PSYC 815 Quiz Power Error
PSYC 815 Quiz: Power, Error, Research Design
Covers the Textbook material from Module 2: Week 2.
- A group of 4th grade students who were taught methods to increase their classroom participation are compared to a group of students who did not receive the classroom participation training on an academic achievement test. The mean of the students in the high classroom participation group on the academic achievement test is MTreatment = 120 and the low classroom participation students group mean was M Control = 105 and their pooled standard deviation is SD = 22. Compute the effect size of the difference between the two means using Cohen’s d statistic as illustrated in Chapter 3. What is the correct effect size?
- What is the strength of the Cohen’s d effect size in the example above according to Cohen’s convention.
- A comparison of the 4th grade students who were taught methods to increase their classroom participation are compared to a group of students who did not receive the classroom participation training on a reading subscale of an academic achievement test using an eta- squared (η2) effect size. The SSTreatment = 1110.050 and the SSError = 664.900. Use the illustration in Chapter 3 to calculate the (η2) effect size and choose the correct answer below.
- What is the strength of the eta- squared (η2) effect size in the example above according to Cohen’s convention.
- Important elements make up a priori power. Which one of the following answers best reflects the elements that make up a priori power?
- Which one of the following best describes power?
- Of the following which is true about power?
- Of the following which can increase the power of your study?
- A power analysis is important for which important step in research
- The correctness of rejecting or failing to reject an H0 is a balance between avoiding making either a Type I or Type II error.
- A researcher conducts a power analysis before implementing a research study to assess the probability of finding a significant effect if it exists. The researcher wants to compare differences between two means on a dependent variable from two independent groups of participants using a one- tailed independent t-test and a total sample size of 70 participants. The following a priori (estimated) effect size, alpha level, estimated sample size, and a criterion power value are used for the power analysis.
- The difference between a sample mean and population mean is referred to as which one of the following?
- Differences in characteristics of participants assigned to treatment conditions may confound attributing the changes in the dependent variable to the treatment refers to which one of the following threats to internal validity of a research design.
- The underlying assumptions of normality and homogeneity of variance are not necessary when a nonparametric statistic like the Mann-
Whitney U statistic is used.
- Which one of the designs controls for all of the threats associated with THIS MESS?
- Which one of the following is the most important way to control for extraneous variance?
- Which one of the statistics below has two or more independent variables?
- A researcher compares three means for significant differences using a One- Way ANOVA and the results are F(2, 75) = 12.45, p < .01. Which one of the following statement reflects the correct interpretation of the results?
- Each observed value in a distribution of scores is paired with its expected value from a normal distribution relates most closely to which one of the following?
- If most of the scores are on the right side of a distribution of scores and there are extreme scores on the left side of the distribution of scores then what exists?
- Imputing data means which one of the following.
- A kurtosis value near zero indicates which of the following?
- Which of the following would be appropriate to manage missing data?
- Why is it important to determine if the underlying assumptions in a statistic are met?
- Matching participants across condition groups is an acceptable way to hold constant the confounding effects of extraneous variables