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SPSS Data Analysis, Data Interpretation, and Result Writing
Rating: 4.2 out of 5(13 ratings)
69 students

SPSS Data Analysis, Data Interpretation, and Result Writing

Learn to Do Stats and Results Writing for Your Own Research, or Business
Created byDr. Prince Raphael D Costa
Last updated 1/2023
English
English [Auto],

What you'll learn

  • Perform data analysis for own scientific research projects
  • Use statistical tests to predict outcomes
  • Create graphs and charts
  • Learn to write results of data analysis

Course content

2 sections • 16 lectures • 2h 13m total length

  • Introduction to SPSS User Interface15:32

    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.

  • Compute Variables4:16

    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.

  • Descriptive Statistics7:40

    Compute descriptive statistics in SPSS by calculating mean, median, mode, and standard deviation for height data, then explore frequency distributions, charts, split-file analyses, and recoding gender.

  • Chi-Square test9:52

    Learn to perform a chi square test in SPSS to assess relationships between categorical variables, using crosstabs, chi square, Cramer's V, odds ratio for dichotomous data, and p-values.

  • Independent Samples t-test9:10

    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.

  • Paired Samples t-test7:28

    Use a paired samples t-test in SPSS to compare mean weight before and after exercise within the same students, and interpret the t value, confidence interval, and significance.

  • One Way ANOVA10:18

    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.

  • Kruskal Walis H Test and Mann Whitney U Test5:40

    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.

  • Pearson and Spearman Correlation8:22

    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.

  • Simple Linear Regression8:41

    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.

  • Binary Logistic Regression5:55

    Learn how to run a binary logistic regression in SPSS to predict a performance category from gender and hours exercised, interpret the model, hosmer-lemeshow goodness of fit, and odds ratios.

  • Reliability analysis6:00

    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.

  • Assumptions and APA style Results Writing1:13

    Learn how to check assumptions of common statistical tests and write APA style results using guided templates and examples for accurate SPSS reporting.

  • ROC Curve7:08

    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.

Requirements

  • Basic computer skill

Description

This course is geared toward complete beginners who never used any statistics software before. You don't need prior knowledge of statistics as well if you follow this course. I have explained the SPSS program from scratch so that any student can learn it from the beginning. I have uploaded an introductory video to the SPSS user interface so that new users can get comfortable with this program. I have shown how to enter data properly and create variables within SPSS. All the parameters of the variables were explained in this course. I have included common statistical tests in this course. These tests are performed in almost all scientific research projects. The SPSS files are also uploaded as practice materials. At the end of this course, you would be competent enough to perform data analysis for your research projects.  You would learn to find out the statistically significant relationships (p-values) between two or more variables. You can also use the knowledge gathered from this course to do your business data analysis. You would be able to make predictions about your sales and profits after doing this course. You would learn how to present data using charts and graphs. No need to hire a statistician again for your data analysis needs.

Who this course is for:

  • Undergraduate and Postgraduate students
  • Medical doctors
  • Researchers

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