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Multiple Choice Questions / Test Bank Engineering Psychology and Human Performance 5th Edition by Christopher Wickens. Isbn. 9781032011738.€23,67
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Multiple Choice Questions / Test Bank Engineering Psychology and Human Performance 5th Edition by Christopher Wickens. Isbn. 9781032011738.
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Multiple Choice Questions / Test Bank Engineering Psychology and Human Performance 5th Edition by Christopher Wickens. Isbn. 9781032011738.
Engineering Psychology and Human Performance Fifth Edition Test Bank. Wickens 5e test bank bank for Engineering Psychology and Human Performance.
Test Bank Engineering Psychology and Human Performance 5th Edition
Chapter 1: Introduction (Multiple Choice Questions Not Available)
Chapter 2: Research Methods
Multiple Choice Questions
Correct answers are indicated by *
1. What are secondary data?
a. Subjective
b. *Data are not themselves the direct source of data.
c. Data that can not be analyzed by statistical means
d. Confounded
2. A meta-analysis is accomplished by aggregating the _________?
a. *statistical effect size
b. Bayesian statistics
c. degree of evidence
d. statistical interactions
3. A computational model uses the ____________?
a. statistical effect size
b. data from questionnaires
c. pooling of data
d. *the output of a formula or a computer simulation
4. An experimental design describes the _____ between an independent and a dependent variable?
a. confounding
b. interaction
c. *conditions used to establish and effect or a causal relation
d. conflict
5. What is a t-test?
a. A technique to analyze trends between two (or more) factors
b. A measure of effect size
c. A method to normalize a data set
d. *A statistical test for the two level comparison
6. A repeated measures design is when _____?
a. the experiment is repeated multiple times
b. *each participant in the experiment receives both conditions.
c. the dependent variable is measured throughout the experiment
d. different participants receive the two conditions
7. What is the benefit of a counterbalanced design?
a. It increases the effect size
b. It makes the data easier to analyze
c. *It averages out the sequential biases when the two groups’ performances are averaged
,d. It provides insight into both side of an argument
8. A 2 x 2 four condition experiment is an example of a ________?
a. *factorial design
b. regression
c. computational model
d. meta-analysis
9. What is a statistical interaction?
a. A conflict between the results of two separate statistical analyses
b. Another term for a confounding variable in the dataset
c. The results of a counter-balanced experimental design
d. *The generalizability of an effect of one independent variable across levels of another
10. What is a mixed design?
a. A counter-balanced experimental design
b. An experimental design with more than one hypothesis
c. * One, or some of, the factors are repeated measures and other(s) are between subjects
d. An experimental design that utilizes a combination of qualitative and quantitative measures
11. What is a regression analysis used on?
a. Questionnaire data exclusively
b. *Interval or ratio- scale levels of the independent variable
c. Assumptions on the statistical power of the experimental design
d. The results of a computational model
12. The extent to which people with higher situation awareness also experience higher workload can
be analysed using a ___________?
a. t-test
b. analysis of variance
c. regression analysis
d. *product moment correlation
13. What is the effort-performance trade-off?
a. The results of a confounding variable in the experimental design
b. The declining performance of participants over the course of an experiment
c. The workload experienced by participants during an experiment
d. *Participants in one condition expend greater effort to increase performance than in another
condition
14. What is N?
a. *Sample size
b. Effect size
c. Statistical power
d. How the results of a product moment correlation are expressed
15. What factor does NOT influence the statistical power of an experiment?
,a. Effect size
b*. Participant age
c. Sample size
d. Variability of the data
16. What is ground truth?
a. The state of the world that the researcher believes
b. The state of the world that the academic literature advocates
c. *The state of the world that the researcher wishes to discover
d. The state of the world that the experimental participants believe
17. When does a Type I error occur?
a. When we fail to fully counter balance the experimental design
b. When we detect an effect that does exist
c. When we fail to detect an effect that does, in fact, exist
d. *When we erroneously conclude there is an effect where in fact there is none
18. When does a Type II error occur?
a. *When we fail to detect an effect that does, in fact, exist
b. When we fail to fully counter balance the experimental design
c. When we erroneously conclude there is an effect where in fact there is none
d. When we detect an effect that does exist
19. What is the effect of increasing statistical power?
a. Reduce the range of dependent variables that can be tested
b. *Reduce the probability of a type II error without a corresponding increase in type I error
c. Reduce the number of participants required
d. Increase the applicability of the results to the real-world
20. A meta-analysis is a tool for _______ ?
a. detecting potential confounds in an experimental design
b. debriefing participants after an experiment to gain additional insights
c. analyzing factorial designs
d. * accumulating evidence over a series of experimental studies
21. Analytical equation models often involve _____ ?
a. task analysis
b. *linear algebra
c. statistic tests
d. participant validation
22. A discreate event simulation model _____?
a. drives the presentation of the experimental scenario
b. provides the means by which participants interact with the scenario
c. *runs in real time to simulate a process inferred to operate within the brain.
d. allows the real-time execution of a task analysis
, 23. What is the main advantage of a discrete event simulation model?
a. *It can impose the variability on the process that is an inherent feature of human performance
b. It reduces the impact of confounding variables
c. It can automatically generate a meta-analysis
d. It does not require validation
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