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Factor Analysis

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of potentially lower number of unobserved variables called factors. It is possible that the variations in three or four observed variables mainly reflect the variations in fewer unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. Factor analysis is related to principal component analysis but the two are not identical.

There are two types of factor analysis, exploratory analysis, EFA, and confirmatory factor analysis, CFA. EFA is used to identify complex interrelationships among items and group items that are part of unified concepts. CFA is a more complex approach that tests the hypothesis that the items are associated with specific factors. CFA uses structural equation modeling to test a measurement model whereby loading on the factors allows for evaluations of relationships between observed variables and unobserved variables. There are also several types of factoring. These include Principal component analysis, canonical factor analysis, common factor analysis, image factoring, alpha factoring and factor regression models.

Factor analysis has been implemented in several statistical analysis programs. It is also implemented in the R programming language and in OpenOpt. Rotations are implemented in the GPArotation R package. 

Definitions and interpretations

Explain what the Box's M, the canonical correlation, the Chi-square, standardized canonical discriminant function coefficients, structure matrix, and the classification statistics mean and how they are interpreted?

Solve one-way ANOVA problem using excel with a real example

Part I Jackson, even-numbered Chapter Exercises, pp. 335-337. 2. How many independent variables are in a 4 x 6 factorial design? How many conditions (cells) are in this design? There are two independent variables in a 4 x 6 design. One variable has four levels, and the other has six levels. There would be 24 conditions in a 4

Perform MANOVA to test three different versions

In this experiment, the researcher is testing three different versions of the new medication. In data file "Activity 8.sav" you will find the following variables: • Drug (0=control, 1=Drug A, 2=Drug B, 3=Drug C), • LDL and HDL (cholesterol numbers of participants after 12 weeks). Using a MANOVA, try to determine which ver

Quantitative methods in healthcare management

The following exercises are required and cover topics in this week's required reading. Review the instructions carefully before completing and submitting the exercises. Complete Exercises 11.1 and 11.6 on pages 285 and 288 in Quantitative Methods in Health Care Management. Use this template for your calculations and answers:

Observed Time, Normal Time, and Standard Time

Exercises: 6.2 and 6.5 6235 (Wk 3) The following exercises are required and cover topics in this week's required reading. Review the instructions carefully before completing and submitting the exercises. Complete Exercises 6.2 and 6.5 on pages 156 and 158 in Quantitative Methods in Health Care Management. Use the Excel docum

Repeated Measure Two Way MANOVA- using SPSS

6.31. Peanuts are an important crop in parts of the southern United States. In an effort to develop improved plants, crop scientists routinely compare varieties with respect to several variables.The data for one two-factor experiment are given in Table 6.17 on page 354. Three varieties (5, 6, and 8) were grown at two geograph

Question in research analysis class

3- -paragraph explanation of when multivariate analysis is appropriate for a quantitative study. Describe the Factorial ANOVA multivariate statistical type test and its potential usefulness when testing the impact of more than one independent variable (factor) on one dependent variable. For example the DV is stress. I will ne

Factor analysis using SPSS

Factor Analysis 1.A researcher is examining factors that predict language development among first grade students. The researcher believes that some of the variables may be correlated and would like to run factor analysis to reduce multicollinearity. The researcher would like to use factor analysis to examine the following v


Check the assumptions of normality, linearity, homoscedasticity, and collinearity. Describe and provide support regarding whether the assumptions were met (Include supporting tables/graphs). Stage 5. Multiple regression 1. Perform multiple regression to identify whether there is an association between the independent vari