What are the assumptions of factor analysis?
The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
What is rotation in factor analysis?
Rotations minimize the complexity of the factor loadings to make the structure simpler to interpret. Rotation of the factor loading matrices attempts to give a solution with the best simple structure. There are two types of rotation: Orthogonal rotations constrain the factors to be uncorrelated.
How do you interpret a rotated component matrix in factor analysis?
The rotated component matrix, sometimes referred to as the loadings, is the key output of principal components analysis. It contains estimates of the correlations between each of the variables and the estimated components.
What is KMO and Bartlett’s test?
The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in your variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful with your data.
How do you interpret eigenvalues in factor analysis?
Any factor with an eigenvalue ≥1 explains more variance than a single observed variable. So if the factor for socioeconomic status had an eigenvalue of 2.3 it would explain as much variance as 2.3 of the three variables.
What is the purpose of exploratory factor analysis?
Exploratory factor analysis (EFA) is generally used to discover the factor structure of a measure and to examine its internal reliability. EFA is often recommended when researchers have no hypotheses about the nature of the underlying factor structure of their measure.
What is exploratory factor analysis when it is applied?
Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It is used to identify the structure of the relationship between the variable and the respondent.
What is the difference between confirmatory factor analysis and exploratory factor analysis?
In exploratory factor analysis, all measured variables are related to every latent variable. But in confirmatory factor analysis (CFA), researchers can specify the number of factors required in the data and which measured variable is related to which latent variable.
What is the minimum sample size for factor analysis?
Minimum Sample Size Recommendations for Conducting Factor Analyses. There is no shortage of recommendations regarding the appropriate sample size to use when conducting a factor analysis. Suggested minimums for sample size include from 3 to 20 times the number of variables and absolute ranges from 100 to over 1,000.
Can you do a confirmatory factor analysis in SPSS?
The Factor procedure that is available in the SPSS Base module is essentially limited to exploratory factor analysis (EFA). In confirmatory factor analysis (CFA), you specify a model, indicating which variables load on which factors and which factors are correlated.
How do you interpret Communalities in factor analysis?
a. Communalities – This is the proportion of each variable’s variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables. b.
Why do we use Amos?
AMOS is statistical software and it stands for analysis of a moment structures. AMOS is an added SPSS module, and is specially used for Structural Equation Modeling, path analysis, and confirmatory factor analysis. It is also known as analysis of covariance or causal modeling software.
How do you save Amos output?
Option 1: When you get the error message click OK, you then get an option to save the file. Save the file to a location you can find again in a moment, somewhere like the desktop for example. Now click close in the error message window and click on the red cross to close the AMOS output window.
Can standardized factor loadings be greater than 1?
Who told you that factor loadings can’t be greater than 1? It can happen. Especially with highly correlated factors. However, if the factors are correlated (oblique), the factor loadings are regression coefficients and not correlations and as such they can be larger than one in magnitude.”
How do you interpret standardized coefficients?
A standardized beta coefficient compares the strength of the effect of each individual independent variable to the dependent variable. The higher the absolute value of the beta coefficient, the stronger the effect. For example, a beta of -. 9 has a stronger effect than a beta of +.
How do you interpret B in linear regression?
If the beta coefficient is positive, the interpretation is that for every 1-unit increase in the predictor variable, the outcome variable will increase by the beta coefficient value.
What is the difference between unstandardized and standardized coefficients?
Unlike standardized coefficients, which are normalized unit-less coefficients, an unstandardized coefficient has units and a ‘real life’ scale. An unstandardized coefficient represents the amount of change in a dependent variable Y due to a change of 1 unit of independent variable X.