Module 4: Linear Regression (Series 2)

BeginnersPrerequisites

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

Mphil Students from participating departments taking the Social Science Research Methods Course as part of their research degree

Students expected to have solid understanding of topics discussed in Modules 1-3. Students need to have firm knowledge of covariance, correlation, and comparison of means. Students also need to have working knowledge of using SPSS.

Number of sessions: 4

• Session 1:Review of covariance, correlations and comparison of means. Introduction to bivariate linear regression.
• Session 2: Multivariate linear regression
• Session 3: Assessing regression models

• The objective is to learn the assumptions underlying regression models
• To run regression analysis using SPSS
• To assess an solve possible problems with a regression model

• To learn fundamental statistical techniques - regression analysis

Presentations, demonstrations and practicals

SPSS v. 16 on PWF Windows

Three exercises

Throughout all introductory statistics modules the main textbook is:

• Field, Andy (2009), Discovering Statistics using SPSS. London:Sage

• To gain the maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking. Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

Four sessions of two hours

Four times in Lent term

• Module 1: Foundations in Statistics (Series 2)
• Module 2: SPSS and Descriptive Statistics (Series 3)