Design and Measurement 3

Aims and objectives

The aims are to provide the theoretical background and practical skills which will enable students to:

• Critically assess statistics presented in journal articles.
• Choose appropriate analyses to perform on your own data.
• Understand the assumptions and limitations involved in the analyses.
• Screen data and, if necessary, transform variables before analysis.
• Perform data analyses using SPSS for Windows.
• Write an informative report on the data analysis.

Teaching methods

Assessment

Content

The unit of study content has been divided into modules, and then further divided into topics.

Module 1: Data Modification and Selection and Data Screening

• Selecting samples of data in SPSS.
• Recoding Variables and creating new variables.
• Identifying outliers and out of range values.
• Removing or replacing outliers and out-of-range values.
  
  Module 2: Investigating the Relationship between Two Metric Variables
• Describing the relationship using graphs and summary statistics.
• Significance testing and power analysis.
• The theory behind simple linear regression.
• Testing the assumptions underlying simple linear regression.
• Using dichotomous variables in regression.
  
  Module 3: Multiple Regression
• Multiple regression using SPSS for Windows.
• Understanding partial and part correlation coefficients.
• Selecting and using different regression strategies.
• Presentation of results.
• Testing the assumptions in multiple regression.
• Testing for interactions in multiple regression.
  
  Module 4: Multivariate Analysis of Variance
• Single factor, independent groups design MANOVA.
• Single factor within subjects design MANOVA.
• Testing assumptions and other practical issues.
  
  Module 5: Factor Analysis
• The steps involved in factor analysis.
• Assessing a factor analysis.
• Performing a factor analysis using SPSS for Windows.