Repeat for both factors. Learn to Perform a Confirmatory Factor Analysis (CFA) in SPSS AMOS With Data From the International Sponsorship Study (2016) 2 An Example in AMOS: Animosity to Germany and Ethnocentrism. Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis (4th Ed.). We will use this later on to populate the model with variables. Clicking on the Variable List icon (see Figure 5), drag the relevant observed variables to the rectangular (observed variable) boxes in the model. It uses the maximum likelihood extraction as it is the algorithm used in AMOS. Some good introductory sources are: An additional practice example is suggested at the end of this guide. Can CFA be performed with the SPSS FACTOR procedure? Read more about Jeff here. For example, variables X1 to X4 load on factor 1; X5 to X8 on factor 2; X9 to X12 on factor 3. This can be done using the Indicator icon, which is extremely useful since it draws all of the constituent parts of the latent factor for you. The Factor procedure that is available in the SPSS Base module is essentially limited to exploratory factor analysis (EFA). (You don't really confirm the model so much as you fail to reject it, adhering to strict hypothesis testing philosophy.) and also from my SPSS data page. When conducting a CFA, it is always good practice to examine each variable before performing further analyses. Do the results change much? SPSS Inc. was acquired by IBM in October, 2009. confirmatory factor analysis spss. The predominant CFA approach today is to consider CFA as a special case of structural equation modeling (SEM). Interpreting Confirmatory Factor Analysis Output from Mplus May 15, 2013 | 4 Comments Being able to find SPSS in the start menu does not qualify you to run a multi-nomial logistic regression. It has a graphical user interface that makes it fairly straightforward to express your CFA model on the screen. Doing the same for the errors, label each consecutively (e1, e2, e3, e4, e5, e6, e7). The seven observed variables are as follows: All seven variables are measured on a 7-point Likert scale from 1 = Strongly disagree to 7 = Strongly agree. This example presents a CFA using data from the International Sponsorship Survey (ISS, 2016). For better or worse? This is known as “confirmatory factor analysis”. Some readers will prefer to extract factor loadings (λ) and R2 directly from this. This is conducted after exploratory factor analysis (EFA) to determine the factor structure of your dataset. If not, is CFA available from any other SPSS procedure or product? To do so, click: Analysis Properties (icon) → Output → check Standardized estimates → Exit. To observe whether they are statistically significant at the p < .05 level, it is necessary to switch to the “Regression Weights” tab, representing unstandardized coefficients. Note: If the full label appears for each variable, follow this sequence: View → Interface Properties → Misc → untick Display Variable Labels. New York: Guilford Press. Confirmatory Factor Analysis 1. If you choose maximum likelihood (ML) or generalized least squares (GLS) as your extraction method, you would get a chi-square measure of goodness of fit, which is a test of the null hypothesis that 3 factors were adequate to explain the covariances among your variables. Hovering above the stars given in the “P” column shows parameter significance. (2005). Between the two ellipses add a double-headed covariance line from the icon screen. The Factor procedure that is available in the SPSS Base module is essentially limited to exploratory factor analysis (EFA). You specify factor loadings as a set of regression statements from the factor to the observed variables. Most researchers would therefore report it providing a caution to the reader. CFA allows the researcher to establish whether a pool of observed variables, underlying broader theoretically derived concepts, can be reduced into a smaller number of latent factors. Each variable should occupy a box. Exploratory Factor Analysis. To provide our customers with SEM capability (including CFA), SPSS distributes AMOS, a SEM program developed by James Arbuckle at AMOS Development Corp. (http://www.amosdevelopment.com/ ). As such, CFA is used for several purposes including scale development and as a foundation for latent regression analysis and structural equation modelling (SEM). You would get a measure of fit of your data to this model. What is confirmatory factor analysis (CFA)? Mahwah NJ: Erlbaum. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. Confirmatory factor analysis (CFA) is a multivariate statistical procedure that is used to test how well the measured variables represent the number of constructs. Search results are not available at this time. EFA … You will find links to the dataset, and you are encouraged to replicate this example. Our results support the conclusion that the two latent factors (animosity and ethnocentrism) are strong reflections of the associated observed variables. Hovering over one of the latent factors, right click and select the following (Figure 4): In the Variable Name box insert the latent variable names (i.e., Animosity). The most fundamental model in CFA is the one factor model, which will assume that the covariance (or correlation) among items is due to a single common factor. With exploratory factor analysis, you can request 3 factors and a particular rotation and look at the results to see if they match your model. Data should be continuous and include a sufficient number of observed variables to allow the model to be “identified.”