Research Data Critique Scenario One
The CEO of ABC manufacturing commissioned a study to look at the differences between the current salaries of her employees by employee job title. There were three job categories: clerical, custodial, and managerial. The study collected current salary data of the three groups and the researcher conducted a statistic and the results are presented below. Using the five steps of hypothesis testing, explain what the researcher might have done, including the appropriate analysis, and interpret the results. Are there any problems with this study? If so, explain what they are.
Average Salary
Clerical (n = 363) $27,838.54
Custodial (n = 27) $30,938.89
Manager (n = 84) $63,977.80
Test statistic = 434.48, p< .05
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Just as a start:
What test is being used to analyze the data?
ANOVA
To analyze the salary differences among different employee job titles, the researcher could have followed the five steps of hypothesis testing. Let's go through each step and explain what the researcher might have done:
Step 1: Formulate the null hypothesis (H0) and alternative hypothesis (Ha):
H0 (Null Hypothesis): There is no significant difference in salaries among the job categories (clerical, custodial, and managerial).
Ha (Alternative Hypothesis): There is a significant difference in salaries among the job categories.
Step 2: Choose the significance level (α):
In this case, the significance level is not explicitly provided, but it is mentioned that the p-value is less than 0.05. Therefore, we can assume the significance level (α) is 0.05, which is a common choice.
Step 3: Collect and analyze the data:
The researcher collected current salary data for each job category. The sample sizes for each group are given as n = 363 for clerical, n = 27 for custodial, and n = 84 for managerial. The average salaries for each group are $27,838.54 for clerical, $30,938.89 for custodial, and $63,977.80 for managerial.
Step 4: Conduct the appropriate analysis:
The researcher conducted a statistical test to compare the means of the three groups. The test statistic is stated as 434.48, but the specific test used is not mentioned. The test statistic could be the result of an analysis of variance (ANOVA) test, which is commonly used to compare means of multiple groups.
Step 5: Interpret the results:
Based on the reported p-value (p< .05), the researcher can reject the null hypothesis. This means that there is a significant difference in salaries among the job categories. However, without knowing the specific test used or the critical value for rejection, it is difficult to provide a more detailed interpretation of the results.
Now, let's consider the potential problems with this study:
1. Small sample sizes: The sample sizes for custodial (n = 27) and managerial (n = 84) groups are relatively small compared to the clerical group (n = 363). This could affect the statistical power and generalizability of the findings.
2. Lack of specific test mentioned: The study does not specify the statistical test used to analyze the data. Without this information, it is difficult to assess the appropriateness of the analysis or compare it to existing literature.
3. Potential confounding variables: The study only considers job title as the differentiating factor for salary comparison. Other variables like years of experience, education level, or performance could also influence salary differences among the groups. Not controlling for these variables may introduce bias into the results.
4. Lack of information on statistical assumptions: The study does not provide information on the assumptions of the statistical test used, such as the normality of data or equality of variances. Violations of these assumptions could impact the validity of the results.
To enhance the study, the researcher could consider addressing these potential problems by increasing the sample sizes, conducting a specific statistical test, controlling for relevant variables, and providing detailed information on statistical assumptions.