Problems with formative and higher-order reflective variables

Cadogan and Lee (this issue) discuss the problems inherent in modeling formative latent variables as endogenous. In response to the commentaries by Rigdon (this issue) and Finn and Wang (this issue), the present article extends the discussion on formative measures. First, the article shows that regardless of whether statistical identification is achieved, researchers are unable to illuminate the nature of a formative latent variable. Second, the study clarifies issues regarding formative indicator weighting, highlighting that the weightings of formative components should be specified as part of the construct definition. Finally, the study shows that higher-order reflective constructs are invalid, highlights the damage their use can inflict on theory development and knowledge accumulation, and provides recommendations on a number of alternative models which should be used in their place (including the formative model). © 2012 Elsevier Inc.

Improper use of formative endogenous variables

Researchers often develop and test conceptual models containing formative variables. In many cases, these formative variables are specified as being endogenous. This article provides a clarification of formative variable theory, distinguishing between the formative latent variable and the formative composite variable. When an endogenous latent variable relies on formative indicators for measurement, empirical studies can say nothing about the relationship between exogenous variables and the endogenous formative latent variable: conclusions can only be drawn regarding the exogenous variables' relationships with a composite variable. The authors also show the dangers associated with developing theory about antecedents to endogenous formative variables at the (aggregate) formative latent variable level. Modeling relationships with endogenous formative variables at the (disaggregate) indicator level informs richer theory development, and encourages more precise empirical testing. When antecedents' relationships with endogenous formative variables are modeled at the formative latent variable level rather than the formative indicator level, theory construction can verge on the superficial, and empirical findings can be ambiguous in substantive meaning.

Deconstructing phonological tasks:the contribution of stimulus and response type to the prediction of early decoding skills

Abstract Phonological tasks are highly predictive of reading development but their complexity obscures the underlying mechanisms driving this association. There are three key components hypothesised to drive the relationship between phonological tasks and reading; (a) the linguistic nature of the stimuli, (b) the phonological complexity of the stimuli, and (c) the production of a verbal response. We isolated the contribution of the stimulus and response components separately through the creation of latent variables to represent specially designed tasks that were matched for procedure. These tasks were administered to 570 6 to 7-year-old children along with standardised tests of regular word and non-word reading. A structural equation model, where tasks were grouped according to stimulus, revealed that the linguistic nature and the phonological complexity of the stimulus predicted unique variance in decoding, over and above matched comparison tasks without these components. An alternative model, grouped according to response mode, showed that the production of a verbal response was a unique predictor of decoding beyond matched tasks without a verbal response. In summary, we found that multiple factors contributed to reading development, supporting multivariate models over those that prioritize single factors. More broadly, we demonstrate the value of combining matched task designs with latent variable modelling to deconstruct the components of complex tasks.

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.

Uncovering the root cause of ethnic difference in ability testing:differential test functioning, test familiarity and trait optimism as explanations of ethnic group differences

The present research represents a coherent approach to understanding the root causes of ethnic group differences in ability test performance. Two studies were conducted, each of which was designed to address a key knowledge gap in the ethnic bias literature. In Study 1, both the LR Method of Differential Item Functioning (DIF) detection and Mixture Latent Variable Modelling were used to investigate the degree to which Differential Test Functioning (DTF) could explain ethnic group test performance differences in a large, previously unpublished dataset. Though mean test score differences were observed between a number of ethnic groups, neither technique was able to identify ethnic DTF. This calls into question the practical application of DTF to understanding these group differences. Study 2 investigated whether a number of non-cognitive factors might explain ethnic group test performance differences on a variety of ability tests. Two factors – test familiarity and trait optimism – were able to explain a large proportion of ethnic group test score differences. Furthermore, test familiarity was found to mediate the relationship between socio-economic factors – particularly participant educational level and familial social status – and test performance, suggesting that test familiarity develops over time through the mechanism of exposure to ability testing in other contexts. These findings represent a substantial contribution to the field’s understanding of two key issues surrounding ethnic test performance differences. The author calls for a new line of research into these performance facilitating and debilitating factors, before recommendations are offered for practitioners to ensure fairer deployment of ability testing in high-stakes selection processes.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