. A current list of IBM trademarks is available on the Web at “IBM Copyright and trademark information” at http://www.ibm.com/legal/copytrade.shtml. With respect to Correlation Matrix if any pair of variables has a value less than 0.5, consider dropping one of them from the analysis (by repeating the factor analysis test in SPSS by removing variables whose value is less than 0.5). MGFA is an approach to confirmatory factor analysis (CFA). Moving on to conduct a SEM or test for observed heterogeneity in this model (multigroup CFA) would now be feasible. RMSEA is the default model and has its own tab. Goodness of fit tests and measures are provided, along with diagnostic information to help you determine weak points in the model. Confirmatory factor analysis (CFA) is a multivariate statistical procedure that is used to test how well the measured variables represent the number of constructs. Steps of conducting Confirmatory Factor Analysis (CFA) in R The CFA requires the model/structure to be specified. Having done this for both latent factors (i.e., all seven observed variables), your model should look something like the one in Figure 6. Alternatively, click on the Text Output icon, which produces lots of information. AMOS will read several data file formats, including SPSS data files. Once estimated, Click view results (red arrow). We find that the model is an acceptable to good fit to the sample data based on commonly accepted thresholds (χ2 = 34.5, df = 13, p < .01, CFI = .97, TLI =.95, RMSEA = .12). Finally, re-estimate the model with the “eighth” variable. We now need to request the software to provide output. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). Factor analysis is used in many fields such as behavioural and social sciences, medicine, economics, and geography as a result of the technological advancements of computers.The two main factor analysis techniques are Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). One Factor Confirmatory Factor Analysis The most fundamental model in CFA is the one factor model, which will assume that the covariance (or correlation) among items is due to a single common factor.Much like exploratory common factor analysis, we will assume that total variance can be partitioned into common and unique variance. Factor loadings are sort of the regression coefficients of the items against the underlying factors or categories, if in fact, you could measure those underlying factors. The example assumes you have already opened the data file in SPSS and a new project in AMOS. The first step is to transfer the SPSS data into AMOS using the Select Data File icon: Select Data file → File Name (select file) → OK. You can check that the file has loaded properly by clicking on the Variable List icon, which loads a list of all the variables in the dataset. The data for this lesson are available at T&F’s data site. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming (2nd Ed.). Download the file and bring it … It essentially involves computing factor scores that are the weighted sums of variables that are presumed to load on the respective factors. Much like exploratory common factor analysis, we will assume that total variance can be partitioned into common and unique variance. Confirmatory Factor Analysis CFA) was performed using SPSS AMOS version 20 to report on the theoretical relationships between the observedand unobserved variables in QUID including if the hypothesized model was a good fit to the observed data. Comprehensive and comprehensible, this classic covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. The dataset is a subset derived from the 2016 International Sponsorship Study (ISS 2016) conducted by researchers at Cardiff University. Watson Product Search AMOS requires the user to draw the model before it can be estimated. 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. 