-the-behavioral-sciences-6th-edition-gravetter-solutions-m anual/

CHAPTER 8 EXPERIMENTAL DESIGNS: BETWEEN-SUBJECTS DESIGN ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ CHAPTER OUTLINE ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ 8.1

INTRODUCTION TO BETWEEN-SUBJECTS EXPERIMENTS Review of the Experimental Research Strategy Characteristics of Between-Subjects Designs Independent scores Advantages and Disadvantages of Between-Subjects Designs Individual differences

8.2

INDIVIDUAL DIFFERENCES AS CONFOUNDING VARIABLES Other Confounding Variables Equivalent Groups

8.3

LIMITING CONFOUNDING BY INDIVIDUAL DIFFERENCES Random Assignment (Randomization) Matching Groups (Matched Assignment) Holding Variables Constant or Restricting Range of Variability Summary and Recommendations

8.4

INDIVIDUAL DIFFERENCES AND VARIABILITY Differences Between Treatments and Variance Within Treatments Minimizing Variance Within Treatments Standardize procedures and treatment setting Limit individual differences Random assignment and matching Sample size Summary and Recommendations

8.5

OTHER THREATS TO INTERNAL VALIDITY OF BETWEEN-SUBJECTS EXPERIMENTAL DESIGNS Differential Attrition Communication Between Groups

© 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

8.6

APPLICATIONS AND STATISTICAL ANALYSES OF BETWEEN-SUBJECTS DESIGNS Two-Group Mean Difference Comparing Means for More Than Two Groups A word of caution about multiple-group designs Comparing Proportions for Two or More Groups

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ KEY WORDS ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ between-subjects experimental design, or independent measures experimental design

individual differences restricted random assignment matching

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ LEARNING OBJECTIVES ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ 1. Describe, compare, and contrast the defining characteristics of a between-subjects design and a within-subjects design, and recognize examples of each.

A between-subjects experimental design, also known as an independent measures experimental design, requires a separate, independent group of individuals for each treatment condition. As a result, the data for a between-subjects design contain only one score for each participant. To qualify as an experiment, the design must satisfy all other requirements of the experimental research strategy, such as manipulation of an independent variable and control of extraneous variables. In a within-subjects design, the same individuals are assessed at different times.

2. Explain the general advantages and disadvantages of between-subjects design compared to within-subjects design.

A main advantage of a between-subjects design is that each individual score is independent from the other scores. In addition, between-subjects designs can be used for a wide variety of research questions. For any experiment comparing two (or more) treatment conditions, it is always possible to assign different groups to the different treatments; thus, a between-subjects design is always an option. One disadvantage of between-subjects designs is that they require a relatively large number of participants.

The primary disadvantage of a between-subjects design stems from the fact that each score is obtained from a unique individual who has personal characteristics that are different from all of the other participants. © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

3. Define individual differences and explain how individual differences between groups and confounding from environmental variables can threaten the internal validity of a between subjects design.

Confounding can come from individual differences. Individual differences are any participant characteristics that can differ from one participant to another. If these characteristics are different from one group to another, then the experiment is confounded. For example, the participants in one group may be older, smarter, taller, or have higher socioeconomic status than the participants in another group. One group may have a higher proportion of males or a higher proportion of divorced individuals than another group. Any of these variables may produce differences between groups that can compromise the research results. Confounding can also come from environmental variables. Environmental variables are any characteristics of the environment that may differ. If these variables are different between groups, then the experiment is confounded by environmental variables. For example, one group may be tested in a large room and another group in a smaller room. Or one group may be measured primarily during the morning and another group during the afternoon. Any such variable may cause differences between groups that cannot be attributed to the independent variable.

4. Identify the three primary techniques for limiting confounding by individual differences in between-subjects experiments (random assignment, matched assignment, and holding variables constant) and explain how each one works.

Random assignment simply means that a random process (such as a coin toss) is used to assign participants to groups. The goal is to ensure that all individuals have the same chance of being assigned to a group. Because group assignment is based on a random process, it is reasonable to expect that characteristics such as age, IQ, and gender are also distributed randomly across groups. In many situations, a researcher can identify a few specific variables that are likely to influence the participants’ scores. In a learning experiment, for example, it is reasonable to expect that intelligence is a variable that can influence learning performance. In this case, it is important that the researcher not allow intelligence to become a confounding variable by permitting one group of participants to be noticeably more intelligent than another group. Instead of hoping that random assignment produces equivalent groups, a researcher can use matching to guarantee that the different groups of participants are equivalent (or nearly equivalent) with respect to intelligence. Another method of preventing individual differences from becoming confounding variables is simply to hold the variable constant. For example, if a researcher suspects that gender differences between groups might confound a research study, one solution is to eliminate gender as a variable. By using only female participants, a researcher can guarantee that all of the groups in a study are equivalent with respect to gender; all groups are all female. An alternative to holding a variable completely constant is to restrict its range of values. For example, a researcher concerned about potential IQ © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

differences between groups could restrict participants to those with IQs between 100 and 110. Because all groups have the same narrow range of IQs, it is reasonable to expect that all groups would be roughly equivalent in terms of IQ. 5. Describe how individual differences influence variability within-treatments and explain how variance within treatments can influence the interpretation of research results.

