Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Registered Report: Testing Ideological Asymmetries in Measurement Invariance.

ABSTRACT: People with different ideological identities differ in their values, personality, affect, and psychological motivations. These differences are observed on measures of practical and clinical importance and these differences are the central node tying together theories about the psychology of political ideology; however, they rest on a critical untested assumption: The measures are invariant across ideological groups. Here, we test this assumption across 28 constructs in data from the United States and the Netherlands. Measures are not invariant across ideological divisions. At the same time, estimates of ideological similarities and differences are largely similar before and after correcting for measurement noninvariance. This may give us increased confidence in the results from this research area, while simultaneously highlighting that some instance of noninvariance did change conclusions and that individual items are not always comparable across political groups.

SUBMITTER: Brandt MJ

PROVIDER: S-EPMC7961747 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Registered Report: Testing Ideological Asymmetries in Measurement Invariance.

Brandt Mark J MJ He Jia J Bender Michael M

Assessment 20210129 3

People with different ideological identities differ in their values, personality, affect, and psychological motivations. These differences are observed on measures of practical and clinical importance and these differences are the central node tying together theories about the psychology of political ideology; however, they rest on a critical untested assumption: The measures are invariant across ideological groups. Here, we test this assumption across 28 constructs in data from the United State ...[more]

PMID: 33514266

Similar Datasets

Registered report protocol: Stress testing predictive models of ideological prejudice.

Project description:In this registered report, we propose to stress-test existing models for predicting the ideology-prejudice association, which varies in size and direction across target groups. Previous models of this relationship use the perceived ideology, status, and choice in group membership of target groups to predict the ideology-prejudice association across target groups. These analyses show that models using only the perceived ideology of the target group are more accurate and parsimonious in predicting the ideology-prejudice association than models using perceived status, choice, and all of the characteristics in a single model. Here, we stress-test the original models by testing the models' predictive utility with new measures of explicit prejudice, a comparative operationalization of prejudice, the Implicit Association Test, and additional target groups. In Study 1, we propose to directly test the previous models using an absolute measure of prejudice that closely resembles the measure used in the original study. This will tell us if the models replicate with distinct, yet conceptually similar measures of prejudice. In Study 2, we propose to develop new ideology-prejudice models for a comparative operationalization of prejudice using both explicit measures and the Implicit Association Test. We will then test these new models using data from the Ideology 2.0 project collected by Project Implicit. We do not have full access to this data yet, but upon acceptance of our Stage 1 registered report, we will gain access to the complete dataset. Currently, we have access to an exploratory subset of the data that we use to demonstrate the feasibility of the study, but its limited number of target groups prevents conclusions from being made.

| S-EPMC11361595 | biostudies-literature

Truth and Bias, Left and Right: Testing Ideological Asymmetries with a Realistic News Supply.

Project description:The debate around "fake news" has raised the question of whether liberals and conservatives differ, first, in their ability to discern true from false information, and second, in their tendency to give more credit to information that is ideologically congruent. Typical designs to measure these asymmetries select, often arbitrarily, a small set of news items as experimental stimuli without clear reference to a "population of information." This pre-registered study takes an alternative approach by, first, conceptualizing estimands in relation to all political news. Second, to represent this target population, it uses a set of 80 randomly sampled items from a large collection of articles from Google News and three fact-checking sites. In a subsequent survey, a quota sample of US participants (n = 1,393) indicate whether they believe the news items to be true. Conservatives are less truth-discerning than liberals, but also less affected by the congruence of news.

| S-EPMC10371040 | biostudies-literature

Ideological asymmetries in online hostility, intimidation, obscenity, and prejudice.

Project description:To investigate ideological symmetries and asymmetries in the expression of online prejudice, we used machine-learning methods to estimate the prevalence of extreme hostility in a large dataset of Twitter messages harvested in 2016. We analyzed language contained in 730,000 tweets on the following dimensions of bias: (1) threat and intimidation, (2) obscenity and vulgarity, (3) name-calling and humiliation, (4) hatred and/or racial, ethnic, or religious slurs, (5) stereotypical generalizations, and (6) negative prejudice. Results revealed that conservative social media users were significantly more likely than liberals to use language that involved threat, intimidation, name-calling, humiliation, stereotyping, and negative prejudice. Conservatives were also slightly more likely than liberals to use hateful language, but liberals were slightly more likely than conservatives to use obscenities. These findings are broadly consistent with the view that liberal values of equality and democratic tolerance contribute to ideological asymmetries in the expression of online prejudice, and they are inconsistent with the view that liberals and conservatives are equally prejudiced.

| S-EPMC10724124 | biostudies-literature

Testing measurement invariance in longitudinal data with ordered-categorical measures.

Project description:A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal measurement invariance: The repeatedly measured variables need to represent the same construct in the same metric over time. Measurement invariance can be studied through factor models examining the relations between the observed indicators and the latent constructs. In longitudinal research, ordered-categorical indicators such as self- or observer-report Likert scales are commonly used, and these measures often do not approximate continuous normal distributions. The present didactic article extends previous work on measurement invariance to the longitudinal case for ordered-categorical indicators. We address a number of problems that commonly arise in testing measurement invariance with longitudinal data, including model identification and interpretation, sparse data, missing data, and estimation issues. We also develop a procedure and associated R program for gauging the practical significance of the violations of invariance. We illustrate these issues with an empirical example using a subscale from the Mexican American Cultural Values scale. Finally, we provide comparisons of the current capabilities of 3 major latent variable programs (lavaan, Mplus, OpenMx) and computer scripts for addressing longitudinal measurement invariance. (PsycINFO Database Record

| S-EPMC5121102 | biostudies-literature

Correction for measurement error in invariance testing: An illustration using SQP

Project description:With the increasing availability of cross-national data, more attention has been given to the issue of comparability. But while a lot of emphasis has been directed to the assessment of measurement invariance, there has been substantially less concern on how measurement error can affect the results of measurement invariance testing. In this study, we show how correction for measurement error can be applied to measurement invariance analysis. We illustrate this using the concept of “Perceived ethnic threat” measured in the European Social Survey Round 3 (2006). The measurement invariance results before and after correction for measurement error are compared. We show that correction for measurement error offers a viable way to ensure that non-invariant parameters are actually caused by differences in the data and not caused by the measurement method.

| S-EPMC7529276 | biostudies-literature

A more general model for testing measurement invariance and differential item functioning.

