The point of interest is where the curve starts to flatten. Overview this tutorial looks at the popular psychometric procedures of factor analysis, principal component analysis pca and reliability analysis. How to conduct a factor analysis in spss click on analyze, data reduction, factor highlight the items you want to include in the analysis, and move them to the variables window using the right arrow moving from left to right, select each. Example factor analysis is frequently used to develop questionnaires. Manova, logistic regression, comparing means tests and factor analysis. Note that we continue to set maximum iterations for convergence at 100 and we will see why later. Cyberloafing predicted from personality and age these days many employees, during work hours, spend time on the internet doing personal things, things not related to their work. By default spss will list variables in the order in which they are entered into the data editor. The larger the value of kmo more adequate is the sample for running the factor analysis. In the descriptives window, you should select kmo and bartletts test of sphericity. Use principal components analysis pca to help decide. Factor analysis is commonly used in the fields of psychology and education6 and is considered the method of choice for interpreting selfreporting questionnaires.
Running a common factor analysis with 2 factors in spss. Its aim is to reduce a larger set of variables into a smaller set of artificial variables, called principal components, which account for most of the variance in the original variables. Be able explain the process required to carry out a principal component analysis factor analysis. Factor analysis is based on the correlation matrix of the variables involved, and correlations usually need a large sample size before they stabilize. Efa cannot actually be performed in spss despite the name of menu item used to perform pca. In contrast, common factor analysis assumes that the communality is a portion of the total variance, so that summing up the communalities represents the total common variance and not the total variance.
The ibm spss statistics 21 brief guide provides a set of tutorials designed to acquaint you with the various components of ibm spss statistics. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output. Figure 5 the first decision you will want to make is whether to perform a principal components analysis or a principal factors analysis. Or simply you can get, for example, a factor based score or an average of individual means of related observed variables create a variable that has means of three variables of each subject. For this to be understandable, however, it is necessary to discuss the theory behind factor analysis. How to conduct a factor analysis in spss click on analyze, data reduction, factor highlight the items you want to include in the analysis, and move them to the variables window using the right arrow moving from left to right, select each of the buttons and select the following. You can do this by clicking on the extraction button in the main window for factor analysis see figure 3. Andy field page 1 162004 factor analysis using spss for an overview of the theory of factor analysis please read field 2000 chapter 11 or refer to your lecture. With respect to correlation matrix if any pair of variables has a value less than 0.
Discovering statistics using spss available for download and read online in other formats. Be able to carry out a principal component analysis factoranalysis using the psych package in r. Be able explain the process required to carry out a principal component analysisfactor analysis. In this process, the following facets will be addressed, among others. Now, with 16 input variables, pca initially extracts 16 factors or components.
If it is an identity matrix then factor analysis becomes in appropriate. Factor analysis is designed for interval data, although it can also be used for ordinal data e. Nov 11, 2016 simple structure is a pattern of results such that each variable loads highly onto one and only one factor. Exploratory factor analysis efa validation of psychometric measures confirmatory factor analysis cfa cannot be done in spss, you have to use e. If the determinant is 0, then there will be computational problems with the factor analysis, and spss may issue a warning message or be unable to complete the factor analysis. Be able to carry out a principal component analysis factor analysis using the psych package in r. The theory behind factor analysis as the goal of this paper is to show and explain the use of factor analysis in spss, the. The default is to estimate the model under missing data theory using all available data. Click on the descriptives button and its dialogue box will load on the screen. Factor analysis in spss to conduct a factor analysis. Be able to select and interpret the appropriate spss output from a principal component analysisfactor analysis.
Or simply you can get, for example, a factorbased score or an average of individual means of related observed variables create a variable that has means of three variables of each subject. Andy field page 1 10122005 factor analysis using spss the theory of factor analysis was described in your lecture, or read field 2005 chapter 15. Factor analysis using spss 2005 university of sussex. The default chosen by spss depends on the data type. For factor analysis, items on the survey that did not exceed a 0. The graph is useful for determining how many factors to retain. Confirmatory factor analysis using amos data youtube. Available methods are varimax, direct oblimin, quartimax, equamax, or promax. Spss will not only compute the scoring coefficients for you, it will also output the factor scores of your subjects into your spss data set so that you can input them into other procedures. Simple structure is a pattern of results such that each variable loads highly onto one and only one factor. Factor analysis is frequently used to develop questionnaires. Once your measurement model turns out statistically significant, you may calculate factor score of the latent variables on the basis of the factor analysis.
We saw that this holds for only 149 of our 388 cases. The solution you see will be the result of optimizing numeric targets, given the choices that you make about extraction and rotation method, the number of factors to retain, etc. As for the factor means and variances, the assumption is that thefactors are standardized. In the scatterdot dialog box, make sure that the simple scatter option is selected, and then click the define button see figure 2. This method simplifies the interpretation of the factors. Spss factor can add factor scores to your data but this is often a bad idea for 2 reasons. This video provides a brief demonstration of how to carry out an exploratory factor analysis in amos using the specification search option. Download pdf discovering statistics using spss book full free. Questionnaire evaluation with factor analysis and cronbach. Allows you to select the method of factor rotation. Principal components analysis pca, for short is a variablereduction technique that shares many similarities to exploratory factor analysis.
