About the labs
The purpose of the labs is to teach you how to apply the theoretical concepts from the lectures.
Here are my goals:
- To teach you how to use the IBM SPSS (Statistical Package for the Social Sciences).
- To revise and apply the concepts described in lecture and guide you through some basic analysis of real world datasets.
- To make sure you are comfortable doing your homeworks and final project.
- To review and answer any questions about materials from lectures.
- Take questions about previous labs or homework assignments.
- Review concepts from lectures.
- Go over SPSS commands and illustrate their applications using different datasets.
The link to these notes is available in NYU Clasees.
My Lab notes will mostly follow the book below (as in Prof. Hassad’s syllabus):
- Weinberg, S. L., & Abramowitz, S. K. (2016). Statistics Using IBM SPSS: An Integrative Approach. Cambridge university press.
Check out the following additional resources:
- If you have any questions, please email me: firstname.lastname@example.org. I should be able to respond within 24 hours.
- I will have office hours (TBA). Please email ahead of time if you plan on attending.
- Prof. Hassad will grade the homeworks. We will discuss any common mistakes.
- Homeworks should be distributed through NYU classes in PDF format.
- Formatting: always follow the homework template Prof. Hassad provides with each homework.
Some general notes
- Statistics helps you make statements about the world and about the issues that matter to you.
- It is directly applied to the “real world”. It has great practical value.
Level of math requiered
- You can do well in this course even if you have not had a good experience with math before. It is a stats class, not a math class.
- Statistical packages do most of the math for you!
Teaching a stats software
SPSS has a huge amount of different commands and tools. What will I cover?
- The goal is to teach you the basics and, most importantly, to show you the intuition behind the software and suggest a simple work-flow.
- If you understand how the software works and what it is capable of doing, you will be qualified to use it for more advanced topics later on.
LAB 1: Defining variables, types of data, split file, measures of central tendency, cross-tabulation, graphical representations of data.
LAB 2: Combined measures of central tendency, measures of dispersion, skewness, graphs, report writing.
LAB 3: The empirical rules of the normal curve. Distribution of z-scores (mean, SD, and shape).
LAB 4: Estimation: Sample size, standard error of the mean, confidence intervals (95% and 99%), precision of estimates.
LAB 5: One-sample t-test: Data entry, analysis, interpretation, report writing (see format and guidelines), editing of output tables.
LAB 6: The Independent samples t-test: Data entry, analysis, interpretation (including the Levene’s test for equality of variances), report writing (see format and guidelines), editing of output tables.
LAB 7: The Dependent (paired) Samples t-test: Data entry, analysis, interpretation, report writing (see format and guidelines), editing of output tables.
LAB 8: One-way ANOVA: Data entry, analysis, interpretation (including the Levene’s test for equality of variances), post-hoc analysis, report writing (see format and guidelines), editing of output tables, graph of group means.
LAB 9: Two-way ANOVA: Data entry, analysis, interpretation (including cell and marginal means, and graphs for interaction effect), the Levene’s test for equality of variances, post-hoc analysis, report writing, editing of output tables.
LAB 10: Pearson’s correlation: Data entry, analysis, interpretation, scatter plots, confounding.
LAB 11: Combined simple linear correlation and regression: Data entry, analysis, interpretation, editing of output tables, report writing (see format and guidelines).
LAB 12: Chi-squared analysis (including Yate’s continuity correction for 2 x2 contingency tables): Data entry, analysis, interpretation Final Exam Review (refer to the review sheet).