The epidemiology of prostate cancer among multiethnic men

The research goal was to document differences in the epidemiology of prostate cancer among multicultural men [non-Hispanic White (NHW), Hispanic (H), non-Hispanic Black (NHB)], and Black subgroups, particularly among NHB subgroups [US-born (USB) and Caribbean-born (CBB)]. Study findings will be useful in supporting further research into Black subgroups. Aim 1 explored changes over time in reported prostate cancer prevalence, by race/ethnicity and by birthplace (within the Black subgroups). Aim 2 investigated relationships between observed and latent variables. The analytical approaches included confirmatory factor analysis (CFA for measurement models) and structural equation modeling (SEM for regression models). National Center for Health Statistics, National Health Interview Survey (NHIS) data from 1999–2008 were used. The study sample included men aged 18 and older, grouped by race/ethnicity. Among the CBB group, survey respondents were limited to the English-speaking Caribbean. Prostate cancer prevalence, by race showed a higher trend among NHB men than NHW men overall, however differences over time were not significant. CBB men reported a higher proportion of prostate cancer among cancers diagnosed than USB men overall. Due to small sample sizes, stable prostate cancer prevalence trends could not be assessed over time nor could trends in the receipt of a PSA exam among NHB men when stratified by birthplace. USB and CBB men differ significantly in their screening behavior. The effect of SES on PSA screening adjusted for risk factors was statistically significant while latent variable lifestyle was not. Among risk factors, family history of cancer exhibited a consistent positive effect on PSA screening for both USB and CBB men. Among the CBB men, the number of years lived in the US did not significantly affect PSA screening behavior. When NHB men are stratified by birthplace, CBB men had a higher overall prevalence of prostate cancer diagnoses than USB men although not statistically significant. USB men were 2 to 3 times more likely to have had a PSA exam compared to CBB men, but among CBB men birthplace did not make a significant difference in screening behavior. Latent variable SES, but not lifestyle, significantly affected the likelihood of a PSA exam.

Evaluating heavy episodic drinking effects on educational attainment

Adolescence and emerging adulthood are transition points that offer both opportunities and constraints on individual development. The purpose of this study is threefold: First, to examine two models (i.e., young adolescents in grades 7 and 8 and older adolescents in grade 12) of heavy episodic drinking and examine how heavy episodic drinking affects subsequent educational attainment. By utilizing two different developmental transitions, i.e., middle school to high school and high school to college, it may be possible to better understand the temporal effects of alcohol use and subsequent educational attainment. The second purpose of this study is to examine how alcohol use at Time 1 may lead to the problems in the adolescent's immediate context due to alcohol (i.e., problems with parents, peers, romantic relationships, problems at school) and to examine if these problems affect educational attainment over and above alcohol use alone. The third purpose of this study is to examine the potential gender differences in these models. The study uses data from the National Longitudinal Study of Adolescent Health, which is a large scale, nationally representative school based sample of 20,745 adolescents who were interviewed in grades 7 to 12. Two longitudinal mediational models were evaluated utilizing structural equation modeling. Binge drinking and number of days drunk were used as indicators for a latent variable of heavy episodic drinking (i.e., LHED). In the 7th and 8th grade model, direct effects of LHED were found to predict educational attainment at grade 12. Additionally, in the 7th and 8th grade sample, a mediated relationship was found whereby educational attainment was predicted by problems with parents. Problems with parents were predicted by number of days drunk in the past year. In the 12th grade sample, there were no direct effects or indirect effects of alcohol on educational attainment. This study highlights the need for using a longitudinal framework when examining heavy episodic drinking's effects on educational attainment. ^

Personality and the healthy lifestyle as predictors of physical health: Can the healthy lifestyle be explained by personality?

In health and epidemiological research, the Healthy Lifestyle (HLS) is often invoked as an explanation for inconsistent effects. Modifiable components of the HLS are advocated as a panacea for the most common threats to public health. Biases resulting from the HLS are theorized to result from covariance among its components. This covariance has not yet been formally modeled. Furthermore, no mechanism has been proposed to explain this covariance among these factors. Using three large nationally representative samples, I evaluated the HLS as a latent variable. Using structural equation modeling (SEM) I evaluated the degree to which the shared variance of HLS components is accounted for by personality traits, and tested the HLS as a mediator of the personality health relationship. Across all three samples, the HLS fits well as a latent variable, is partially accounted for by personality traits, and mediates the effects of personality traits on health. In all cases personality traits have direct effects on health independent of the HLS. These results suggest that the utility of personality traits as predictors of health exceeds that provided by commonly used lifestyle predictors.

