Principal Component Analysis PCA Exploratory Factor Analysis EFA

Exploratory Factor analysis (EFA), 5-Point Likert Scale, Questionnaire, Survey, Data, Principal Factor Analysis (PFA) , Principal Axis Factoring (PAF), PCA is the first phase of EFA, Multivariate statistical method, scale, Statistical, method, variables, latent constructs, measured variables, unobserved, factors, relationships, assumptions, common variance, correlation, confirmatory factor analysis, Sample Size, Kaiser-Meyer-Olkin (KMO), correlation matrix, factorization, Linearity, Observed variables, multicollinearity, Normality, normally distributed, data transformations, Interval or ratio level data, Ordinal variables, categories, Categorical variables, categorical factor analysis, Homoscedasticity, equal Variance, outliers, Common factors, Unique factors, Independence of Factors, uncorrelated, Factor rotation methods, Oblique, Direct oblimin, Promax, Factors not correlated, Orthogonal, Varimax, Equamax, Quatrimax, Correlation matrix, bivariate correlation matrix, Determinant , Sample Size, Communalities, proportion, Eigenvalues, Scree plot, Factor loading, Correlation coefficient, relationship, association, construct, Analyze, data, Survey, points, Likert, scale, Continuous scale, Nominal, Instrument, Demographic data, Sex, Occupation, Education, experiences, Family members, Items, Questions, Response, Ordinal, dichotomous, Outcome, Single item, Multiple items, Dependent variable, Independent variable (IV), Exploratory, Predictor, DV, Survey analysis methods, Data Preparation, Data cleaning, Remove, Duplicate, Incomplete, Errors responses, numerical values, categorical data, Multinominal, Code, Reverse coding, constructs, composite, Averages, Sums, Descriptive Analysis, Frequency distribution, Calculate , Summary statistics, Calculate measures, Mean, Median, Mode, SD, Range, IQ1, IQ3, Data Visualization, Graphs, charts, Create, representations, Bar charts, Pie charts, Histograms, Stacked charts, Scatter plots, Line charts, Factor Analysis, identify, latent, constructs, explain, correlations, Principal component analysis, reduction, latent factors, EFA, CFA, Cluster Analysis, Grouping data, Cross-tabulation, relationships, categorical variables, Chi-squared test, Correlation analysis, Pearson’s correlation coefficients, Spearman’s correlation coefficients, Regression analysis, Linear regression, Binomial regression, Multinomial regression, Ordinal regression, Comparative Analysis, Ethnicity, Parametric tests, T-tests, ANOVA tests, Mann Whitney U test, Kruskal Walis test, Jonckheere's trend test, Statistical Significance, Confidence intervals, 95% CI, Hypothesis testing, P-value, Reliability tests, Cronbach’s alpha, Validity, Data reduction statistical technique, sections, highly related, Multivariate statistical techniques, Unsupervised statistical method, Dimensionality (variables) reduction data method, principal components, variance, multi-collinearity, load, Construct PCA, scatterplot, Sampling adequacy, Bartlett's test of sphericity, structures, patterns, maximum variance, linear combinations, mathematical transformation, Types of factor extraction, extract, hypothesized measurement model, model's goodness of fit, CMIN/DF, Root mean square error of approximation (RMSEA), Comparative fit index (CFI), GFI, AGFI, Standardised root mean square residual (SRMR), Normed fit index (NFI), non-normed fit index (NNFI), PCFI, confirm, disconfirm, factor structure, theoretical model, factor loadings, latent factors, observed variables, Fit Indices, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), modification indices, Residual, parameter estimates, covariance matrix, Structural equation modelling, SEM, LISREL, EQS, AMOS, Mplus, LAVAAN package in R, Python package semopy 2, maximum likelihood statistic

[@KhaledAbdallah-vw2ik]

Great work doctor Thank you so much ❤❤

[@kayem9653]

Thank you for this information. How do we label the factors?