Assessing Factorial Invariance in Ordered-Categorical Measures

By R. Millsap, Jenn Yun-Tein. 2004

862 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ecb990931736e4b1a2f6c4e5bada671142d7f2a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Food Science

In Statistics and Probability

Citations in the corpus

Detecting Violations of Factorial Invariance Using Data-Based Specification Searches: A Monte Carlo Study (2007, methodology): “Appropriate factor models for discrete ordinal items differ from those used in the continuous case (Millsap & Tein, 2004).”
Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys (2014, methodology): “To set the scale and origin of the latent variable for multiple-groups data generation we used the methods described in Millsap and Yun-Tein (2004).”
The Comparability of Measurements of Attitudes toward Immigration in the European Social Survey Exact versus Approximate Measurement Equivalence (2015, methodology): “A detailed account of equivalence testing with MGCFA for ordinal data can be found in Millsap and Yun-Tein (2004).”
Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages (2020, methodology): “As can be seen in the baseline model specification, items are treated as ordered, delta parameterization and Wu and Estabrook’s 2016 model identification are employed (other options are available, including Millsap & Yun-Tein, 2004 identification).”
A Test for Cluster Bias: Detecting Violations of Measurement Invariance Across Clusters in Multilevel Data (2013, methodology): “This approach is applicable to both the linear factor model (Reise, Widaman, & Pugh, 1993) and the ordinal factor model (Millsap & Yun-Tein, 2004).”
Conducting Measurement Invariance Tests with Ordinal Data: A Guide for Social Work Researchers (2015, background): “…significant change in fit when all the parameters tested in a particular step (e.g., all factor loadings) are constrained to be equal across groups proceed systematically to test individual parameters or subsets of parameters to identify the source of the noninvariance (Millsap & Yun-Tein, 2004).”
Evaluating Measurement Invariance of TALIS 2013 Complex Scales: Comparison between Continuous and Categorical Multiple-Group Confirmatory Factor Analyses (2014, methodology): “For ordered-categorical variables (e.g., four-point Likert-scale responses), Muthén and Asparouhov (2002) and Millsap and Yun-Tein (2004) proposed a categorical MGCFA approach with the solution for full measurement invariance.”
Measurement Invariance in International Surveys: Categorical Indicators and Fit Measure Performance (2017, methodology): “For model identification and to set the scale of the latent variable, we followed Millsap and Yun-Tein (2004).”
Testing Measurement Invariance Using MIMIC (2012, background): “…factor irrespective of the levels of a variable other than the common factor (Barendse, Oort, & Garst, 2010; Mellenbergh, 1989; Meredith, 1993; Millsap & Yun-Tein, 2004): P Y y G P Y yij i ij i( | , ) ( | ).= = =η η (1) Given the factor score (hi), the conditional probability of the response…”
Spirituality in the Workplace: Theory and Measurement (2011, background): “…the potential problems resulting from treating ordered-categorical data as continuous data (e.g., Flora & Curran, 2004; Lubke & Muthen, 2004; Millsap & Jenn, 2004; Muthen, 1984; Muthen & Kaplan, 1985) Future research can treat survey data collected using Likert-type scales as…”
The Reciprocal Internal/External Frame of Reference Model (2011, methodology): “This estimator has been shown to perform as well as robust weighted least squares methods (Lei, 2009) that are not easily incorporated into studies of the invariance of constructs in large, complex models (e.g., Millsap & Yun-Tein, 2004).”
Testing Measurement Invariance for a Second-Order Factor. A Cross-National Test of the Alienation Scale (2018, methodology): “The model was identified using the constraints suggested by Millsap and Yun-Tein (2004), the second-order scalar invariance model was identified by constraining the latent intercepts to 0 in all groups and the second-order factor’s mean to 0 in one group.”