# About the labs

## Purpose

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.

## Lab Structure

- Take questions about previous labs or homework assignments.
- Review concepts from lectures.
- Go over SPSS commands and illustrate their applications using different datasets.

## Materials

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:

## Logistics

### Questions

- If you have any questions, please email me:
**jms1738@nyu.edu**. 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.

### Homeworks

- 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

**Why statistics?**

- 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.

## Tentative schedule

**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).