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#SPSS CHISQUARE HOW TO#
#SPSS CHISQUARE DOWNLOAD#

If the total variance is 1, then the communality is \(h^2\) and the unique variance is \(1-h^2\). The total variance is made up to common variance and unique variance, and unique variance is composed of specific and error variance. The figure below shows how these concepts are related:

Error variance: comes from errors of measurement and basically anything unexplained by common or specific variance (e.g., the person got a call from her babysitter that her two-year old son ate her favorite lipstick).

Specific variance: is variance that is specific to a particular item (e.g., Item 4 “All computers hate me” may have variance that is attributable to anxiety about computers in addition to anxiety about SPSS).

Unique variance is any portion of variance that’s not common.

Values closer to 1 suggest that extracted factors explain more of the variance of an individual item.

Communality (also called \(h^2\)) is a definition of common variance that ranges between \(0 \) and \(1\).

Items that are highly correlated will share a lot of variance.

Common variance is the amount of variance that is shared among a set of items.

Factor analysis assumes that variance can be partitioned into two types of variance, common and unique Since the goal of factor analysis is to model the interrelationships among items, we focus primarily on the variance and covariance rather than the mean. Go to top of page Partitioning the variance in factor analysis These interrelationships can be broken up into multiple components. Recall that the goal of factor analysis is to model the interrelationships between items with fewer (latent) variables. Due to relatively high correlations among items, this would be a good candidate for factor analysis. I dream that Pearson is attacking me with correlation coefficientsįrom this table we can see that most items have some correlation with each other ranging from \(r=-0.382\) for Items 3 “I have little experience with computers” and 7 “Computers are useful only for playing games” to \(r=.514\) for Items 6 “My friends are better at statistics than me” and 7 “Computer are useful only for playing games”. My friends will think I’m stupid for not being able to cope with SPSS Let’s get the table of correlations in SPSS Analyze – Correlate – Bivariate: Go to top of page Pearson Correlation of the SAQ-8

I have little experience with computers.

I dream that Pearson is attacking me with correlation coefficients.

My friends will think I’m stupid for not being able to cope with SPSS.

The SAQ-8 consists of the following questions:

#SPSS CHISQUARE DOWNLOAD#

Click on the preceding hyperlinks to download the SPSS version of both files. For simplicity, we will use the so-called “ SAQ-8” which consists of the first eight items in the SAQ. Let’s proceed with our hypothetical example of the survey which Andy Field terms the SPSS Anxiety Questionnaire. Go to top of page Motivating Example: The SAQ (SPSS Anxiety Questionnaire) Do all these items actually measure what we call “SPSS Anxiety”? Let’s say you conduct a survey and collect responses about people’s anxiety about using SPSS. Suppose you are conducting a survey and you want to know whether the items in the survey have similar patterns of responses, do these items “hang together” to create a construct? The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying or latent variables called factors (smaller than the number of observed variables), that can explain the interrelationships among those variables. Principal axis factoring (2-factor PAF).Running a PCA with 2 components in SPSS.Running a PCA with 8 components in SPSS.Partitioning the variance in factor analysis.SPSS Syntax: SPSS Syntax File for EFA and PCA Seminar.Powerpoint Slides: Slides for EFA and PCA in SPSS.

#SPSS CHISQUARE HOW TO#

The seminar will focus on how to run a PCA and EFA in SPSS and thoroughly interpret output, using the hypothetical SPSS Anxiety Questionnaire as a motivating example. For the EFA portion, we will discuss factor extraction, estimation methods, factor rotation, and generating factor scores for subsequent analyses. For the PCA portion of the seminar, we will introduce topics such as eigenvalues and eigenvectors, communalities, sum of squared loadings, total variance explained, and choosing the number of components to extract. We will begin with variance partitioning and explain how it determines the use of a PCA or EFA model. This seminar will give a practical overview of both principal components analysis (PCA) and exploratory factor analysis (EFA) using SPSS.