Exam #1 Flashcards
- During a research study the participants are able to guess the research hypothesis, causing them to behave differently than they would under normal conditions. This phenomenon is due to:demand characteristics
the Hawthorne effect
the use of a quasi-experimental design
the use of psychic research participants
The Correct Answer is “A”
Demand characteristics are cues in a research study that allow participants to guess the hypothesis. As a result, participants may behave differently than they would under normal conditions. The Hawthorne effect (B) is a similar phenomenon, but refers to the tendency of research participants to behave differently due to the mere fact they are participating in research – rather than due to cues about how they are expected to behave. Quasi-experimental designs (C) are simply designs which do not randomly assign participants to groups. Finally, Choice D is a possible, but less probable, cause of this phenomenon.
- A factorial design, unlike a two group design:allows more independent variables to be studied
requires a larger sample
shows the effect of an independent variable on the dependent variable
cannot detect a curvilinear relationship between variables
The Correct Answer is “A”
A. In a two group design, one group is exposed to a treatment and another, control group, is not exposed or gets a different treatment. The results of both groups are tested in order to compare the effects of treatment. A factorial design is a design with more than one independent variable. In this design, the independent variables are simultaneously investigated to determine the independent and interactive influence they have on the dependent variable. The effect of each independent variable on the dependent variable (c.) is called a main effect and in a factorial design there are as many main effects as there are independent variables. An interaction effect between two or more independent variables occurs when the effect that one independent variable has on the dependent variable depends on the level of the other independent variable. At least three levels must be used to predict a curvilinear relationship (d.).
- A psychologist believes that physical exercise can reduce a person’s anxiety level, which reduces the strength of substance cravings in people recovering from substance dependence. According to this hypothesis anxiety is a:suppressor variable
mediator variable
moderator variable
criterion contaminator
The Correct Answer is “B”
A mediator variable is a variable that accounts for or explains the effects of an IV on a DV. That is, the IV affects the mediator variable, which affects the DV. In this example, the IV is exercise, the mediator variable is anxiety, which explains how the DV, substance craving, is reduced. A moderator variable (C) is similar to a mediator variable, but a moderator variable only influences the strength of the relationship between two other variables, it doesn’t fully account for it. For example, if a job selection test has different validity coefficients for different ethnic groups, ethnicity would be a moderator variable because it influences the relationship between the test (predictor) and actual job performance (the criterion) but it does not fully account for the relationship. A suppressor variable (A) reduces or conceals the relationship between variables. For example, the K scale in the MMPI-2 is a suppressor variable because it measures defensiveness, which can suppress the scores on the clinical scales. The K scale is, therefore, used as a correction factor for some of the clinical scales. Criterion contamination (D) is the artificial inflation of validity which can occur when raters subjectively score ratees on a criterion measure after they have been informed how the ratees scored on the predictor.
- The exam score and the ______________ are necessary to calculate the 68% confidence interval for an examinee’s obtained test score.
standard deviation standard error of measurement standard error of estimate test’s mean
The Correct Answer is “B”
B. Adding and subtracting one standard error of measurement to and from the examinee’s obtained test score yields a 68% confidence interval. The standard error of measurement (calculated from the test’s standard deviation and reliability coefficient) is needed to determine a confidence interval around an obtained test score. While the standard deviation (a.) is needed to calculate the standard error of measurement, it cannot be used to determine a confidence interval by itself and the standard error of estimate (c.) is used to construct a confidence interval around a predicted criterion score.
- All of the following are norm-referenced scores except:pass/fail
grade-equivalent scores
T-score
percentile rank
The Correct Answer is “A”
A. Norm-referenced scores indicate how well an individual performed on a test compared to others in the norm group. A pass or fail score achieved by one individual does not indicate how many others passed or failed. Pass/fail is a criterion-referenced score, which indicates if an individual knows the exam content or not, but does not measure performance relative to other examinees. The other three responses are norm-referenced scores. A grade-equivalent score (b.) permits a test user to compare an individual’s exam performance to others in different grade levels. A T-score (c.) is a type of standard score, or norm-referenced scores indicating how a test-taker performed in terms of standard deviation units from the mean score of the norm group. A percentile rank (d.) shows the percent of individuals in the norm group who scored lower.