In addition to becoming confounding variables, individual differences have the potential to produce high variability in the scores within a research study. The problem comes from the fact that in some situations, a 10-point difference is large, but in other circumstances, a 10-point difference is small. The absolute size of the difference must be evaluated in relation to the variance of the scores. Variance is a statistical value that measures the size of the differences from one score to another. If the scores all have similar values, then the variance is small; if there are big differences from one score to the next, then variance is large. It may be helpful to think of the variance within each group as similar to interference to a cell phone or radio signal. When there is a lot of interference, it is difficult to get a clear signal. Similarly, when a research study has a lot of variance, it is difficult to see a real treatment effect. In between-subjects research, much of the variance is caused by individual differences. Remember, each individual score represents a different individual. Whenever there are large differences between individuals, there is large variance. Notice that we are distinguishing differences between treatments and variance (differences) within treatments. Researchers typically try to increase the differences between treatments and to decrease the variance within treatments. At the same time, however, we would like to decrease the variance within treatments. Because a between-subjects design has a separate group of participants for each treatment condition, the variance within treatments is also the variance within groups.

6. Identify the options for reducing or controlling the variance within treatment condition and explain how each option works.

The best techniques for minimizing the negative consequences of high variance are to standardize treatments and to minimize individual differences between the participants in the study. Both of these techniques help eliminate factors that can cause differences between scores and therefore can reduce the variance within treatments.

7. Describe how differential attrition and communication between participants can threaten the internal validity of between-subjects designs and identify these problems when they appear in a research study.

Differential attrition refers to differences in attrition rates from one group to another and can threaten the internal validity of a between-subjects experiment. Differential attrition is a threat to internal validity because we do not know whether the obtained differences between treatment conditions are caused by the treatments or by differential attrition. In © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

addition, whenever the participants in one treatment condition are allowed to talk with the participants in another condition, there is the potential for a variety of problems to develop. One such problem is diffusion. Diffusion refers to the spread of the treatment from the experimental group to the control group, which tends to reduce the difference between the two conditions. This is a threat to the internal validity of a between-subjects design because the true effects of the treatment can be masked by the shared information (i.e., it appears that there is no difference between the groups because both groups are actually getting much of the same treatment). Another risk is that an untreated group learns about the treatment being received by the other group, and demands the same or equal treatment. This is referred to as compensatory equalization. Problems can also occur when participants in an untreated group change their normal behavior when they learn about a special treatment that is given to another group. One possibility is that the untreated group works extra hard to show that they can perform just as well as the individuals receiving the special treatment. This is referred to as compensatory rivalry.

Finally, resentful demoralization can also occur, in which participants in the untreated group become less productive and unmotivated because they think the researcher is expecting some better result from the treated group.

8. Describe how between-subjects designs are used to compare means and proportions for two or more groups, identify the statistical techniques that are appropriate for each application, and explain each design’s strengths and weaknesses.

When the measurements consist of numerical scores, typically, a mean is computed for each group of participants, and then an independent-measures t-test is used to determine whether there is a significant difference between the means. With designs that have more than two groups, the mean is computed for each group of participants, and a single-factor analysis of variance (ANOVA) (independent measures) is used to determine whether there are any significant differences among the means.

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ OTHER LECTURE SUGGESTIONS ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ 1. In between-subjects experiments, random assignment of participants to treatments is essential to minimize the threat of confounding from individual differences between groups. However, random assignment does not guarantee equivalent groups; instead, you are trusting chance to spread participant characteristics evenly across the treatment conditions.

2. Note that either holding a variable constant or matching a variable across treatments will guarantee that the variable will not be confounding. Randomization, on the other hand, usually works but offers no guarantee.

