HiTOP Is Not an Improvement Over the DSM

In their response to our article (both in this issue), DeYoung and colleagues did not sufficiently address three fundamental flaws with the Hierarchical Taxonomy of Psychopathology (HiTOP). First, HiTOP was created using a simple-structure factor-analytic approach, which does not adequately represent the dimensional space of the symptoms of psychopathology. Consequently, HiTOP is not the empirical structure of psychopathology. Second, factor analysis and dimensional ratings do not fix the problems inherent to descriptive (folk) classification; self-reported symptoms are still the basis on which clinical judgments about people are made. Finally, HiTOP is not ready to use in real-world clinical settings. There is currently no empirical evidence demonstrating that clinicians who use HiTOP have better clinical outcomes than those who use the Diagnostic and Statistical Manual of Mental Disorders ( DSM). In sum, HiTOP is a factor-analytic variation of the DSM that does not get the field closer to a more valid and useful taxonomy.

Evaluation of Latent Models Assessing Physical Fitness and the Healthy Eating Index in Community Studies: Time-, Sex-, and Diabetes-Status Invariance

Accurate measurement requires assessment of measurement equivalence/invariance (ME/I) to demonstrate that the tests/measurements perform equally well and measure the same underlying constructs across groups and over time. Using structural equation modeling, the measurement properties (stability and responsiveness) of intervention measures used in a study of metabolic syndrome (MetS) treatment in primary care offices, were assessed. The primary study (N = 293; mean age = 59 years) had achieved 19% reversal of MetS overall; yet neither diet quality nor aerobic capacity were correlated with declines in cardiovascular disease risk. Factor analytic methods were used to develop measurement models and factorial invariance were tested across three time points (baseline, 3-month, 12-month), sex (male/female), and diabetes status for the Canadian Healthy Eating Index (2005 HEI-C) and several fitness measures combined (percentile VO2 max from submaximal exercise, treadmill speed, curl-ups, push-ups). The model fit for the original HEI-C was poor and could account for the lack of associations in the primary study. A reduced HEI-C and a 4-item fitness model demonstrated excellent model fit and measurement equivalence across time, sex, and diabetes status. Increased use of factor analytic methods increases measurement precision, controls error, and improves ability to link interventions to expected clinical outcomes.

Genomic Selection Using Environmental Covariates Within an Integrated Factor Analytic Linear Mixed Model

This paper introduces a single-stage genomic selection approach which directly integrates environmental covariates within a special factor analytic framework. The factor analytic approach of Smith et al. (2001) is an effective method of analysis for multi-environment trial (MET) datasets, but has limited biological interpretation since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes directly interpretable, and thence predictable. This paper develops a model for both predictable and observable GEI in terms of a joint set of known and latent factors, as well as non-genetic sources of variation within trials and environments. This single-stage approach is referred to as the integrated factor analytic linear mixed model (IFA-LMM). The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer Crop Science. The results show that the environmental covariates explain 34.6% of the genetic variance across environments (compared to only 23.3% for a conventional regression model). This represents 92.7% of the crossover GEI. The latent factors then explain 40.7% of the genetic variance, which represents 87.6% of the non-crossover GEI. This demonstrates the ability of the IFA-LMM to model crossover and non-crossover GEI in a manner that is both informative and practical to plant breeding.

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

How Values Ground Value Creation: The practical inference framework

After more than two decades of searching, the holy grail of integrating norms into management and organization research remains elusive. Researchers still lack a clear framework that explains value creation in relation to normative values, and, in turn, a means to incorporate values into research methods and generate value-based practical insights. To fill that need, this article presents an epistemological framework for understanding value creation. The practical inference framework centers on the activity of practical reasoning, a kind of reasoning that is legitimized by intrinsic values. It turns the ordinary epistemic equation on its head by seeking reasons rather than causes, and justifications rather than descriptions. In doing so, it shows how both factor analytic and newer, divergent methods of research can integrate with a robust architecture of value creation in ways that offer relevant knowledge for managers and society.