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28 Cards in this Set

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The results of a‭ ‬research study indicate that stress inoculation is most effective for‭ ‬people with mild to moderate anxiety,‭ ‬while pharmacotherapy‭ ‬plus stress inoculation‭ ‬is most effective for people with severe anxiety.‭ ‬In this situation,‭ ‬level of‭ ‬anxiety is a:
moderator variable

In the study described in this question, there is an interaction between type of therapy and level of anxiety.

A moderator variable influences the nature of the relationship between an IV and DV. In this situation, level of anxiety affects (moderates) the relationship between type of therapy and therapy outcome.
When using a‭ "‬multiple-baseline‭" ‬design,‭ ‬a researcher will:
sequentially apply a treatment to different behaviors.

The multiple-baseline design is a type of single-subject design.

When using the multiple-baseline design, a treatment is sequentially applied to different baselines – i.e., to different behaviors, to the same behavior in different settings, or to the same behavior performed by different individuals. Unlike a "reversal" design (apply and then withdraw a treatment multiple times), the multiple-baseline design does not require withdrawal of the treatment during the course of the study.
If a teacher adds‭ ‬10‭ ‬points to each score in a distribution of scores,‭ ‬this will:
affect the mean of the distribution but not its standard deviation or variance.

For the exam, you want to be familiar with the effects of adding or subtracting a constant to each score in a distribution or multiplying or dividing each score by a constant.

a. CORRECT Adding a constant to each score in the distribution affects the distribution's mean (the new mean is the original mean plus the constant). However, adding a constant does not affect the variability of scores – i.e., it does not change the range, standard deviation, or variance.
When a distribution of scores is normally shaped,‭ ‬approximately what percent of scores fall between a z-score of‭ ‬0‭ ‬and a z-score of‭ ‬+1.0‭?
34

A z-score of 0 is equivalent to the mean score and a z-score of +1.0 is equivalent to the score that is one standard deviation above the mean.

When a distribution is normally shaped, approximately 34% of scores fall between a z-score of 0 and a z-score of +1.0 (i.e., between the mean score and the score that is one standard deviation above the mean).

In a normal distribution, approximately 68% of scores fall between z-scores of -1.0 and +1.0.
A bivariate correlation coefficient of‭ ‬.60‭ ‬indicates‭ ________ ‬times as much shared variability between variables as does a bivariate correlation coefficient of‭ ‬.30.
4

The correlation coefficient is squared to determine the amount of shared variability.

A correlation coefficient of .60 indicates that 36% of variability is shared variability, while a correlation of .30 indicates that 9% of variability is shared variability. Since 36% is four times larger than 9%, this means that a coefficient of .60 indicates four times as much shared variability as does a coefficient of .30.
A factorial design:
always includes two or more independent variables.

The term “factor” in factorial design refers to independent variables.

A factorial research design is any design that includes two or more “factors” (independent variables).
The magnitude of the standard error of the mean‭ ‬increases as:
the sample size decreases and the population variance increases.

Knowing the formula for the standard error of the mean would have helped you identify the correct answer to this question.

The standard error of the mean is equal to the population standard deviation divided by the square root of the sample size. This formula indicates that, as the population standard deviation increases and/or the sample size decreases, the standard error increases in magnitude.
You would use the Solomon four-group design in order to:
evaluate the impact of pretesting.

The Solomon four-group design combines the pretest-posttest control group design with the posttest only control group design.

The purpose of the Solomon four-group design is to evaluate the impact of pretesting on a study’s internal and external validity.
On the basis of the results of the t-test a psychologist uses to analyze the data she collects,‭ ‬the psychologist concludes that her results are‭ "‬significant at the‭ ‬.01‭ ‬level.‭" ‬This means that:
there is a‭ ‬1%‭ ‬chance that she will incorrectly reject the null hypothesis.

Significance at the .01 level means that there is a 1% chance that the obtained value (e.g., the mean or the difference between means) could have occurred by chance alone given the value specified in the null hypothesis. In other words, there is a 1% probability that the null hypothesis will be incorrectly rejected (that a Type I error will be made).

