Latent variable models: an introduction to factor, path, and structural analysis

By J. Loehlin. 1986

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Citations in the corpus

Graphite: Iterative Generative Modeling of Graphs (2018, background): “Latent variable generative modeling is an effective approach for unsupervised representation learning of high-dimensional data [26].”
The Influence of Individual, Job, and Organizational Characteristics on Correctional Staff Job Stress, Job Satisfaction, and Organizational Commitment (2008, background): “…β in Table 3), it is possible to compare the magnitude of the impact of each of the independent variables on the dependent variable job stress because standardized regression coefficients are metric free (i.e., represent standardized units) and thus can be compared to one another (Loehlin, 1992).”
Conceptual and methodological issues in studying school leadership effects as a reciprocal process (2011, unknown): “Rather than conceptualizing leaders as either the ‘‘origin or the pawn’’ (see Bridges, 1970) in leadership for learning, mutual-influence models propose that relationships among variables in a proposed model could have reciprocal or bi-directional effects (Loehlin, 1992).”
Developing a new measure of entrepreneurial mindset: Reliability, validity, and implications for practitioners. (2016, background): “The chi-square goodness-of-fit statistic assesses the magnitude of variance unexplained by the model but is sensitive to small differences when sample sizes are large (Comrey & Lee, 1992; Loehlin, 1992; Marsh, Balla, & McDonald, 1988).”
At what sample size do latent variable correlations stabilize? (2018, background): “Additionally, correlations estimated within a latent variable framework are also known to represent measurement-error-free correlations (Jöreskog & Sörbom, 1989; Loehlin, 1992).”
Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. (2017, methodology): “The AIC is a measure of the quality of each model, relative to each of the other models for a given set of data (Akaike, 1974; Loehlin, 1992) and the AIC value of the model can be expressed as: AIC L c= −2 ln + 2 (32) Where L is the maximum value of the likelihood function for the model and c is…”
Ethnic Identity Formation During Adolescence (2006, methodology): “Prior to testing our structural model, measurement models were examined for each ethnic group using a confirmatory factor analysis to determine whether the indicator variables were adequately measuring the latent variables (Hatcher, 1994; Loehlin, 1998; see Table 1 for bivariate correlations).”