Question 1

What is the definition of effect size?

Accepted Answer

Effect size refers to the magnitude of a difference or relationship and is typically calculated by expressing the difference or relationship on a standardized scale. This allows for comparisons across different studies or measures. Option B is not the definition of effect size, but rather refers to the mean difference between groups. Option C refers to the variability of results and not the magnitude of a difference. Option D is not accurate as effect size is not based on hypothetical or expected results.

Question 2

Suppose a researcher conducted an experiment on improving algebra achievement, and the experimental posttest raw-score mean equaled 500.00 (sd = 100.00), and the control group raw-score mean equaled 400.00 (sd = 100.00). What is the value of the effect size for the experiment?

Accepted Answer

The effect size can be calculated using the formula for Cohen's d, which is the difference between two means divided by the standard deviation. In this case, (500 - 400) / 100 = 1.0. This indicates a large effect size.

Question 3

In this topic, the raw-score differences between the means (5 for Experimenter A and 10 for Experimenter B) were standardized by dividing each of them by what statistic?

Accepted Answer

The raw-score differences between the means were standardized by dividing by the standard deviation. This gives us the standardized effect size, which allows for comparison between studies using different scales or units of measurement. Choice A (effect size or d) is correct in describing what the standardized score represents, but is not the statistic being divided by. Choice B (the difference of the means) is incorrect because this is a raw-score difference and not a statistic that is being divided by. Choice C (the median of the raw scores) is irrelevant because we are not standardizing based on the median.

Question 4

If the value of d for the difference between two means equals 1.00, the experimental group's mean is how many standard-deviation units higher than the control group's mean?

Accepted Answer

The answer of If the value of d for the...

Question 5

Using Cohen's suggested descriptors, a value for d of 0.20 would be described as what?

Accepted Answer

According to Cohen's suggested descriptors, a value of 0.20 would be considered small. Very small values are typically below 0.10, medium values range from 0.30 to 0.50, and large values are above 0.50.

Question 6

What is the "effective range" of standard deviation units on both sides of the mean?

Accepted Answer

The "effective range" of standard deviation units on both sides of the mean is known as the "standard deviation from the mean." In a normal distribution, approximately 68% of the data falls within one standard deviation from the mean, approximately 95% falls within two standard deviations from the mean, and approximately 99.7% falls within three standard deviations from the mean. Therefore, the "effective range" is 3.0 standard deviations from the mean.

Question 7

According to this topic, what are the two measures of effect size that are very widely reported?

Accepted Answer

Cohen's d and effect-size r are the two measures of effect size that are very widely reported.

Question 8

Cohen's d is expressed on a standard-deviation scale that typically ranges from what to what?

Accepted Answer

Cohen's d is a measure of effect size that can theoretically range from negative infinity to positive infinity, depending on the difference between the means relative to the standard deviation. However, in practice, values typically fall within the range of -3.00 to +3.00, indicating small, medium, and large effects.

Question 9

A value of r should be interpreted by doing what?

Accepted Answer

The correlation coefficient (r) should be squared to determine the coefficient of determination (r^2), which represents the proportion of variability in one variable that can be explained by the other variable. Then, this value should be multiplied by 100 to convert it to a percentage. Therefore, choice C, which involves both squaring and multiplying by 100, is the best option.

Question 10

Meta-analysis is used for what?

Accepted Answer

Meta-analysis is a statistical method that combines the results of previous studies to provide a more comprehensive understanding of a particular research topic. It is a powerful tool for synthesizing and summarizing data from multiple studies, allowing researchers to draw more accurate conclusions and identify patterns or inconsistencies across different studies.

Question 11

When comparing results between similar experiments, which of the following is NOT a factor identified in this topic as potentially explaining differences?

Accepted Answer

Computation errors are not typically considered a factor in explaining differences between similar experiments, as they are mistakes in calculation rather than inherent variations or uncertainties in the experimental process itself. Factors like unknown systematic errors, known systematic errors, and random errors of measurement directly relate to the experimental setup and execution, influencing the variability and reproducibility of results.

Question 12

The experimental group secretly having access to the answer key for the test is an example of what type of error?

Accepted Answer

The answer of The experimental group secretly having access to...

Question 13

Reliable results may also be said to be valid results.

Accepted Answer

Reliability refers to the consistency of a measure, while validity refers to the accuracy of a measure. A result can be reliable without being valid.

Question 14

Computing a mean difference across studies that used measurement scales with different possible numbers of score points is meaningless. What is suggested in this topic to overcome this problem?

Accepted Answer

Converting to a standardized measurement scale or using regression analysis would not address the issue of different possible numbers of score points. Weighting the average value of d would not be meaningful because the d values would not be comparable across studies. Using a measure of effect size such as Cohen's d would allow for the comparison of mean differences across studies with different possible numbers of score points.

Question 15

What is usually done when the studies to be used in a meta-analysis have different sample sizes?

Accepted Answer

When studies to be used in meta-analysis have different sample sizes, the most common method is to weight the average value of Cohen's d by the inverse of the variance of each study's effect size. This means that studies with larger sample sizes and smaller variances have a greater influence on the overall estimate of effect size. Therefore, choice D is the best choice.

Question 16

Which standardized measure of effect size is used in meta-analytic studies?

Accepted Answer

Both effect-size r and Cohen's d are commonly used standardized measures of effect size in meta-analytic studies.

Question 17

Which of the following describes the weakness of meta-analysis known as publication bias?

Accepted Answer

Publication bias refers to the tendency for researchers to only publish studies that show statistically significant results, while ignoring studies with non-significant results. This leads to an incomplete picture of the research on a given topic, as the non-significant results may contradict the overall findings.

Question 18

Which of the following is a potential weakness of meta-analysis, as described in this topic?

Accepted Answer

Meta-analyses rely on data from previous studies, and if the studies included in the meta-analysis are not diverse in terms of sample selection, the results may not be applicable to a broader population. This can be a potential weakness of meta-analysis. The other options listed (use of moderator variables, lack of objectivity, and independent confirmation) are not necessarily weaknesses of meta-analysis but rather aspects that must be carefully considered and addressed during the meta-analytic process.

Question 19

In a report in which separate analyses are conducted for various subgroups, what is the variable on which the studies were divided into subgroups called?

Accepted Answer

The variable on which studies are divided into subgroups for separate analyses is called a moderator variable. Moderator variables are used to explore how or under what conditions different effects occur. They can influence the strength or direction of the relationship between independent and dependent variables.

Quiz 9: Effect Size and Meta-Analysis