Discrimination in clinical settings and its relationship to depression and anger in women living with HIV

Objectives: To describe the frequency of feared discrimination in various social situations and of perceived discrimination in clinical settings, as well as to study the relationship between discrimination and depression and anger in women living with human immunodeiciency virus (HIV). Material and methods: The scale of Feared and Perceived Discrimination for Women with HIV (DTP-40-MV), the Beck Depression Inventory (BDI-2), and the Anger Expression scale of State-Trait-anger expression inventory (STaXi-2-aX/eX) were applied to a random sample of 200 women living with HIV. Results: These women feared being discriminated against, perceived discrimination upon the review of medical records, but perceived little discrimination in clinical care. a model with good adjustment to the data showed that the fear of being discriminated against creates a disposition toward perception of discrimination in the clinical settings (latent variable with 2 indicators: review of the medical records and clinical care) and increases cognitive/affective depressive symptoms; higher anger control decreases the anger manifestation; greater discrimination perceived in the clinical settings decreases anger control, which facilitates the expression of anger and slows cognitive/affective depressive symptoms; and these latter symptoms sensitize the perception of discrimination before the clinical records. Conclusion: Feared discrimination is a clinically relevant aspect due to its frequency and effect on depressive symptoms and perception of discrimination before the review of medical records.

Relação entre impacto de um treinamento gerencial no trabalho, desejabilidade social, suporte a transferência e características dos respondentes

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Programa de Pós-Graduação em Administração, 2016.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

The immediate effects of atlanto-occipital joint manipulation and suboccipital muscle inhibition technique on active mouth opening and pressure pain sensitivity over latent myofascial trigger points in the masticatory muscles

DESIGN: A randomized controlled trial.OB JECTIVE: To investigate the immediate effects on pressure pain thresholds over latent trigger points (TrPs) in the masseter and temporalis muscles and active mouth opening following atlanto-occipital joint thrust manipulation or a soft tissue manual intervention targeted to the suboccipital muscles. BACKGROUND : Previous studies have described hypoalgesic effects of neck manipulative interventions over TrPs in the cervical musculature. There is a lack of studies analyzing these mechanisms over TrPs of muscles innervated by the trigeminal nerve. METHODS: One hundred twenty-two volunteers, 31 men and 91 women, between the ages of 18 and 30 years, with latent TrPs in the masseter muscle, were randomly divided into 3 groups: a manipulative group who received an atlanto-occipital joint thrust, a soft tissue group who received an inhibition technique over the suboccipital muscles, and a control group who did not receive an intervention. Pressure pain thresholds over latent TrPs in the masseter and temporalis muscles, and active mouth opening were assessed pretreatment and 2 minutes posttreatment by a blinded assessor. Mixed-model analyses of variance (ANOVA) were used to examine the effects of interventions on each outcome, with group as the between-subjects variable and time as the within-subjects variable. The primary analysis was the group-by-time interaction. RESULTS: The 2-by-3 mixed-model ANOVA revealed a significant group-by-time interaction for changes in pressure pain thresholds over masseter (P<.01) and temporalis (P =.003) muscle latent TrPs and also for active mouth opening (P<.001) in favor of the manipulative and soft tissue groups. Between-group effect sizes were small. CONCLUSIONS: The application of an atlanto-occipital thrust manipulation or soft tissue technique targeted to the suboccipital muscles led to an immediate increase in pressure pain thresholds over latent TrPs in the masseter and temporalis muscles and an increase in maximum active mouth opening. Nevertheless, the effects of both interventions were small and future studies are required to elucidate the clinical relevance of these changes. LEVEL OF EVIDENCE : Therapy, level 1b. J Orthop Sports Phys Ther 2010;40(5):310-317. doi:10.2519/jospt.2010.3257. KEYWORDSDS: cervical manipulation, muscle trigger points, neck, TMJ, upper cervical.