For this computer assignment, you will conduct a series of principal factor analyses to examine the factor structure of a. Use principal components analysis pca to help decide. Sample factor analysis writeup exploratory factor analysis of the short version of the adolescent coping scale. A factor extraction method developed by guttman and based on image theory. Factor analysis is also used to verify scale construction. The plot above shows the items variables in the rotated factor space. The offdiagonal elements the values on the left and right side of diagonal in the table below should all be. To run a factor analysis, use the same steps as running a pca analyze dimension reduction factor except under method choose principal axis factoring. As for the factor means and variances, the assumption is that thefactors are standardized. Factor scores, structure and communality coefficients.
The alternative methods for calculating factor scores are regression, bartlett, and andersonrubin. 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. 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. Note that we continue to set maximum iterations for convergence at. 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. The broad purpose of factor analysis is to summarize. Development of psychometric measures exploratory factor analysis efa validation of psychometric measures confirmatory factor analysis cfa cannot be done in spss, you have to use e. Exploratory factor analysis smart alexs solutions task 1 reruntheanalysisinthischapterusingprincipalcomponentanalysisandcomparethe resultstothoseinthechapter. Results including communalities, kmo and bartletts test, total. With respect to correlation matrix if any pair of variables has a value less than 0. In the descriptives window, you should select kmo and bartletts test of sphericity. Factor analysis has no ivs and dvs, so everything you want to get factors for just goes into the list labeled variables. 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.
Exploratory and confirmatory factor analysis in gifted. As part of a factor analysis, spss calculates factor scores and automatically saves them in the data file, where they are easily accessible for further analyses see table 2. This video describes how to perform a factor analysis using spss and interpret the results. Principal components analysis pca using spss statistics. Figure 5 the first decision you will want to make is whether to perform a principal components analysis or a principal factors analysis. Be able explain the process required to carry out a principal component analysis factor analysis. Spss factor analysis syntax show both variable names and labels in output. Ibm spss statistics 23 is wellsuited for survey research, though by no means is it limited to just this topic of exploration. Pcaspss factor analysis principal component analysis. Factor analysis is a collection of methods used to examine how underlying constructs inuence the responses on a number of measured variables. Principal components pca and exploratory factor analysis.
Although spss anxiety explain some of this variance, there may be systematic factors such as technophobia and nonsystemic factors that cant be explained by. Exploratory factor analysis efa researchers use exploratory factor analysis when they are interested in a attempting to reduce the amount of data to be used in subsequent analyses or b determining the number and character of underlying or latent factors in a data set. A comparison of factor analysis programs in spss, bmdp, and. Factor analysis in spss principal components analysis part 2 of 6 duration. Kaisermeyerolkin measure of sampling adequacy this measure varies between 0 and 1, and values closer to 1 are better. Focusing on exploratory factor analysis quantitative methods for. Univariate descriptives includes the mean, standard deviation, and number of valid cases for each variable. Factor analysis using spss ml model fitting direct quartimin, promax, and varimax rotations of 2factor solution. Conduct and interpret a factor analysis statistics solutions. Factor analysis programs in sas, bmdp, and spss are discussed and compared in terms of documentation, methods and options available, internal logic, computational accuracy, and results provided. Factor analysis is based on the correlation matrix of the variables involved, and correlations usually need a large sample size before they stabilize. Similar to factor analysis, but conceptually quite different.
Overview this tutorial looks at the popular psychometric procedures of factor analysis, principal component analysis pca and reliability analysis. C8057 research methods ii factor analysis on spss dr. In this process, the following facets will be addressed, among others. A factor extraction method that considers the variables in the analysis to be a sample from the universe of potential variables. Initial solution displays initial communalities, eigenvalues, and the percentage of variance explained correlation matrix. Perhaps the strongest is that the book provides only a shallow coverage of factor analysis. Simple structure is a pattern of results such that each variable loads highly onto one and only one factor. Be able to select and interpret the appropriate spss output from a principal component analysis factor analysis. An online book manuscript by ledyard tucker and robert maccallum that provides an extensive technical treatment of the factor analysis model as well as methods for conducting exploratory factor analysis. Such underlying factors are often variables that are difficult to measure such as iq, depression or extraversion. Principal components analysis pca, for short is a variablereduction technique that shares many similarities to exploratory factor analysis. Factor analysis is a statistical technique for identifying which underlying factors are measured by a much larger number of observed variables.
Be able to carry out a principal component analysis factor analysis using the psych package in r. University of northern colorado abstract principal component analysis pca and exploratory factor analysis efa are both variable reduction techniques and sometimes mistaken as the same statistical method. To save space each variable is referred to only by. Based on these comparisons, recommendations are offered which include a clear overall preference for sas, and advice against.
Within this dialogue box select the following check boxes univariate descriptives, coefficients, determinant, kmo and bartletts test of sphericity, and reproduced. You can do this by clicking on the extraction button in the main window for factor analysis see figure 3. Factor transformation matrix this is the matrix by which you multiply the unrotated factor matrix to get the rotated factor matrix. Summarised extract from neill 1994 summary of the introduction as related to the factor analysis. Click on the descriptives button and its dialogue box will load on the screen.
