Revised XXXXXXXXXX Independent Samples t-test Write-up (Graded Assignment) Username: Name Instructions: For this graded assignment, you will complete the write-up below after completing the...

Write-Up: Independent Samples
t-test


Revised 6-30-20 Independent Samples t-test Write-up (Graded Assignment) Username: Name Instructions: For this graded assignment, you will complete the write-up below after completing the corresponding tutorial to this assignment. You will delete figures and tables where appropriate and then insert correct figures and tables. Also delete and then insert correct answers where there is RED text. To begin, cut and paste the data set below into SPSS (or you can type in the data manually). Do not copy the header row when you paste into SPSS. Before carrying out the analysis in SPSS, you need to set up your data file correctly using the “Variable View” tab.  Scenario: The purpose of this study was to see if there was a difference between Miller Analogies Test (MAT) scores of male (1) and female (2) graduate students. Code Name Gender MAT (scores) 1 Benjamin 1 345 2 Liam  1 457 3 Sam 1 554 4 James 1 586 5 Ethan  1 567 6 Oliver  1 478 7 Daniel  1 457 8 Dylan 1 353 9 Lisa 2 365 10 Carrol 2 461 11 Alisson 2 589 12 Megan 2 445 13 Tammy  2 560 14 Abigail 2 457 15 Emily 2 359 16 Elizabeth 2 350 FINDINGS Overview The purpose of this study was to see if there was a difference between Miller Analogies Test (MAT) scores of male and female graduate students. The independent variable was gender and the dependent variable was MAT scores. An Independent Samples t-test was used to test the hypothesis. The Findings section includes the research question, null hypothesis, data screening, descriptive statistics, assumption testing, and results. Research Question RQ: Is there a difference between Miller Analogies Test (MAT) scores of male and female graduate students? Null Hypothesis H0: There is no significant difference between Miller Analogies Test (MAT) scores of male and female graduate students. Data Screening Data screening was conducted on each group’s dependent variable. The researcher sorted the data on each variable and scanned for inconsistencies. No data errors or inconsistencies were identified. Box and whiskers plots were used to detect outliers on each dependent variable. No outliers where identified. See Figure 1 for box and whisker plots. Figure 1 Box and Whisker Plots Descriptive Statistics Descriptive statistics were obtained on the dependent variable for each group. The sample consisted of 00 participants. Scores on the MAT range from 200-600. A high score of 600 is a perfect score on the MAT, whereas a low score of 200 means that the student only filled out his or her name on the test. Descriptive statistics can be found in Table 1. Table 1 Descriptive Statistics Assumption Testing Assumption of Normality The Independent Samples t-test requires that the assumption of normality be met. Normality was examined using Shapiro-Wilks/Kolmogorov-Smirnov because the sample size was less than 50 participants. The assumption of normality was met/not met. See Table 2 for Tests of Normality. Table 2 Tests of Normality Assumption of Homogeneity of Variance The Independent Samples t-test requires that the assumption of homogeneity of variance be met. The assumption of homogeneity of variance was examined using the Levene’s test. The assumption of homogeneity of variance was met/not met where (p = .00). See Table 3 for Levene’s test of Equality of Error Variance. Table 3 Levene’s Test of Equality of Error Variance Results An Independent Samples t-test was conducted to see if there was a difference in MAT scores between male and female graduate students. The independent variable was gender and the dependent variable MAT scores. The researcher rejected/failed to reject the null hypothesis at the 95% confidence level where t(00) = 00.00, p = .00. Eta square equaled (2 = .000). The effect size was extremely large/ very large/ large/ medium/ small. Eta square was calculated using the formula 2 = t2/(t2 + df). There was/was not a statistical difference between the MAT scores of male (M = 00.00, SD = 00.00) and female (M = 00.00, SD = 00.00) graduate students. See Table 4 for Independent Samples t-test results. Table 4 Independent Samples t-test