The best way to analyse a Likert scale and interpret the results using exploratory factor analysis
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Email: [email protected]
WhatsApp: +212619398603 / wa.link/l6jvny
Common types of Factor Analysis (FA) in academic research and journal articles:
1. Principal components analysis with direct oblimin
2. Maximum likelihood with varimax rotation technique
3. Principal axis factoring with Promax rotation technique
What type of factor analysis estimation method and rotation techniques to use, when and why? and what is the difference between Principal Components Analysis (PCA) and Factor Analysis (FA) with Principal Axis Factoring and Maximum Likelihood? What is the difference between varimax rotation and promax rotation?
1. Principal components analysis with direct oblimen combination might be used in personality research where researchers have reasons to believe that the personality traits (like extraversion and openness) they are investigating are correlated. They could start with a large number of personality items and use PCA to reduce them to a smaller number of components and then apply Direct Oblimin to understand how these components relate to each other.
2. Maximum likelihood with varimax rotation technique combination might be used in cognitive psychology where a researcher is testing a specific theory about cognitive abilities. The researcher might hypothesize distinct, uncorrelated cognitive factors (like memory, attention, and reasoning) and use ML to estimate these factors and Varimax to simplify the interpretation.
3. Principal axis factoring with Promax rotation technique combination could be used in clinical psychology research, such as exploring the underlying factor structure of a new mental health screening tool. Here, PAF would help identify the underlying factors (like symptoms of depression, anxiety, etc.), and Promax would allow these factors to correlate, reflecting the reality that mental health symptoms often co-occur.
How to solve this issue in two ways: There are fewer than two cases, at least one of the variables has zero variance, there is only one variable in the analysis, or correlation coefficients could not be computed for all pairs of variables. No further statistics will be computed.
1) Make sure that the sample size is at least 100; otherwise, try to increase the sample size.
2) Try experimenting with different estimation methods and rotation techniques.
Related topics include:
Factor analysis of Likert scale with Cronbach alpha reliability
Factor analysis of Likert scale with composite scores
Factor analysis of Likert scale with hypothesis testing
Factor analysis of Likert scale with regression
Factor analysis of Likert scale with correlation
Covariance relations and measurement errors on IBM SPSS AMOS or SmartPLS 4
#SPSS #FactorAnalysis #LikertScale #EFA #CFA #ACP #PCA #PAF #Validity #Reliability #MultivariateDataAnalysis #Likertscale #SEM #Dimensionalityreduction #Itemresponsetheory #measurement #Qualtrics #GoogleForms #Excel #Questionnaire #Survey #Psychology #Socialsciences #Statistics #ChatGPT #IBMSPSS #spsstraining #smartpls
Email: [email protected]
WhatsApp: +212619398603 / wa.link/l6jvny
Common types of Factor Analysis (FA) in academic research and journal articles:
1. Principal components analysis with direct oblimin
2. Maximum likelihood with varimax rotation technique
3. Principal axis factoring with Promax rotation technique
What type of factor analysis estimation method and rotation techniques to use, when and why? and what is the difference between Principal Components Analysis (PCA) and Factor Analysis (FA) with Principal Axis Factoring and Maximum Likelihood? What is the difference between varimax rotation and promax rotation?
1. Principal components analysis with direct oblimen combination might be used in personality research where researchers have reasons to believe that the personality traits (like extraversion and openness) they are investigating are correlated. They could start with a large number of personality items and use PCA to reduce them to a smaller number of components and then apply Direct Oblimin to understand how these components relate to each other.
2. Maximum likelihood with varimax rotation technique combination might be used in cognitive psychology where a researcher is testing a specific theory about cognitive abilities. The researcher might hypothesize distinct, uncorrelated cognitive factors (like memory, attention, and reasoning) and use ML to estimate these factors and Varimax to simplify the interpretation.
3. Principal axis factoring with Promax rotation technique combination could be used in clinical psychology research, such as exploring the underlying factor structure of a new mental health screening tool. Here, PAF would help identify the underlying factors (like symptoms of depression, anxiety, etc.), and Promax would allow these factors to correlate, reflecting the reality that mental health symptoms often co-occur.
How to solve this issue in two ways: There are fewer than two cases, at least one of the variables has zero variance, there is only one variable in the analysis, or correlation coefficients could not be computed for all pairs of variables. No further statistics will be computed.
1) Make sure that the sample size is at least 100; otherwise, try to increase the sample size.
2) Try experimenting with different estimation methods and rotation techniques.
Related topics include:
Factor analysis of Likert scale with Cronbach alpha reliability
Factor analysis of Likert scale with composite scores
Factor analysis of Likert scale with hypothesis testing
Factor analysis of Likert scale with regression
Factor analysis of Likert scale with correlation
Covariance relations and measurement errors on IBM SPSS AMOS or SmartPLS 4
#SPSS #FactorAnalysis #LikertScale #EFA #CFA #ACP #PCA #PAF #Validity #Reliability #MultivariateDataAnalysis #Likertscale #SEM #Dimensionalityreduction #Itemresponsetheory #measurement #Qualtrics #GoogleForms #Excel #Questionnaire #Survey #Psychology #Socialsciences #Statistics #ChatGPT #IBMSPSS #spsstraining #smartpls
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