For an iterated principal axis solution spss first estimates communalities, with r. However, there are distinct differences between pca and efa. Exploratory factor analysis efa attempts to discover the nature of the constructs inuencing a set of. Factor analysis in spss to conduct a factor analysis, start from the analyze menu. The technique involves data reduction, as it attempts to represent a set of variables by a smaller number. Once your measurement model turns out statistically significant, you may calculate factor score of the latent variables on the basis of the factor analysis. This form of factor analysis is most often used in the context of structural equation modeling and is referred to as confirmatory factor analysis.
The following paper discusses exploratory factor analysis and gives an overview of the statistical. Factor analysis and pca are often confused, and indeed spss has pca as a method of factor analysis. Exploratory factor analysis university of groningen. The rest of the output shown below is part of the output generated by the spss syntax shown at the beginning of this page. The available options are coefficients, significance levels, determinant, kmo and bartletts test of sphericity, inverse, reproduced, and antiimage. Its aim is to reduce a larger set of variables into a smaller set of artificial variables, called principal components, which account for. In the factor analysis window, click scores and select save as variables, regression, display factor score coefficient matrix. Factor analysis in spss to conduct a factor analysis.
The kaisermeyerolkin measure of sampling adequacy is a statistic that indicates the proportion of variance in your variables that might be caused by underlying factors. 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. The sample is adequate if the value of kmo is greater than 0. The simple scatter plot is used to estimate the relationship between two variables figure 2 scatterdot dialog box. Table 2 is a factor score matrix for our population of 301 participants on the six variables. Aug 19, 2014 this video describes how to perform a factor analysis using spss and interpret the results. Unfortunately the book has a number of problems, at least for my purposes.
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. Andy field page 5 162004 interpreting output from spss select the same options as i have in the screen diagrams and run a factor analysis with orthogonal rotation. Confirmatory factor analysis cfa starts with a hypothesis about how many factors there are and which items load on which factors. Factor analysis is a statistical data reduction and analysis technique that strives to explain correlations among multiple outcomes as the result of one or more underlying explanations, or factors. This method maximizes the alpha reliability of the factors. The scores that are produced have a mean of 0 and a variance equal to the squared multiple correlation between the estimated factor scores and the true factor values. Pdf expert sessions delivered on factor analysis and structure equation modeling using spss and amos in national level two week. Factor analysis researchers use factor analysis for two main purposes. Exploratory factor analysis 4 in spss a convenient option is offered to check whether the sample is big enough. Efa, in contrast, does not specify a measurement model initially and usually seeks to discover the measurement model.
It is an assumption made for mathematical convenience. Interpreting spss output for factor analysis youtube. Chapter 4 exploratory factor analysis and principal. Andy field page 5 10122005 interpreting output from spss select the same options as i have in the screen diagrams and run a factor analysis with orthogonal rotation. I discuss how to enter the data, select the various options, interpret the output e. This discussion includes screen shots of the various dialogs. This table shows two tests that indicate the suitability of your data for structure detection. For example, a confirmatory factor analysis could be. In the scatterdot dialog box, make sure that the simple scatter option is selected, and then click the define button see figure 2.
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. Factor loadings and factor correlations are obtained as in efa. Furthermore, spss can calculate an antiimage matrix. After providing an overview of factor analysis, the book launches into how spss and sas can be used for factor analysis. Using efa to explore the underlying dimensions of the construct of interest. Factor analysis is commonly used in the fields of psychology and education6 and is considered the method of choice for interpreting selfreporting questionnaires. Factor variables v1 v2 v3 v4 v5 v6 v7 v8 v9 v11 v12 v v14 v16 v17 v20 missing pairwise important. Running a common factor analysis with 2 factors in spss. Nov 11, 2016 simple structure is a pattern of results such that each variable loads highly onto one and only one factor. Run this stepbystep example on a downloadable data file.
Ml model fitting direct quartimin, promax, and varimax rotations of 2 factor solution. Factor analysis using spss 2005 discovering statistics. Note that we continue to set maximum iterations for convergence at 100 and we will see why later. Relationship to factor analysis principal component analysis looks for linear combinations of the data matrix x that are uncorrelated and of high variance. Development of psychometric measures exploratory factor analysis efa validation of psychometric measures confirmatory factor analysis cfa cannot be done in spss, you have to use. Pdf expert sessions delivered on factor analysis and structure equation modeling using spss and amos in national level two week faculty development. This procedure is intended to reduce the complexity in a set of data, so we choose data reduction.
To save space each variable is referred to only by its label on the data editor e. 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. Jun 30, 2011 i demonstrate how to perform and interpret a factor analysis in spss. Spss will extract factors from your factor analysis. Some problems with respect to logic and output are described. Exploratory factor analysis columbia university mailman. This video demonstrates how interpret the spss output for a factor analysis. Factor analysis in spss to conduct a factor analysis reduce. A comparison of factor analysis programs in spss, bmdp. In such applications, the items that make up each dimension are specified upfront. Based on these comparisons, recommendations are offered which include a clear overall preference for sas, and advice against general use of.
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