Factor analysis was performed to extract and decide on the number of factors underlying the measured. Factor analysis in spss to conduct a factor analysis, start from the analyze menu. The idea is to fit a bifactor model where the two latent factors are the verbal and performance constructs. Similar to factor analysis, but conceptually quite different. Exploratory factor analysis reliability ronbachs alpha the data were analyzed using social sciences spss software version 23. Factor analysis is a multivariate technique for identifying whether the correlations between a set of observed variables stem from their relationship to one or more latent variables in the data, each of which takes the form. For example, for variables of type numeric, the default measurement scale is a continuous or interval scale referred to by spss as scale. This presentation will explain efa in a straightforward, nontechnical manner, and provide detailed instructions on how to carry out an efa using the spss. Kaisermeyerolkin measure of sampling adequacy this measure varies between 0 and 1, and values closer to 1 are better.
The simple scatter plot is used to estimate the relationship between two variables figure 2 scatterdot dialog box. A factor with four or more loadings greater than 0. Kaisermeyerolkin kmo measure of sampling adequacy this test checks the adequacy of data for running the factor analysis. This video provides a brief overview of how to use amos structural equation modeling program to carry out confirmatory factor analysis of survey scale items. Exploratory factor analysis university of groningen. Base analysis 2factor ml using direct quartimin on raw data instead of correlation matrix syntax and output for the analysis.
The data analysis was conducted using spss and amos software programme packages for windows. Validity and reliability of the instrument using exploratory. The presentation comes from example 23 in the amos 21. Exploratory factor analysis smart alexs solutions task 1 reruntheanalysisinthischapterusingprincipalcomponentanalysisandcomparethe. Principal component analysis can be performed in sas using proc princomp, while it can be performed in spss using the analyzedata reductionfactor analysis menu selection. It is an assumption made for mathematical convenience. Each component has a quality score called an eigenvalue.
In the factor analysis window, click scores and select save as variables, regression, display factor score coefficient matrix. The factor procedure that is available in the spss base module is essentially limited to exploratory factor analysis efa. Pdf discovering statistics using spss download full. The simple scatter plot is used to estimate the relationship between two variables.
It can be seen that the curve begins to flatten between factors 3 and 4. Exploratory factor analysis in amos using specification. The broad purpose of factor analysis is to summarize. Factor scores will only be added for cases without missing values on any of the input variables.
For variables of type string, the default is a nominal scale. Spss will extract factors from your factor analysis. This guide is intended for use with all operating system versions of the software, including. Factor analysis using spss 6 scree plot the scree plot is a graph of the eigenvalues against all the factors. The illustration is simple, employing a 175 case data set of scores on subsections of the wisc.
C8057 research methods ii factor analysis on spss dr. This short monograph outlines three approaches to implementing confirmatory factor analysis with r, by using three separate packages. Ibm spss statistics 21 brief guide university of sussex. Confirmatory factor analysis and structural equation modeling 57 analysis is specified using the knownclass option of the variable command in conjunction with the typemixture option of the analysis command. Principal components analysis pca using spss statistics. This procedure is intended to reduce the complexity in a set of data, so we choose data reduction. Exploratory factor analysis exploratory factor analysis efa is used to determine the number of continuous latent variables that are needed to explain the correlations among a set of observed variables. Chapter 4 exploratory factor analysis and principal.
Factor analysis is a procedure used to determine the extent to which shared variance the intercorrelation between measures exists between variables or items within the item pool for a developing measure. An introduction to factor analysis ppt linkedin slideshare. To run a factor analysis, use the same steps as running a pca analyze dimension reduction factor except under method choose principal axis factoring. Questionnaire evaluation with factor analysis and cronbachs. Only components with high eigenvalues are likely to represent a real underlying factor. Within this dialogue box select the following check boxes univariate descriptives, coefficients, determinant, kmo and bartletts test of sphericity, and reproduced. In summary, for pca, total common variance is equal to total variance explained.
This can be checked by looking at scatterplots of pairs of variables. The offdiagonal elements the values on the left and right side of diagonal in the table below should all be. Tabachnick and fidell 2001, page 588 cite comrey and lees 1992 advise regarding sample size. Click on the check box of suppress absolute values less than to select it. If a factor explains lots of variance in a dataset, variables correlate highly with that factor, i. The variables used in factor analysis should be linearly related to each other. Factor analysis in spss to conduct a factor analysis reduce. Fortunately, we do not have to do a factor analysis in order to determine. Be able to select and interpret the appropriate spss output from a principal component analysis factor analysis. Although this format is often convenient, when interpreting factors it can be useful to list variables by size. Exploratory factor analysis and principal components analysis exploratory factor analysis efa and principal components analysis pca both are methods that are used to help investigators represent a large number of relationships among normally distributed or scale variables in a simpler more parsimonious way. Apr 09, 2018 this video provides a brief demonstration of how to carry out an exploratory factor analysis in amos using the specification search option. Rotation dialogue box from the factor analysis dialogue box click on the options button and its dialogue box will be loaded on the screen.