3. In addition to the activities presented at the end of the chapter, the following can be used as an in-class activity for this chapter: © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

Randomly select two samples, each about n = 5, from the students in your class. Pick one variable with small individual differences, such as height or age (if you have traditional students), and one variable with large individual differences, such as weight or the number of text messages received on the previous day. For each variable, compute the mean for each group and compare the two distributions of scores. You should find similar means and similar distributions. Next, add a l0-point “treatment effect” to the scores in one group and compare the two groups again. For the small variable with small individual differences, there should now be a clear difference between groups. However, the variable with large individual differences will probably not show noticeable differences between groups.

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ NOTES ON END-OF-CHAPTER EXERCISES ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ 1. In addition to the key words, you should also be able to define the following terms: Within-subjects design: A research design in which the different groups of scores are all obtained from the same group of participants. Also known as repeated-measures design.

Between-subjects design: A research design in which each of the different groups of scores is obtained from a separate group of participants. Also known as an independentmeasures design.

Independent-measures design: A research design in which each of the different groups of scores is obtained from a separate group of participants.

Random assignment: A procedure in which a random process is used to assign participants to treatment conditions.

Variance within treatments, or variance within groups: A measure of the differences between scores for a group of individuals who have all received the same treatment. The intent is to measure naturally occurring differences that have not been caused by a treatment effect. Also known as variance within groups.

Differential attrition: A threat to internal validity that occurs when attrition in one group is systematically different from the attrition in another group.

Diffusion: A threat to internal validity that occurs when a treatment effect spreads from the treatment group to the control group, usually from participants talking to each other.

Compensatory equalization: A threat to internal validity that occurs when an untreated group demands to receive a treatment that is the same as or equivalent to the treatment received by another group in the research study. © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

Compensatory rivalry: A threat to internal validity that occurs when an untreated group learns about special treatment received by another group, then works extra hard to show they can perform just as well as that group.

Resentful demoralization: A threat to internal validity that occurs when an untreated group learns of special treatment given to another group, and becomes less productive and less motivated because they resent the other group’s expected superiority.

Single-factor two-group design, or two-group design: A research design comparing two groups of participants or two groups of scores representing two levels of a factor.

Also known as the two-group design.

Single-factor multiple-group design: A research design comparing more than two groups of participants (or groups of scores) representing more than two levels of the same factor.

2. (LO1) At the beginning of this chapter (p. 186), we described a study comparing the effectiveness of studying material printed on paper to studying material displayed on a computer screen (Ackerman & Goldsmith, 2011). Explain why this study is an example of a betweensubjects design and describe how the same question could be addressed with a within-subjects design.

This study is an example of between-subjects design because the researchers compared scores from two different groups of participants. This study could be done as a withinsubjects design by using one group of participants. That single group of participants would be asked to take two quizzes—one after studying on paper and one after studying on a computer. The two scores from the same group would then be compared.

3. (LO2) In a between-subjects design, each individual score is obtained from a separate participant. a. Briefly explain why this is an advantage. Because each participant is measured only once, the researcher can be reasonably confident that the resulting measurement is relatively clean and uncontaminated by other treatment factors. b. Briefly explain why this is a disadvantage. One disadvantage of between-subjects designs is that they require a relatively large number of participants. Remember, each participant contributes only one score to the final data. This can be a problem for research involving special populations in which the number of potential participants is relatively small.

4. (LO2) A researcher has a sample of 30 rats that are all cloned from the same source. The 30 rats are genetically identical and have been raised in exactly the same environment since birth. © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

The researcher conducts an experiment, randomly assigning 10 of the clones to treatment A, 10 to treatment B, and the other 10 to treatment C. Explain why the clone experiment is better than a between-subjects study using 30 regular rats that are randomly assigned to the three treatments.

In other words, explain how the clone experiment eliminates the basic problems with a betweensubjects study.

Using the cloned rats eliminates the problem of individual differences, which can become a confounding variable in a between-subjects study and can also produce such variability in the scores that it becomes difficult to determine whether a treatment has had an effect.

In addition, if the rats are treated in the same identical environments in which they have been raised, that also eliminates the second basic problem of a between-subjects study, which is the possibility for environmental variables to become confounding variables.

5. (LO3) Briefly explain how a participant characteristic, such as personality, could be a confounding variable in a between-subjects experiment.

Differences (such as gender, age, personality, and family background) that exist between participants at the beginning of an experiment are called preexisting individual differences, or simply individual differences. The concern with individual differences is that they can cause two different individuals to produce two different scores when a dependent variable is measured in a research study.

6. (LO4) Explain the advantages and disadvantages of using random assignment as a method to prevent individual differences from becoming confounding variables.

The advantage of using random assignment is that it helps establish groups without letting prejudice or bias enter the assignment process; however, while random assignment will produce equal groups in the long run, in the short run, there are no guarantees that the groups will be equal.

