Irfan Ahmed, PhD

Vectors and loops are two tools drawn from computer programming that can be very useful when manipulating data. Their primary use is to perform a large number of similar computations using a relatively small program. Some of the more complicated types of data manipulation can only reasonably be done using vectors and loops. A vector is a set of variables that are linked together because they represent similar things. The purpose of the vector is to provide a single name that can be used to access any of the entire set of variables. A loop is used to tell the computer to perform a set of procedures a specified number of times. Often times we need to perform the same transformation on a large number of variables. By using a loop, we only need to define the transformation once, and can then tell the computer to do the same thing to all the variables using a loop. If you have computer-programming experience then you have likely come across these ideas before. However, what SPSS calls...

Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables. There are basically two types of factor analysis: exploratory and confirmatory. Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses. Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing responses in a predicted way. SPSS only has the capability to perform EFA. CFAs require a program with the ability to perform structural equation modeling, such as LISREL or AMOS. The primary objectives of an EFA are to determine the number of factors influencing a set of measures and the strength of the relationship between each factor and each observed measure. To perform an EFA, you first identify a set of variables that you want to analyze. SPSS will then examine the correlation matrix between those variables to identify those that tend to vary togeth...

Ideally, the measurements that we take with a scale would always replicate perfectly. However, in the real world there are a number of external random factors that can affect the way that respondents provide answers to a scale. A particular measurement taken with the scale is therefore composed of two factors: the theoretical "true score" of the scale and the variation caused by random factors. Reliability is a measure of how much of the variability in the observed scores actually represents variability in the underlying true score. Reliability ranges from 0 to 1. In psychology it is preferred to have scales with reliability greater than 0.7. The reliability of a scale is heavily dependent on the number of items composing the scale. Even using items with poor internal consistency, you can get a reliable scale if your scale is long enough. For example, 10 items that have an average inter-item correlation of only .2 will produce a scale with a reliability of .714. Ho...