SPSS Data Analysis, Data Interpretation, and Result Writing

Explore the SPSS user interface by switching between data view and variable view, entering numeric and string data, assigning missing values, and computing the mean with descriptive statistics.

Learn to compute a new variable in SPSS using transform and compute variable to create BMI from weight and height, and use a serial number for sorting ascending or descending.

Compare the means of two independent groups using the parametric independent samples t test to determine statistical significance. Assess assumptions: independence, normality, equal variances, and random sampling, to validate results.

Learn to perform one-way ANOVA on socioeconomic class and performance scores, recode the class variable, check homogeneity of variances, and interpret p-values, with Kruskal-Wallis as a nonparametric alternative.

Perform the kruskal-wallis test and mann-whitney u test in SPSS as nonparametric alternatives to one-way anova when assumptions fail, comparing performance across lower, middle, upper socioeconomic classes and interpreting p-values.

Explore Pearson and Spearman correlation tests to assess relationships between weight and height, including data assumptions, interpreting the correlation coefficient and p-values, and when to switch to Spearman.

Assess how exercise (independent variable) predicts weight change (dependent variable) using simple linear regression in SPSS, validating assumptions and interpreting a strong, significant relationship with r² ≈ 0.757.

Perform Cronbach's alpha to assess internal consistency of a 20-item six-point Likert scale measuring internet usage patterns among undergraduates; a Cronbach's alpha above 0.7 confirms reliability.

Explore the ROC curve to balance sensitivity and specificity when using a scoring system to diagnose a condition, and interpret the area under the curve and cutoff values.