IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced

Learn that the default model in Amos is any model tested in structural equation modeling, initial measurement model, with other models like covariance and structural weight models listed for analysis.

Explore unstandardized and standardized estimates in SEM with AMOS, learn how B values express unit-driven changes and beta denotes standardized effects in regression paths.

Explore the bottom tabs to view a path diagram and tables, showing variables, their estimates, and standard errors via the view text option.

Explore how Amos labels variables in structural equation modeling, distinguishing endogenous, exogenous, observed, latent, and residual variables, and understand tools for drawing and saving observed variables.

A structural equation model abstracts reality, and researchers build a model to approximate that reality using theoretical propositions and hypothesized relationships among variables, including independent and dependent variables.

Recognize exogenous variables as the senders of arrows influencing endogenous variables like wellness, with locus of control internal and external as exogenous predictors, and wellness mediating relationship to psychological well-being.

Understand observed (measured) variables, such as age and gender from questionnaires, and unobserved (latent) variables like personality dimensions, which are inferred through observed items.

Investigate a structural model of managerial innovation that links search and adapt heuristics and fast and frugal heuristics to heuristic intelligence, which in turn explains business excellence.

Use the move object tool to reposition e1, e2, e3 and the latent variable so error terms and observed variables are visible, then apply the touchup tool to remove overflow.

In AMOS, every error term or residual has a built-in constraint: the path value is automatically set to one. This ensures the model remains defined and calculations are possible.

Draw paths with single headed arrows to model relationships in AMOS, treating socioeconomic status as a mediating variable affecting performance, and add error terms for model identification.

Moz offers three select tools: select one object, select all objects, and unselect all. Use the move object tool to move the selected objects or the entire model.

Understand how Amos estimates model parameters to minimize the discrepancy between sample and predicted variance-covariance matrices, covering factor loadings, factor variances and covariances, and indicator variances and covariances.

Explore when to apply the maximum likelihood method in sem; the lecture shows that when data are normal, maximum likelihood is the better technique.

Learn when to apply the asymptotic distribution free method, highlighting its use only with very large samples, typically in the thousands.

Before applying maximum likelihood, verify three assumptions: observed variables are normally distributed and continuous with a large sample; test univariate and multivariate normality.

Apply the Israel six correction factor to test your multigroup invariance, ensuring the hypothesized model holds across different groups.

Explore how constraints shape models in Amos, including pre-assigned values, path values, second-order constructs like personality from introversion and extroversion, indicators, and chi-square corrections for many constraints.

Explore how to determine an appropriate sample size for structural equation modeling, with a practical heuristic of 200–300 and guidance to perform sample power analysis for journal or thesis contexts.

Learn when regression falls short for complex models and how SEM handles unseen constructs, using personality and hard work to model success and the role of adding variables like IQ.

Explain what exploratory factor analysis is and how it reduces many data items into a few meaningful factors, using personality traits as an example.

Explore exploratory factor analysis across fields to identify independent factors that explain latent variables, with examples from engineering, psychology, anthropology, and image analysis.

Trace the historical origin of factor analysis from Spearman to Cattell and apply exploratory factor analysis to compress large item pools into meaningful scales and test factors.

Explore descriptive statistics in SPSS AMOS using univariate descriptives to view the mean and standard deviation for each observed variable, and note the initial solution for commonalities.

Explore building and interpreting a correlation matrix for exploratory factor analysis, including coefficients, significance, and determinant checks, with KMO and Bartlett's tests of sphericity to assess adequacy.

Explore inverse and reproduced correlations, and anti-image analysis to compare correlation and covariance matrices, assess sampling adequacy with KMO and Bartlett's test, and decide on indicator inclusion after factor analysis.

Explore the extraction method in sem amos, focusing on principal component analysis to identify the main component driving data variance, and learn when this method suits research goals.

Learn how maximum likelihood estimation overcomes the sample-population gap in SEM extraction, offering probabilistic estimation and greater generalizability compared to PCA and other methods.

Compare scree plot and Kaiser's eigenvalue criterion to determine the number of meaningful factors in factor analysis, using unrotated and rotated solutions.

Identify latent personality factors by performing a from-scratch factor analysis in SPSS with principal component analysis, scree plot, and varimax rotation.

Conduct reliability analysis with Cronbach's alpha, remove unreliable items by item-total correlations and cross-loadings, and refine factors with principal component analysis until the determinant is non-negative and variance reaches ~61–63%.

Learn to present factor analysis results in APA style by listing factor indicators and loadings, naming six factors (supportiveness, neuroticism, introversion, conscientiousness, artistic, creative), and reporting variance explained with eigenvalues.

Explore how to evaluate factor reliability using Cronbach's alpha, including reverse coding and item deletion, to ensure internal consistency in SPSS AMOS.

Import the EFA model's rotated component matrix into Amos with Pattern Matrix Builder. Specify the data file and perform missing value analysis with EM to validate a six-factor personality scale.

Assess discriminant validity as part of construct validity by confirming six latent dimensions are distinct and not highly correlated, while evaluating convergent validity to establish overall construct validity.

Explore the concept of validity, showing how a research model or test measures what it claims to measure, using six factors and model-fit and reliability indices to verify adequacy.

Understand how to compute average variance extracted in SEM using standardized factor loadings, and interpret standardized estimates from latent constructs to indicators.

Explore maximum shared squared variance (MSV) and its role in discriminant validity by examining covariances between latent variables and the significance of the absolute value.

Ensure AVE exceeds ESV to establish discriminant validity, since a latent construct should extract more variance than the error term or risk overlap with other constructs.

Report a judicious combination of incremental and absolute fit indices to assess model fit. Emphasize CFI as the incremental index, and include GFI, SRMR, RMK, and chi-square by df ratio.

Describe the chi-square null hypothesis as the comparison of population and model implied covariance matrices, and show good fit when sample, model implied, and population covariances align via maximum likelihood.

Calculate srmr with the amos plugin by opening the dialog and clicking calculate estimates. Determine that the default model's srmr is 0.0774, near 0.08 cutoff, so we reject the model.

Explore rmk, a measure of lack of model fit that adjusts for model complexity and provides a 90% confidence interval; near-zero lower limit, upper limit under 0.08, indicate good model.

Compute RMSEA to assess model fit; when RMSEA is 0.075 with a 95% CI above 0.08, the sample covariance and model implied covariance diverge, leading to rejecting the model.