- Jose scored 75 on his final exam. The test scores were normally distributed, with a mean of 60 and a standard deviation of 15. Jose’s score would be in which of the following percentile ranges?
35–49 50–64 65–79 80–95
The Correct Answer is “D”
D. In a normal distribution, 1.0 is 34 percentile points above the mean of 50. Jose’s standard score is (75-60)/15 or 1.0, putting his score at the 84th percentile.
- The eta correlation ratio would be used to
estimate the strength of a nonlinear relationship. measure the relationship between two dichotomous variables. estimate the strength of a relationship between a dichotomous variable and a quantitative variable. measure the relationship of variables measured by ranks.
The Correct Answer is “A”
There are a number of different types of correlation coefficients. The most common is the Pearson r, which is used to measure the relationship between two quantitative variables assumed to be related in a linear way. When a nonlinear relationship is assumed, the eta coefficient can be used instead. A linear relationship is one where as the value of one variable increases the other increases (positive correlation), or where as the value of one variable increases the other decreases (negative correlation). For example, the correlation between height and weight will be positive and linear; the correlation between income and health problems will be negative and linear. In a nonlinear relationship, variables are related but not in this linear fashion. An example would be a depression drug that has no effect at low dose, decreases symptoms at moderate doses, and increases symptoms at very high doses. Here there would be a non-linear relationship between dosage and depression level. Regarding the other choices, the phi coefficient can be used to measure the correlation between two dichotomous variables (i.e., variables that can take one of two values). The point-biserial coefficient is used to measure the correlation between a dichotomous variable and a quantitative variable. And Spearman’s rho is used to measure the correlations between two sets of ranked data
- An educational psychologist has data on 12 different variables collected from students in the graduating high school class of the preceding year, including high school GPA, SAT scores, teacher ratings, and various tests of motivation and personality. She is interested in using these measures to predict success in college. In this instance, the psychologist would use stepwise multiple regression in order to
develop a predictive equation using all 12 measures. determine the optimal set of measures to use. determine if mean differences on the 12 measures significantly differ from each other. identify any cultural bias in the predictor or criterion measure.
The Correct Answer is “B”
Stepwise multiple regression is a variation of multiple regression. In multiple regression, one develops an equation that uses two or more predictor variables to predict scores on a criterion (outcome) variable. Stepwise multiple regression involves starting with a large set of predictors and reducing them to a smaller set that provides significant predictive value without providing overlapping information. Specifically, the goal is to get predictors that have high enough correlations with the criterion and low enough correlations with each to be included. If predictors have high correlations with each other, they are basically providing overlapping information and there is no point in including them. The two variations of this technique are forward stepwise multiple regression and backwards stepwise multiple regression. In forward stepwise multiple regression, you choose the predictor with the highest correlation with the criterion, you add one predictor at a time, and then run a significance test to see if the added predictor significantly increases the combined predictive value of the overall equation. The process stops when an added predictor fails to significantly increase predictive value. In backwards stepwise regression, you start with all predictors, and remove predictors, starting with the one that is least correlated with the criterion. This process ends when removal of a predictor causes a significant decrease in the ability of the equation to predict values on the criterion.
- A researcher inquires about the subjects’ performance expectations and beliefs about the purpose of the study at the conclusion of the experiment. The researcher finds the subjects’ actual performance is consistent with their beliefs and expectations when analyzing the data. The results of the study may be confounded by:
the Hawthorne effect demand characteristics carryover effects changing criteria
The Correct Answer is “B”
B. Demand characteristics are unintentional cues in the experimental environment or manipulation that affect or account for the results of the study. In this situation, the subjects’ may have acted in ways consistent with their expectations rather than simply in response to the experimental manipulation. The Hawthorne effect (a.) occurs when research subjects act differently because of the novelty of the situation and the special attention they receive as research participants. Carryover effects (c.) occur in repeated measures designs when the effects of one treatment have an impact on the effects of subsequent treatments.
- You are conducting a study to examine the differences in reaction time between elderly people and young people. Subjects are asked to view stimuli on a computer screen and to press a lever every time they see certain target stimuli. Your results indicate that younger people respond faster than older people, and you conclude that reaction time is faster for younger people. Your conclusion is faulty because of
carry-over effects. differential attrition effects. a selection bias. cohort effects.
