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| 1 | |
In a between-subjects design: |
| | A) | All subjects receive every level of the independent variable. |
| | B) | one or a few subjects are studied extensively at all levels of the independent variable. |
| | C) | each subject receives only one level of the independent variable. |
| | D) | data are analyzed by plotting the performances of single subjects. |
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| 2 | |
Within-subjects designs differ from between-subjects designs in that: |
| | A) | only one group of subjects is used. |
| | B) | only one subject participates at each level of the independent variable. |
| | C) | only variables "within" the subject are examined, such as heart rate. |
| | D) | All of the above |
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| 3 | |
Unwanted variations in environmental conditions and subject characteristics that affect your dependent variable: |
| | A) | do not affect the results of between-subjects designs. |
| | B) | constitute the independent variable. |
| | C) | produce error variance. |
| | D) | can be completely eliminated through proper experimental design. |
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| 4 | |
Error variance comes from: |
| | A) | differences between subjects in their characteristics. |
| | B) | moment-to-moment changes in subject characteristics. |
| | C) | environmental conditions that are not absolutely constant. |
| | D) | All of the above |
| | E) | Both a and b |
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| 5 | |
To deal with the effects of error variance in a between-subjects design, you can: |
| | A) | attempt to hold extraneous variables constant. |
| | B) | increase the effectiveness of your independent variable. |
| | C) | randomize error variance across groups. |
| | D) | All of the above |
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| 6 | |
You assess the reliability of effects in between-subjects designs by: |
| | A) | using inferential statistics. |
| | B) | comparing a single subject's performance across repeated exposures to the same level of the independent variable. |
| | C) | checking whether the samples adequately represent the population. |
| | D) | All of the above |
| | E) | None of the above |
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| 7 | |
A limitation of the randomized two-group design is that you: |
| | A) | cannot determine whether the independent variable has a reliable effect. |
| | B) | must use extremely large groups of subjects to detect an effect of your independent variable. |
| | C) | do not learn much about the function relating the independent and dependent variables. |
| | D) | cannot apply parametric inferential statistics to the data. |
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| 8 | |
Your between-subjects design manipulates a single variable across several quantitative levels. It would be described as a __________________________ design. |
| | A) | two-group |
| | B) | single-factor parametric |
| | C) | single-factor nonparametric |
| | D) | factorial |
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| 9 | |
The multiple control group design is used when: |
| | A) | a single control group is not adequate to rule out alternative explanations for your results. |
| | B) | the level of a drug is the independent variable. |
| | C) | a placebo control group would not be a good idea. |
| | D) | multiple t tests will be used to analyze the data. |
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| 10 | |
An advantage of using matching rather than simple random assignment to form your groups is that: |
| | A) | matching reduces error due to subject differences. |
| | B) | matching is easier to do than randomization. |
| | C) | matching eliminates any possible correlation between the scores in the two treatments. |
| | D) | All of the above |
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| 11 | |
A disadvantage of matched groups design is that: |
| | A) | you must take the extra step of matching subjects before assigning them to groups. |
| | B) | matching will improve the sensitivity of the experiment only if the matched characteristic has a relatively large effect on the dependent variable. |
| | C) | you must be sure that the instrument you use to determine the match is valid and reliable. |
| | D) | All of the above |
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| 12 | |
In a within-subjects experiment, you: |
| | A) | randomly assign subjects to different treatment groups. |
| | B) | examine the individual performance of a single subject across several treatments. |
| | C) | expose a single group of subjects to all the treatments. |
| | D) | ask participants to report what is going on "within" their minds. |
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| 13 | |
An advantage of the within-subjects design is that it: |
| | A) | tends to be more powerful than the equivalent between-subjects design. |
| | B) | eliminates the problem of carryover effects. |
| | C) | is less susceptible to confounding than other designs. |
| | D) | All of the above |
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| 14 | |
When using a within-subjects design (as opposed to a between-subjects design), you can often: |
| | A) | make do with fewer subjects. |
| | B) | increase the power of your experiment. |
| | C) | reduce error variance. |
| | D) | All of the above |
| | E) | Both a and c only |
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| 15 | |
A disadvantage of using a within-subjects design (as opposed to a between-subjects design) is that: |