The probability of incorrectly retaining the null hypothesis (i.e., of retaining a false null hypothesis) is equal to beta, not alpha.

Because the results are statistically significant, the psychologist will reject (not retain) the null hypothesis.
A researcher reports that she calculated a Cohen’s d of‭ ‬.50‭ ‬for the data she collected in a study that compared two brief treatments for generalized anxiety disorder.‭ ‬This means that:
there was a difference of one-half standard deviation between the means of the two groups.

Cohen’s d is a measure of effect size. It indicates the difference between the means of two groups in terms of standard deviations.

A Cohen’s d of .50 indicates that one group obtained a mean that is one-half standard deviation higher than the mean obtained by the other group.
A psychologist‭ ‬compares the effectiveness of three brief interventions‭ ‬for obsessive-compulsive disorder by randomly assigning adults who have received this diagnosis to one of the three interventions and measuring their symptoms prior to the beginning of treatment and one week,‭ ‬one month,‭ ‬and six months following treatment.‭ ‬The psychologist is using which of the following types of research design‭?
mixed

The study described in this question has two independent variables – type of treatment and time.

Type of treatment is a between-groups variable in this study since each participant is assigned to one of the three interventions. In contrast, time is a within-subjects variable since all participants are being evaluated on the dependent variable at four different times. When a study includes both between-groups and within-subjects variables, it is referred to as a mixed design.
If your data analysis involves calculating an‭ "‬effect size,‭” ‬you are conducting which of the following‭?
meta-analysis

Meta-analysis is used to combine the results of multiple studies. For example, a meta-analysis might be conducted to combine the results of several different studies that investigated the effects of cognitive therapy on depression. Use of this technique involves calculating an effect size for each study and then calculating a mean effect size for all of the studies.
Research participants act as their own no-treatment‭ “‬controls‭" ‬in which of the following types of research‭?
single subject

In research, a control group is a comparison group that does not receive treatment or, alternatively, receives a standard treatment.

In single-subject research, a participant’s behavior during the baseline (no treatment) and treatment phases is compared. Thus, when using single-subject designs, participants act as their own no-treatment controls.
You would use the Solomon four-group design in order to:
evaluate the impact of pre-testing
The Solomon four-group design combines the pretest-posttest control group design with the posttest only control group design.
The purpose of the Solomon four-group design is to evaluate the impact of pretesting on a study’s internal and external validity.
Parametric statistical tests are usually preferable to non-parametric tests because they are more‭ "‬powerful.‭" ‬This means that the use of a parametric test to analyze the data collected in a research study helps ensure that:
a false null hypothesis will be rejected.

Statistical power is the ability to detect a false null hypothesis.

a false null hypothesis will be retained is a Type II error
To analyze the relationship between gender and‭ ‬Holland’s six occupational themes,‭ ‬the appropriate statistical test would be which of the following‭?
multiple sample chi-square

The study described in this question has one independent variable (gender) and one dependent variable (Holland’s six occupational themes), and the dependent variable is measured on a nominal scale.

The multiple-sample chi-square test is appropriate for studies that include two or more variables and the data to be analyzed represent a nominal scale.

The single-sample chi-square test is appropriate for descriptive studies that include a single nominal variable.
Autocorrelation is most likely to be a problem when using which of the following research designs‭?
time-series

Autocorrelation refers to the correlation between measurements of the dependent variable when it is repeatedly administered to the same participants. Autocorrelation is a problem in repeated measures designs because it can artificially inflate the value of the inferential statistic and thereby increase the probability of making a Type I error.

Of the research designs listed in the answers, repeated measurement of the dependent variable is characteristic only of the time series design, which is a type of repeated measures (within subjects) design.
When using stepwise multiple regression,‭ ‬the addition of predictors to the equation is usually based on:
the magnitude of R-squared.

The types of multiple regression include simple, hierarchical, and stepwise. As its name implies, stepwise regression involves adding (or subtracting) predictors to the multiple regression equation one at a time.

