The effect of additional variables on factor indeterminacy in models with a single common factor

Psychometrika ◽  
1978 ◽  
Vol 43 (2) ◽  
pp. 177-192 ◽  
Author(s):  
Stanley A. Mulaik ◽  
Roderick P. McDonald
Keyword(s):  
Common Factor ◽  
Factor Indeterminacy

Root Cause Analyses of Suicides of Mental Health Clients

Crisis ◽  
2015 ◽  
Vol 36 (5) ◽  
pp. 316-324 ◽  
Author(s):  
Donna Gillies ◽  
David Chicop ◽  
Paul O'Halloran
Keyword(s):  
Mental Health ◽  
Suicide Prevention ◽  
Common Factor ◽  
Data Collection Tool ◽  
Related Factors ◽  
Root Cause ◽  
Communication Problems ◽  
The Individual ◽  
Mental Health Clients ◽  
Imminent Risk

Abstract. Background: The ability to predict imminent risk of suicide is limited, particularly among mental health clients. Root cause analysis (RCA) can be used by health services to identify service-wide approaches to suicide prevention. Aims: To (a) develop a standardized taxonomy for RCAs; (b) to quantitate service-related factors associated with suicides; and (c) to identify service-related suicide prevention strategies. Method: The RCAs of all people who died by suicide within 1 week of contact with the mental health service over 5 years were thematically analyzed using a data collection tool. Results: Data were derived from RCAs of all 64 people who died by suicide between 2008 and 2012. Major themes were categorized as individual, situational, and care-related factors. The most common factor was that clients had recently denied suicidality. Reliance on carers, recent changes in medication, communication problems, and problems in follow-through were also commonly identified. Conclusion: Given the difficulty in predicting suicide in people whose expressions of suicidal ideation change so rapidly, services may consider the use of strategies aimed at improving the individual, stressor, support, and care factors identified in this study.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Development of an Observer-Rated Measure of Common-Factor Therapist Competence

2010 ◽  
Author(s):  
Ruth C. Brown ◽  
Michael A. Southam-Gerow ◽  
Bryce D. McLeod ◽  
Alexis M. Quinoy-Boe ◽  
Emily J. Wheat
Keyword(s):  
Common Factor ◽  
Therapist Competence

Investigation into Cigarette Smoking and Atmospheric Pollution in the Aetiology of Lung Cancer

1963 ◽  
Vol 2 (01) ◽  
pp. 13-19 ◽  
Author(s):  
R. Doll
Keyword(s):  
Lung Cancer ◽  
Cigarette Smoking ◽  
Atmospheric Pollution ◽  
Direct Evidence ◽  
Retrospective Studies ◽  
Common Factor ◽  
Animal Experiments ◽  
Smoking Habits ◽  
Careful Examination ◽  
Ancillary Data

The evidence that cigarette smoking and atmospheric pcllution are causes of lung cancer is largely statistical. The first evidence was indirect; that is, i1. was noticed that in many countries the incidence of lung cancer had increased and that the increase could be correlated with changes in the prevalence of cigarette smoking and of certain types of atmospheric pollution.Since then much direct evidence has been obtained. The relationship between cigarette smoking and lung cancer has been demonstrated retrospectively by comparing the smoking habits of patients with and without lung cancer and prospectively by observing the mortality from lung cancer in groups of persons of known smoking habits. Conclusions can be drawn from these studies only after careful examination of the results. In particular it is important in retrospective studies to test a) the reproducibility of the data, b) the representativeness of the data, and c) the comparability of the special series and their controls. The resul1.s of retrospective studies are all similar and all show a close relationship between cigarette smoking and the disease.The results have been confirmed by pro~pective studies which are lesF. open to bias. The results can be explained if cigarette smoking causes lung cancer or if both are related to some third common factor. Ancillary data (pathological changes in the bronchial mucosa, animal experiments, etc.) support the causal hypothesis.The evidence relating to atmospheric pollution is less definite and it is difficult to get direct evidence of a relationship in the individual. It is clear that pollution has little effect in the absence of smoking, but the mortality associated with a given amount of smoking is generally greater in large towns than in the countryside and among men who have emigrated from Britain than among men who have lived all their lives in less polluted countries.

scholarly journals Genetic and environmental architecture of conscientiousness in adolescence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yusuke Takahashi ◽  
Anqing Zheng ◽  
Shinji Yamagata ◽  
Juko Ando
Keyword(s):  
Individual Differences ◽  
Effortful Control ◽  
Genetic Variance ◽  
Genetic Factors ◽  
Common Factor ◽  
Genetic Correlations ◽  
Self Control ◽  
Genetic Analyses ◽  
Environmental Correlations ◽  
Per Se

AbstractUsing a genetically informative design (about 2000 twin pairs), we investigated the phenotypic and genetic and environmental architecture of a broad construct of conscientiousness (including conscientiousness per se, effortful control, self-control, and grit). These four different measures were substantially correlated; the coefficients ranged from 0.74 (0.72–0.76) to 0.79 (0.76–0.80). Univariate genetic analyses revealed that individual differences in conscientiousness measures were moderately attributable to additive genetic factors, to an extent ranging from 62 (58–65) to 64% (61–67%); we obtained no evidence that shared environmental influences were observed. Multivariate genetic analyses showed that for the four measures used to assess conscientiousness, genetic correlations were stronger than the corresponding non-shared environmental correlations, and that a latent common factor accounted for over 84% of the genetic variance. Our findings suggest that individual differences in the four measures of conscientiousness are not distinguishable at both the phenotypic and behavioural genetic levels, and that the overlap was substantially attributable to genetic factors.

scholarly journals Wasting and Associated Factors among Children under 5 Years in Five South Asian Countries (2014–2018): Analysis of Demographic Health Surveys

2021 ◽  
Vol 18 (9) ◽  
pp. 4578
Author(s):  
Nidhi Wali ◽  
Kingsley E. E. Agho ◽  
Andre M. N. Renzaho
Keyword(s):  
South Asia ◽  
South Asian ◽  
Health Surveys ◽  
Associated Factors ◽  
Common Factor ◽  
Household Wealth ◽  
Health Concern ◽  
Demographic And Health Surveys ◽  
Asian Countries ◽  
Sampling Weights

Child wasting continues to be a major public health concern in South Asia, having a prevalence above the emergency threshold. This paper aimed to identify factors associated with wasting among children aged 0–23 months, 24–59 months, and 0–59 months in South Asia. A weighted sample of 564,518 children aged 0–59 months from the most recent demographic and health surveys (2014–2018) of five countries in South Asia was combined. Multiple logistic regression analyses that adjusted for clustering and sampling weights were used to examine associated factors. Wasting prevalence was higher for children aged 0–23 months (25%) as compared to 24–59 months (18%), with variations in prevalence across the South Asian countries. The most common factor associated with child wasting was maternal BMI [adjusted odds ratio (AOR) for 0–23 months = 2.02; 95% CI: (1.52, 2.68); AOR for 24–59 months = 2.54; 95% CI: (1.83, 3.54); AOR for 0–59 months = 2.18; 95% CI: (1.72, 2.77)]. Other factors included maternal height and age, household wealth index, birth interval and order, children born at home, and access to antenatal visits. Study findings suggest need for nutrition specific and sensitive interventions focused on women, as well as adolescents and children under 2 years of age.

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