| | A) | each subject must spend more time in the experiment. |
| | B) | carryover effects must be controlled for. |
| | C) | participant attrition may be a more serious problem. |
| | D) | All of the above |
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| 16 | |
If subjects are exposed to a treatment that alters the subjects' responses to a subsequent treatment, the data suffer from: |
| | A) | response differential. |
| | B) | carryover. |
| | C) | external validity. |
| | D) | internal validity. |
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| 17 | |
If you are told that break-ins are occurring in your neighborhood during the night, then house noises you used to ignore may now awaken you. This phenomenon is called: |
| | A) | adaptation. |
| | B) | habituation. |
| | C) | sensitization. |
| | D) | contrast. |
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| 18 | |
A completely counterbalanced design deals with order effects by: |
| | A) | averaging them out across treatments. |
| | B) | eliminating them. |
| | C) | including only some of the possible treatment orders, with the restriction that each treatment appear in each position an equal number of times. |
| | D) | reducing them to tolerable levels through instructions to the participants. |
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| 19 | |
If you make the number of treatment orders in your design equal to the number of treatments, you can use a _______________________ design to keep each treatment appearing an equal number of times at each ordinal position. |
| | A) | completely counterbalanced |
| | B) | nested |
| | C) | mixed |
| | D) | Latin square |
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| 20 | |
Counterbalancing can be counted on to control order effects only if: |
| | A) | the order effects induced by different orders are of different magnitudes. |
| | B) | the order effects induced by different orders are of the same approximate magnitude. |
| | C) | order effects are vanishingly small compared to the treatment effects. |
| | D) | adaptation or habituation is responsible for the order effects. |
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| 21 | |
The most serious asymmetry in carryover effects occurs when a treatment produces: |
| | A) | habituation. |
| | B) | fatigue. |
| | C) | sensitization. |
| | D) | irreversible changes. |
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| 22 | |
Making treatment order an independent variable enables you to: |
| | A) | eliminate carryover effects. |
| | B) | reduce the effects of sensitization and fatigue. |
| | C) | measure the size of any carryover effect. |
| | D) | decrease the number of subjects required by your design. |
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| 23 | |
To deal with carryover effects, you can: |
| | A) | counterbalance the order of treatments. |
| | B) | make treatment order an independent variable. |
| | C) | take steps to reduce carryover. |
| | D) | All of the above |
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| 24 | |
A strong carryover effect can be a problem with within-subjects designs. If you wish to retain some of the advantage of the within-subject design but must avoid carryover, you should substitute a ________________________ design. |
| | A) | factorial between-subjects design. |
| | B) | matched groups design. |
| | C) | randomized groups design. |
| | D) | correlational design. |
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| 25 | |
An experiment exposes all participants to five advertising styles (in counterbalanced order) and assesses desire for the product following each exposure. The experiment follows a _____________________ within-subjects design. |
| | A) | single-factor, multilevel |
| | B) | five-factor factorial |
| | C) | nested |
| | D) | multivariate |
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| 26 | |
In a factorial design, if the effect of Variable A (on the dependent measure) changes with the level of Variable B, then: |
| | A) | the results are hopelessly confounded. |
| | B) | an interaction is present. |
| | C) | there are no main effects of the two independent variables. |
| | D) | the dependent variable is insensitive. |
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| 27 | |
A serious problem with using factorial designs having a large number of factors is that: |
| | A) | they require a large number of subjects. |
| | B) | the complex interactions that may emerge are difficult to interpret. |
| | C) | there are no statistical analyses available for such designs. |
| | D) | Both a and b |
| | E) | Both b and c |
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| 28 | |
You plan to conduct a factorial experiment in which there are three levels of Factor A and four levels of Factor B. You intend to use 10 subjects per group. How many subjects will you need for the experiment? |
| | A) | 10 |
| | B) | 70 |
| | C) | 120 |
| | D) | 240 |
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| 29 | |
A multivariate design always includes: |
| | A) | several levels of the independent variable. |
| | B) | two or more dependent variables. |
| | C) | two or more independent variables. |
| | D) | All of the above |
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| 30 | |
The best way to deal with a counfounding variable is to: |
| | A) | use a multivariate design.. |
| | B) | use a within-subjects design. |
| | C) | choose the most powerful statistic possible. |
| | D) | exercise care when designing an experiment. |
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