When using multiple regression, each predictor is retained in the equation as long as it contributes significantly to the total amount of variability in the criterion that is explained by the combined predictors. The proportion of explained variability is measured by R-squared.
To reduce the likelihood that experimenter expectancy will bias the results of a research study,‭ ‬you would use which of the following techniques‭?
double-blind

Experimenter expectancy refers to the bias that results when the experimenter’s expectations about the outcomes of the study influence the actual outcomes (e.g., as the result of cues the experimenter inadvertently provides participants).

When a double-blind procedure is used, research participants and the experimenter are “blind” to the experimental conditions – i.e., they do not know what group participants are in. Because the researcher does not know whether individual participants are in the experimental or control group, this reduces the likelihood that he/she will provide cues to participants that could bias the results of the study.

In a single-blind study, the participants do not know what group they are in. This procedure would not eliminate experimenter expectancies.
To increase statistical power,‭ ‬you would:
increase alpha from‭ ‬.01‭ ‬to‭ ‬.05.

Power refers to the ability to detect (reject) a false null hypothesis.

The ability to reject the null hypothesis is increased by increasing alpha. When the null hypothesis is, in fact, false, increasing the size of alpha also increases power.

Parametric tests are more powerful than are nonparametric tests.
When the relationship between the predictor‭ (‬the X variable‭) ‬and the criterion‭ (‬the Y variable‭) ‬is curvilinear and both variables are measured on an interval or ratio scale,‭ ‬the appropriate correlation coefficient is:
eta

The choice of a correlation coefficient is based on several factors including the scale of measurement of the variables and the shape of the relationship between them (linear vs. non-linear).

Eta is used to measure the relationship between two continuous (interval or ratio) variables when their relationship is nonlinear.

Rho (also known as the Spearman rank-order correlation coefficient) is used when both variables are measured as ranks.
The denominator term in the F-ratio is‭ ‬reduced in magnitude by:
decreasing within-group variability

The mean square within (MSW) is the denominator of the F-ratio and, as its name implies, is a measure of within-group variability.

Within-group variability is a measure of error; and decreasing within-group variability decreases error and the magnitude of the denominator of the F-ratio.
If a teacher adds‭ ‬10‭ ‬points to each score in a distribution of scores,‭ ‬this will:
affect the mean of the distribution but not its standard deviation or variance.

For the exam, you want to be familiar with the effects of adding or subtracting a constant to each score in a distribution or multiplying or dividing each score by a constant.

a. CORRECT Adding a constant to each score in the distribution affects the distribution's mean (the new mean is the original mean plus the constant). However, adding a constant does not affect the variability of scores – i.e., it does not change the range, standard deviation, or variance.
To analyze the data collected in a study that included a single independent variable and three dependent variables,‭ ‬a researcher would use a MANOVA‭ (‬multivariate analysis of variance‭) ‬rather than three separate one-way ANOVAs because:
The MANOVA is used to simultaneously assess the effects of one or more independent variables on two or more dependent variables.

If three separate ANOVAs were conducted at a level of significance of .05 each, the "experimentwise error rate" (probability of making a Type I error) would be increased to approximately .15. By conducting a single MANOVA at the .05 level, the experimentwise error rate remains at .05.
A factorial design:
always includes two or more independent variables.

The term “factor” in factorial design refers to independent variables.

A factorial research design is any design that includes two or more “factors” (independent variables).
When a distribution of scores is normally shaped,‭ ‬approximately what percent of scores fall between a z-score of‭ ‬0‭ ‬and a z-score of‭ ‬+1.0‭?
34

A z-score of 0 is equivalent to the mean score and a z-score of +1.0 is equivalent to the score that is one standard deviation above the mean.

When a distribution is normally shaped, approximately 34% of scores fall between a z-score of 0 and a z-score of +1.0 (i.e., between the mean score and the score that is one standard deviation above the mean).
A researcher would use an ABAB design rather than an AB design in order to control which of the following threats to his study’s validity‭?
history

An ABAB design is a single-subject design that involves collecting baseline data, administering the independent variable, removing the independent variable, and then readministering the independent variable.

The use of an ABAB design helps to determine whether or not an observed change in the dependent variable is due to the independent variable or to an external event (history). Specifically, if the participant exhibits the same change after the independent variable is administered the second time that he/she exhibited after it was administered the first time, that change can be assumed to have been caused by the independent variable rather than by history.