16 June 2018, [{"Product":{"code":"SSLVC7","label":"SPSS Amos"},"Business Unit":{"code":"BU053","label":"Cloud & Data Platform"},"Component":"Not Applicable","Platform":[{"code":"PF025","label":"Platform Independent"}],"Version":"Not Applicable","Edition":"","Line of Business":{"code":"LOB10","label":"Data and AI"}}], Confirmatory Factor Analysis (CFA) in SPSS Factor, http://www.ibm.com/software/analytics/spss/support/spss_license.html. Before going any further new users to AMOS Graphics (herein AMOS) may wish to familiarize themselves with the main window (see Figure 1) and several of the more critical icons in the package (see Figure 2). (2006). Modified date: The results table is shown in Figure 8. Confirmatory Factor Analysis for Applied Research. We do note that the RMSEA is slightly above the accepted threshold (<.10) but not alarmingly so. Principles and Practice of Structural Equation Modeling (2nd Ed.). The scientific-mind factors consisted of two indicators, Confirmatory Factor Analysis Professor Patrick Sturgis 2. Confirmatory factor analysis for all constructs is an important first step before developing a structural equation model. Critically, the data suggest that all factor loadings are high (i.e., > .70). Confirmatory factor analysis would then check that these categories are relevant to the demographic you have. Confirmatory Factor Analysis The model fit is derived from comparing the correlations (technically, the covariances) among the items to the correlations expected by the model being tested. Its aim is to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for … Having finished the specification (i.e., drawing) you can now estimate the model. Click the Calculate Estimates icon (piano keys). CFI/TLI are found in baseline comparisons (in CMIN tab). Learn to Perform a Confirmatory Factor Analysis (CFA) in SPSS AMOS With Data From the International Sponsorship Study (2016) This dataset is designed for teaching Confirmatory Factor Analysis (CFA) using the AMOS 24.0 software package. Search support or find a product: Search. Finally, we consult the global indices of model fit. AMOS benefits from showing the model results directly on the graphic itself. Loadings which are not specified are assumed to be fixed at 0. You can download this sample data, which also includes another variable labelled “extra.” See whether you can reproduce the results presented here, and then, looking at the wording of the additional variable, decide whether this is best reflected by the animosity or ethnocentrism latent factor. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are similar techniques, but in exploratory factor analysis (EFA), data is simply explored and provides information about the numbers of factors required to represent the data. Testing of scientific-mind measurement was used as the research instrument and construct validity testing of the scientific-mind measurement model utilized second-order confirmatory factor analysis (CFA) was carried out with SPSS AMOS software Version 23. Mathematically, certain models imply certain correlations, e.g ., if one-factor model, items should be highly correlated, items that do Confirmatory Factor Analysis (CFA) is a special form of factor analysis. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. The next stage is to draw the measurement model. This is your ethnocentrism factor. In the Text Output box, click Model Fit. Plan • Measuring concepts using latent variables • Exploratory Factor Analysis (EFA) • Confirmatory Factor Analysis (CFA) • Fixing the scale of latent variables • Mean structures • Formative indicators • Item parcelling • Higher-order factors 3. From the Text Output box, click: Estimates → Scalars → Standardized Regression Weights. Since this has been covered in other datasets, we focus on the main CFA operation but highlight that several of the animosity items have positive skewness and kurtosis. Running Preliminary Analysis for Multivariate Statistics using SPSS. Then, holding the click, draw an ellipse on the page using the mouse. , file CFA-Wisc.sav. Confirmatory factor analysis (CFA) is used to study the relationships between a set of observed variables and a set of continuous latent variables. Now add a second latent factor, this time adding three observed variables. Starting with the animosity latent factor, click four times to represent its four observed variables. Analysis class in the Psychology Department at the University at Albany. There is also a recent book which focuses on SEM with AMOS and includes several CFA examples: The goal of this document is to outline rudiments of Confirmatory After selecting the Indicator icon move to the blank Path Diagram page. 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. This video describes how to perform a factor analysis using SPSS and interpret the results. model utilized second-order confirmatory factor analysis (CFA) was carried out with SPSS AMOS software Version 23. Brown, T.A. In this guide, you will learn how to produce a Confirmatory Factor Analysis (CFA) in IBM® SPSS® AMOS Graphics software using a practical example to illustrate the process. Confirmatory factor analysis (CFA) is a highly complex statistical technique that is used to confirm or validate the internal structure of the survey that was yielded from reliability and Principal Components Analysis (PCA). This step-by-step tutorial will walk you through doing an exploratory factor analysis (EFA) in SPSS to come-up with a clean pattern matrix to be used in confirmatory factor analysis (CFA) part of structural equation modeling (SEM) in SPSS-AMOS. One Factor Confirmatory Factor Analysis. SPSS does not offer structural