7. (LO3 and 4) A recent survey at a major corporation found that employees who regularly participated in the company fitness program tended to have fewer sick days than the employees who did not participate. However, because the study was not a true experiment, you cannot conclude that regular exercise causes employees to have fewer sick days. a. Identify another factor (a confounding variable) that might explain why some employees participated in the fitness program, and why those same employees had fewer sick days. One other possibility is that the people who participated in the fitness program were healthier already, and therefore did not need to take as many sick days because they already got sick less often than the unhealthy employees. b. Describe the design for a between-subjects experiment that would determine whether participation in the exercise program caused fewer sick days. You could assign the employees to two groups. Group A would then start participating in the fitness © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

program, and Group B would not. After a predetermined amount of time, you could compare the number of sick days taken by each group to see if there is a significant difference between the groups. c. Describe how the factor you identified in Part A could be controlled in your experiment. To control the variable of overall health, you could either distribute the healthy and unhealthy people equally in each group, or you could eliminate overall health as a variable by only including healthy people in your study.

8. (LO5) Describe how individual differences can produce large variance within treatments and explain why this is a problem in a between subjects experiment.

Since each score in a between-subjects study represents an individual, if there are major individual differences among a population, the variance between scores can be very large.

Large variances within treatments are bad because the differences that exist inside the treatment conditions determine the variance of the scores, and a large variance can obscure patterns in the data.

9. (LO4 and 6) Explain how holding a participant variable such as gender constant prevents the variable from becoming a confounding variable and can help reduce the variance within treatments. Identify the problem with using this method.

One method of preventing individual differences from becoming confounding variables is simply to hold the variable constant. For example, if a researcher suspects that gender differences between groups might confound a research study, one solution is to eliminate gender as a variable. By using only female participants, a researcher can guarantee that all of the groups in a study are equivalent with respect to gender—all groups are all female. An alternative to holding a variable completely constant is to restrict its range of values. For example, a researcher concerned about potential IQ differences between groups could restrict participants to those with IQs between 100 and 110. Because all groups have the same narrow range of IQs, it is reasonable to expect that all groups would be roughly equivalent in terms of IQ. The drawback of this strategy is that it threatens the external validity, or the ability to generalize the results to the general population.

10. (LO7) Describe some of the problems that can arise when the participants in one treatment condition of a between-subjects experiment are allowed to communicate with participants in a different condition.

Whenever the participants in one treatment condition are allowed to talk with the participants in another condition, there is the potential for a variety of problems to develop. One such problem is diffusion. Diffusion refers to the spread of the treatment from the experimental group to the control group, which tends to reduce the difference between the two conditions. This is a threat to the internal validity of a between-subjects © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

design because the true effects of the treatment can be masked by the shared information (i.e., it appears that there is no difference between the groups because both groups are actually getting much of the same treatment). Another risk is that an untreated group learns about the treatment being received by the other group, and demands the same or equal treatment. This is referred to as compensatory equalization. Problems can also occur when participants in an untreated group change their normal behavior when they learn about a special treatment that is given to another group. One possibility is that the untreated group works extra hard to show that they can perform just as well as the individuals receiving the special treatment. This is referred to as compensatory rivalry.

Finally, resentful demoralization can also occur, in which the participants in the untreated group “give up,” becoming less productive and motivated because they resent the expected superiority of the treated group.

11. Describe the advantages of a two-group design compared to an experiment with more than two groups.

The primary advantage of a two-group design is its simplicity. It is easy to set up a twogroup study, and there is no subtlety or complexity when interpreting the results; either the two groups are different or they are not. In addition, a two-group design provides the best opportunity to maximize the difference between the two treatment conditions; that is, you may select opposite extreme values for the independent variable. This technique increases the likelihood of obtaining noticeably different scores from the two groups, thereby demonstrating a significant mean difference.

12. Identify the advantages of a multiple-group design compared to an experiment with only two groups.

In addition to revealing the full functional relationship between variables, a multiplegroup design also provides stronger evidence for a real cause-and-effect relationship than can be obtained from a two-group design. With a multiple-group design, the researcher changes the treatment conditions (independent variable) several times across several groups, demonstrating differences in performance for each different treatment condition.

By contrast, a two-group design changes the treatment condition only once and observes only one difference in performance.

⎯⎯⎯⎯⎯⎯⎯⎯⎯ WEB RESOURCES ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Research Randomizer: Free Random Sampling and Random Assignment: http://www.randomizer.org Between Subjects Designs, from Explorable Psychology Experiments: © 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

https://explorable.com/between-subjects-design Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2827892/

© 2019 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.