Project description:The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable modeling approaches: the multiple groups model or the multiple-indicator multiple-cause (MIMIC) model. Both approaches offer opportunities to detect differential item functioning within multi-item scales, and thereby to test measurement invariance, but both approaches also have significant limitations. The multiple groups model allows 1 to examine the invariance of all model parameters but only across levels of a single categorical individual difference variable (e.g., ethnicity). In contrast, the MIMIC model permits both categorical and continuous individual difference variables (e.g., sex and age) but permits only a subset of the model parameters to vary as a function of these characteristics. The current article argues that moderated nonlinear factor analysis (MNLFA) constitutes an alternative, more flexible model for evaluating measurement invariance and differential item functioning. We show that the MNLFA subsumes and combines the strengths of the multiple group and MIMIC models, allowing for a full and simultaneous assessment of measurement invariance and differential item functioning across multiple categorical and/or continuous individual difference variables. The relationships between the MNLFA model and the multiple groups and MIMIC models are shown mathematically and via an empirical demonstration. (PsycINFO Database Record

| S-EPMC5140785 | biostudies-literature

Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework.

Project description:Comparative public health research makes wide use of self-report instruments. For example, research identifying and explaining health disparities across demographic strata may seek to understand the health effects of patient attitudes or private behaviors. Such personal attributes are difficult or impossible to observe directly and are often best measured by self-reports. Defensible use of self-reports in quantitative comparative research requires not only that the measured constructs have the same meaning across groups, but also that group comparisons of sample estimates (eg, means and variances) reflect true group differences and are not contaminated by group-specific attributes that are unrelated to the construct of interest. Evidence for these desirable properties of measurement instruments can be established within the confirmatory factor analysis (CFA) framework; a nested hierarchy of hypotheses is tested that addresses the cross-group invariance of the instrument's psychometric properties. By name, these hypotheses include configural, metric (or pattern), strong (or scalar), and strict factorial invariance. The CFA model and each of these hypotheses are described in nontechnical language. A worked example and technical appendices are included.

| S-EPMC1808350 | biostudies-other

Is Differential Noneffortful Responding Associated With Type I Error in Measurement Invariance Testing?

Project description:Low test-taking effort as a validity threat is common when examinees perceive an assessment context to have minimal personal value. Prior research has shown that in such contexts, subgroups may differ in their effort, which raises two concerns when making subgroup mean comparisons. First, it is unclear how differential effort could influence evaluations of scale property equivalence. Second, if attaining full scalar invariance, the degree to which differential effort can bias subgroup mean comparisons is unknown. To address these issues, a simulation study was conducted to examine the influence of differential noneffortful responding (NER) on evaluations of measurement invariance and latent mean comparisons. Results showed that as differential rates of NER grew, increased Type I errors of measurement invariance were observed only at the metric invariance level, while no negative effects were apparent for configural or scalar invariance. When full scalar invariance was correctly attained, differential NER led to bias of mean score comparisons as large as 0.18 standard deviations with a differential NER rate of 7%. These findings suggest that test users should evaluate and document potential differential NER prior to both conducting measurement quality analyses and reporting disaggregated subgroup mean performance.

| S-EPMC8377344 | biostudies-literature

Testing Measurement Invariance of the Dark Triad Dirty Dozen in a Belgian Adult Sample.

Project description:The Dirty Dozen (Jonason & Webster, 2010) is a frequently used concise version of the Dark Triad to measure three socially aversive personality traits: Machiavellianism, psychopathy and, narcissism. The present study has examined measurement invariance in a sample of Belgian adults. The present study aims to assess measurement invariance of the Dutch version of the Dirty Dozen measure across gender in a large city-based representative adult sample in Belgium (N = 1587). Multi-group first-order confirmatory factor analysis for categorical indicators was utilized. In addition, unique associations between Dirty Dozen traits, trait self-control and, acceptance of illegitimate norms were examined in a series of structural equation models. Results indicated that the internal consistency of the Dirty Dozen subscales was good for Machiavellianism (α = 0.80) and narcissism (α = 0.80), but modest for psychopathy (α = 0.64). The hypothesized three correlated factors model with separate factors for Machiavellianism, psychopathy and, narcissism provided a poor fit for men and women. Invariance testing across gender showed evidence for weak invariance only, indicating that the underlying latent factors are measured the same way with the same metric in the two populations. However, we were not able to establish strong measurement invariance. Observed group differences should be interpreted with caution. Furthermore, Machiavellianism and psychopathy were strongly associated with trait self-control in both men and women. Strong correlations were found between acceptance of illegitimate norms and Dirty Dozen traits, Machiavellianism and, psychopathy, but not with narcissism.

| S-EPMC8698221 | biostudies-literature

Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling.

Project description:Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

| S-EPMC7797957 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data