equation modeling techniques. This example presents a CFA using data from the International Sponsorship Survey (ISS, 2016). (See Technote #1476881, "Multiple Group Factor Analysis in SPSS") for a discussion of multiple group factor analysis, an approach to CFA that could be addressed in part through SPSS). Specifically, a pool of seven observed variables, used to capture 123 English respondent’s animosity towards … Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. SPSS does not include confirmatory factor analysis but those who are interested could take a look at AMOS. Specifically, a pool of seven observed variables, used to capture 123 English respondent’s animosity towards Germany (four variables) and their ethnocentrism towards other countries generally (three variables), is reflected in a two-latent factor measurement model. New York: Guilford. You would not get a test of whether the factor loading matrix conformed to your model. IBM® SPSS® Statistics software (SPSS) screenshots Republished Courtesy of International Business Machines Corporation, © International Business Machines Corporation. See more information on acquiring AMOS at http://www.ibm.com/software/analytics/spss/support/spss_license.html . This is supported by AMOS, a ‘sister’ package to SPSS. Your model should approximately look like the one in Figure 3. Loehlin, John C. (2004). Confirmatory factor analysis (CFA) is a highly complex statistical technique that is used to confirm or validate the internal structure of the survey that was yielded from reliability and Principal Components Analysis (PCA). Once a questionnaire has been validated, another process called Confirmatory Factor Analysis can be used. As suggested by others, for Confirmatory Factor Analysis you will have to use special software like AMOS, LISREL, EQS etc. To begin, we should look at the standardized factor loadings for each factor. Search, None of the above, continue with my search. Kline, R.B. This is shown in Figure 7. This can be done in SPSS. The purpose of an EFA is to describe a multidimensional data set using fewer variables. New York: Routledge. In this case, all are highly significant (p < .01). If in the EFA you explore the factor structure, here in CFA, you confirm the factor structure you extracted in the EFA. No results were found for your search query. Findings - The testing of the scientific-mind measurement model for secondary school students in Bangkok was consistent with the empirical data. In confirmatory factor analysis (CFA), you specify a model, indicating which variables load on which factors and which factors are correlated. Next, click the ellipse shape as many times as you have observed variables. AMOS is a separate program and would be stored in a separate directory from SPSS. Until the early to mid 1970's, there were a handful of ways to approach CFA, but many of these seem to have fallen by the wayside. SPSS Amos 23 * is the preferable software package for running this type of analysis. The reader can scroll through these metrics as they require. The specific focus in factor analysis is understanding which variables are associated with which latent constructs. Other product and service names might be trademarks of IBM or other companies. The data set is the WISC-R data set that the multivariate statistics textbook by the Tabachnick textbook (Tabachnick et al., 2019) employs for confirmatory factor analysis illustration. You can move or rotate the factor using the lorry icon or the rotate icon. You now have one latent factor ready to populate. Confirmatory Factor Analysis With AMOS. Please try again later or use one of the other support options on this page. Byrne, Barbara M. (2010). For the purpose of demonstration, we retain the raw data. Report the findings. But what if I don't have a clue which -or even how many- factors are represented by my data? IBM, the IBM logo, ibm.com, and SPSS are trademarks or registered trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Check here to start a new keyword search. How-to Guide for AMOS in IBM® SPSS® Statistics Software, An Example in AMOS: Animosity to Germany and Ethnocentrism, 2 An Example in AMOS: Animosity to Germany and Ethnocentrism, Germany is not a reliable trading partner (ANI4), It is not right to purchase foreign products because it puts English people out of jobs (Ethno1), We should purchase products manufactured in England instead of letting other countries get rich off us (Ethno2), English people should not buy foreign products because it hurts English businesses and causes unemployment (Ethno3). The next task is to provide a Name for the latent factors (ellipses) and errors (small circles). We introduce these concepts within the framework of confirmatory factor analysis (CFA), which restricts analyses to those used to evaluate measurement models. 1. The multiple groups refer to groups of variables, not subsamples of cases. Suppose that you have a particular factor model in mind. In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social research.