Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. This technique extracts maximum common variance from all variables and puts them into a common score. As an index of all variables, we can use this score for further analysis. Factor analysis is part of general linear model (GLM) and this method also assumes several assumptions: there is linear relationship, there is no multicollinearity, it includes relevant variables into analysis, and there is true correlation between variables and factors. Several methods are available, but principal component analysis is used most commonly. Show
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Types of factoring: There are different types of methods used to extract the factor from the data set: 1. Principal component analysis: This is the most common method used by researchers. PCA starts extracting the maximum variance and puts them into the first factor. After that, it removes that variance explained by the first factors and then starts extracting maximum variance for the second factor. This process goes to the last factor. 2. Common factor analysis: The second most preferred method by researchers, it extracts the common variance and puts them into factors. This method does not include the unique variance of all variables. This method is used in SEM. 3. Image factoring: This method is based on correlation matrix. OLS Regression method is used to predict the factor in image factoring. 4. Maximum likelihood method: This method also works on correlation metric but it uses maximum likelihood method to factor. 5. Other methods of factor analysis: Alfa factoring outweighs least squares. Weight square is another regression based method which is used for factoring. Factor loading: Criteria for determining the number of factors: According to the Kaiser Criterion, Eigenvalues is a good criteria for determining a factor. If Eigenvalues is greater than one, we should consider that a factor and if Eigenvalues is less than one, then we should not consider that a factor. According to the variance extraction rule, it should be more than 0.7. If variance is less than 0.7, then we should not consider that a factor. Rotation method: Rotation method makes it more reliable to understand the output. Eigenvalues do not affect the rotation method, but the rotation method affects the Eigenvalues or percentage of variance extracted. There are a number of rotation methods available: (1) No rotation method, (2) Varimax rotation method, (3) Quartimax rotation method, (4) Direct oblimin rotation method, and (5) Promax rotation method. Each of these can be easily selected in SPSS, and we can compare our variance explained by those particular methods. Assumptions:
Key concepts and terms: Exploratory factor analysis:Assumes that any indicator or variable may be associated with any factor. This is the most common factor analysis used by researchers and it is not based on any prior theory. Confirmatory factor analysis (CFA):Used to determine the factor and factor loading of measured variables, and to confirm what is expected on the basic or pre-established theory. CFA assumes that each factor is associated with a specified subset of measured variables. It commonly uses two approaches:
Resources Bryant, F. B., & Yarnold, P. R. (1995). Principal components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate analysis. Washington, DC: American Psychological Association. Dunteman, G. H. (1989). Principal components analysis. Newbury Park, CA: Sage Publications. Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. Gorsuch, R. L. (1983). Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum Associates. Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis with readings (4th ed.). Upper Saddle River, NJ: Prentice-Hall. Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute. Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. Thousand Oaks, CA: Sage Publications. Kim, J. -O., & Mueller, C. W. (1978a). Introduction to factor analysis: What it is and how to do it. Newbury Park, CA: Sage Publications. Kim, J. -O., & Mueller, C. W. (1978b). Factor Analysis: Statistical methods and practical issues. Newbury Park, CA: Sage Publications. Lawley, D. N., & Maxwell, A. E. (1962). Factor analysis as a statistical method. The Statistician, 12(3), 209-229. Levine, M. S. (1977). Canonical analysis and factor comparison. Newbury Park, CA: Sage Publications. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage Publications. Shapiro, S. E., Lasarev, M. R., & McCauley, L. (2002). Factor analysis of Gulf War illness: What does it add to our understanding of possible health effects of deployment, American Journal of Epidemiology, 156, 578-585. Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy. Boston, MA: Kluwer. Widaman, K. F. (1993). Common factor analysis versus principal component analysis: Differential bias in representing model parameters, Multivariate Behavioral Research, 28, 263-311. Related Pages:
Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include: Data Analysis Plan Edit your research questions and null/alternative hypotheses Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references Justify your sample size/power analysis, provide references Explain your data analysis plan to you so you are comfortable and confident Two hours of additional support with your statistician Quantitative Results Section (Descriptive Statistics, Bivariate and Multivariate Analyses, Structural Equation Modeling, Path analysis, HLM, Cluster Analysis) Clean and code dataset Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate) Conduct analyses to examine each of your research questions Write-up results Provide APA 6th edition tables and figures Explain chapter 4 findings Ongoing support for entire results chapter statistics Please call 727-442-4290 to request a quote based on the specifics of your research, schedule using the calendar on this page, or email [email protected] What is the term for the method for analyzing accounts that are based on one single factor typically the level of sales potential?Describe two techniques for account classification.
Single-Factor Analysis-Single-factor analysis, also referred to as ABC analysis, is the simplest and most often used method for classifying accounts.
What is the term for the method for analyzing accounts that is based on one single factor?single factor/ ABC analysis. A method for analyzing accounts that is based on one single factor, typically the level of sales potential. Also called ABC analysis./ most often used for classifying accounts. portfolio analysis. A method for analyzing accounts that allows two factors to be considered simultaneously.
What is territory analysis and account classification?Personal goal, territory goal, account goal, sales call goal. Territory analysis. surveying an area to determine customers and prospects who are most likely to buy. Account classification. process of categorizing existing customers and prospects based on their potential as a customer.
Which of the following is a benefit of single factor analysis?It classifies accounts on the basis of sales potential. Which of the following is a benefit of single-factor analysis? It requires no data manipulation.
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