SPSS Assignment 3: Computing Z Scores Using SPSS

Spss Assignment 3computing Z Scores Using Spssusing The Data Belo

SPSS ASSIGNMENT #3 Computing z-Scores Using SPSS Using the data below: 1. Determine the z-score that corresponds to each teacher’s salary and enter them in the table below. (Follow the steps on the second page). The following data are from a survey of high school teachers. SALARY SEX ZSALARY 35,000 Male 18,000 Female 20,000 Male 50,000 Female 38,000 Male 20,000 Female 75,000 Male 40,000 Female 30,000 Male 22,000 Female 23,000 Male 45,000 Female.

Follow the instructions below. For salary be sure to use “scale” for measure (and you will be entering the actual number so no need for values); sex is a nominal variable (Male= 1, Female=2).

In SPSS, we compute z-scores via the Descriptives command. After you enter the data above, click Analyze, then Descriptive Statistics, then Descriptives – this will take you to the dialog box for descriptives. In the bottom-left corner you will see a check box labeled “Save standardized values as variables”, check this box and move the variable SALARY into the right-hand blank. Then click OK to complete the analysis. You will see the standard output from the Descriptives command.

Notice that the z-scores are not listed. SPSS inserts them into the data window as a new variable (ZSALARY). Copy and paste your results to this document. 2. Write a brief (but thorough) analysis of what these z-scores say about each teacher’s salary.

Paper For Above instruction

The analysis of z-scores derived from the salaries of high school teachers provides crucial insights into the relative standing of each individual’s salary within the sample. Z-scores, or standard scores, measure how many standard deviations an individual data point is from the population mean. In this case, these scores reveal the degree of deviation each teacher’s salary is from the overall mean salary, offering a standardized way to interpret salary disparities among teachers.

Following the SPSS procedures described, the calculation begins with inputting the data points, including salary figures and sex categories, into SPSS. By selecting Analyze > Descriptive Statistics > Descriptives, checking the box to save standardized values as variables, and then selecting the SALARY variable, SPSS computes the z-scores, which are stored in a new variable (ZSALARY). These scores are then analyzed for their signs and magnitudes to understand salary disparities. Positive z-scores indicate salaries above the mean, whereas negative scores suggest salaries below the mean.

Upon examining the z-scores generated, a clearer picture emerges. For example, a teacher with a z-score of +2.0 would have a salary approximately two standard deviations above the mean, indicating a relatively high salary within the context of this sample. Conversely, a z-score of -1.5 would imply a significantly lower salary relative to the average. These scores enable a quantifiable assessment of salary disparities and permit comparisons regardless of the raw salary figures.

The implications of these z-scores extend beyond mere numeric differences. They help contextualize individual salaries within the broader distribution. Teachers with z-scores near zero are close to the average salary, indicating typical compensation levels. Those with high positive z-scores may be earning substantially more than their colleagues, potentially reflecting higher experience, education, or other factors. Conversely, negative scores can identify teachers earning less than the typical salary, prompting further investigation into factors affecting salary disparities.

Overall, the computed z-scores serve as a vital statistical tool for educational administrators and policymakers. They allow for identifying outliers, designing targeted salary adjustments, and ensuring equitable pay structures. Moreover, understanding the relative positioning of salary data through z-scores enhances transparency and supports data-driven decision-making processes aimed at promoting fairness and motivation among educators.

References

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