The Correct Answer is “D”
The study described here is an example of a cross-sectional design, in which two or more different age groups are compared to determine whether aging has an effect on a particular dependent variable. A problem with cross-sectional designs is cohort effects. This refers to differences between the groups in experience rather than age that could be accounting for differences between them on the dependent variable. Cohort effects seem like a particularly plausible explanation for the results here, since it’s likely that young people have more experience with computers than older people.
- A researcher studying the effects of two different psychotherapies in the treatment of depression conducts a statistical test, finds the mean score on a test of depression for one of the therapy groups is significantly lower than that of the mean score for the other group, and rejects the null hypothesis that the two therapies do not differ in effectiveness. In reality, in the underlying population, the mean scores of patients after undergoing each type of therapy are equal. The researcher has made which of the following errors?
Type I Type II false negative experiment-wise
The Correct Answer is “A”
To answer this question, it helps to understand some basic features of statistical hypothesis tests. First, they use samples from populations to test hypotheses about entire populations. For this reason, the conclusions they arrive at are probabilistic–since the entire population is not included in the study, there is always a chance that conclusions reached about the entire population will be erroneous. There are ways, such as increasing sample size, to reduce the probability of error, but there is no way to be 100% certain that obtained results from a sample hold true for an entire population. Second, they test the probability that the null hypothesis, or the probability of no effect, is true. For example, in the study described by the question, a statistical hypothesis test would provide the probability that means are actually equal in the population, given the obtained differences in sample means. Put another way, the test would yield the probability that the two samples were drawn from the same population. There are two types of erroneous conclusions about populations that statistical tests can yield. One would be to reject the null hypothesis when the null hypothesis is in fact true. In other words this type of error would be to conclude that population means are different when in fact they are the same, or to conclude that a treatment has an effect on a dependent variable when in fact it does not. This type of error, which is exemplified in the question, is called a Type I error. The other type of error, retaining a false null hypothesis, or concluding that a treatment does not have an effect in the underlying population when in fact it does, is called a Type II error.
- Excessive variability in a behavior over time can make it difficult to obtain accurate information about the effects of an intervention on that behavior. Such variability poses the biggest threat for which of the following research designs?single-subject
factorial
split-plot
Solomon four-group
The Correct Answer is “A”
In a single-subject research design, the target behavior is measured at regular intervals throughout the baseline and treatment phases. If the behavior changes often in strength, intensity, or frequency, it would be difficult to obtain a clear baseline reading or to determine if the intervention is having the desired effect.
- You are investigating whether there is a relationship between the number of years one has been smoking cigarettes and the number of psychotherapy sessions required to quit smoking. The best statistical method to analyze the results is:chi-square
Pearson r
t-test for independent samples
multiple regression analysis
The Correct Answer is “B”
In this case, you are attempting to assess the relationship between two variables that are measured on a continuous (interval or ratio) scale. The Pearson r allows you to do this. The Pearson r is the bivariate (i.e., for two variables) correlation coefficient used when variables are measured on an interval or ratio scale.
- A test measuring verbal fluency is administered to 250 college students, and a split-half reliability coefficient is obtained. If the same test instead had been administered to 250 students aged 12-21, the obtained reliability coefficient probably would havebeen higher.
been lower.
remained about the same.
moved from negative to positive.
The Correct Answer is “A”
One factor that affects any correlation coefficient, including a reliability coefficient, is the range of scores. If the range of scores is restricted on either or both sets of scores, the correlation coefficient will be lowered. The two sets of scores involved in a split-half reliability coefficient are scores obtained by the same group of individuals on two different halves on the test. Originally, the test was administered to only college students. In the second scenario, the test was administered to a broader range of students.
- Randomly selecting certain schools from a large school district and then including all teachers in those schools or a random sample of teachers from those schools in your study is referred to ascluster sampling.
stratified sampling.
systematic sampling.
nested sampling.
The Correct Answer is “A”
In this situation, you are starting out the sampling processing by selecting naturally occurring groups (clusters) of subjects. This is referred to as cluster sampling, and is useful when it’s not feasible to directly sample individuals from the population.
