Inconsistent Effect of Psychometric-Scale Familiarization on the Relationship Between Ratings of Perceived Exertion and External Load Measures in Elite Youth Soccer Players

OBJECTIVES

To examine the moderating effect of familiarization on the relationship between external load and ratings of perceived exertion (RPEs) in elite youth soccer players.

METHODS

Thirty-five elite male youth soccer players were monitored over a 31-week period. Players had no previous experience using the centiMax scale (arbitrary units [AU]). The final sample included familiarized (blackness test; n = 20) and nonfamiliarized players (n = 15) with the Borg centiMax scale. Players recorded a global RPE and differential RPEs (dRPE) for breathlessness (RPE-B) and leg-muscle exertion (RPE-L) 15 to 30 minutes following training sessions and competitive matches. Separate multivariable-adjusted random-effects generalized additive models with restricted maximum likelihood quantified familiarization versus no-familiarization differences in actual perceived exertion score (in AU) by number of accelerations, decelerations, and high-speed running distance (in meters) as predictor variables, respectively.

RESULTS

Players improved their blackness test score from 39% to 78%. For explorations by number of accelerations, familiarization effects were not practically relevant for the RPE and RPE-B variables. The width and sign of the effects for the RPE-L variable at 30 efforts of 10 AU (95% CI, 4-16 AU) suggested that scores were lower for players who underwent familiarization versus players who did not. Familiarization effects were not practically relevant for any RPE variable irrespective of the number of deceleration efforts and high-speed running distance covered.

CONCLUSION

Improved performance on the blackness test did not have a moderating effect on the relationship between proxy measures of external load and RPEs.

A statistical test to reject the structural interpretation of a latent factor model

Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally efficacious. The assumption that the indicators do not have effects on anything subsequent, and that they are themselves only affected by antecedents through the underlying latent is a strong assumption, effectively imposing a structural interpretation on the latent factor model. In this paper, we show that this structural assumption has empirically testable implications, even though the latent variable itself is unobserved. We develop a statistical test to potentially reject the structural interpretation of a latent factor model. We apply this test to data concerning associations between the Satisfaction-with-Life-Scale and subsequent all-cause mortality, which provides strong evidence against a structural interpretation for a univariate latent underlying the scale. Discussion is given to the implications of this result for the development, evaluation, and use of measures, and for the use of factor analysis itself.

Constructed Measures and Causal Inference: Towards a New Model of Measurement for Psychosocial Constructs

Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurement models offer different conceptualizations of the relation between the indicators and what is sometimes conceived of as a univariate latent variable supposed to correspond in some way to the construct. I argue that the empirical implications of reflective and formative models will often be violated by data since the causally relevant constituents will generally be multivariate, not univariate. These empirical implications can be formally tested but factor analysis is not adequate to do so. I argue that formative models misconstrue the relation between the constructed measures and the underlying reality by which causal processes operate, but that reflective models misconstrue the nature of the underlying reality itself by typically presuming that the constituents of it that are causally efficacious are unidimensional. I discuss the ensuing problems arising from these misconstruals. I propose a causal interpretation of associations between constructed measures and various outcomes that is applicable to both reflective and formative models and is applicable even if the usual assumptions of these models are violated. I put forward an outline for a new model of the process of measure construction and discuss the practical implications of these observations and proposals for the provision of definitions, the selection of items, item-by-item analyses, the construction of measures, and the interpretation of the associations of these measures with subsequent outcomes.

All Happy Emotions Are Alike but Every Unhappy Emotion Is Unhappy in Its Own Way: A Network Perspective to Academic Emotions

Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.

Judging the Neonatal Abstinence Syndrome Assessment Tools to Guide Future Tool Development: The use of Clinimetrics as Opposed to Psychometrics

In the face of the current Neonatal Abstinence Syndrome (NAS) epidemic, there is considerable variability in the assessment and management of infants with NAS. In this manuscript, we particularly focus on NAS assessment, with special attention given to the popular Finnegan Neonatal Abstinence Score (FNAS). A major instigator of the problem of variable practices is that multiple modified versions of the FNAS exist and continue to be proposed, including shortened versions. Furthermore, the validity of such assessment tools has been questioned, and as a result, the need for better tools has been suggested. The ultimate purpose of this manuscript, therefore, is to increase researchers' and clinicians' understanding on how to judge the usefulness of NAS assessment tools in order to guide future tool development and to reduce variable practices. In short, we suggest that judgment of NAS assessment tools should be made on a clinimetrics viewpoint as opposed to psychometrically. We provide examples, address multiple issues that must be considered, and discuss future tool development. Furthermore, we urge researchers and clinicians to come together, utilizing their knowledge and experience, to assess the utility and practicality of existing assessment tools and to determine if one or more new or modified tools are needed with the goal of increased agreement on the assessment of NAS in practice.

Unpacking Constructs: A Network Approach for Studying War Exposure, Daily Stressors and Post-Traumatic Stress Disorder

Conflict-affected populations are exposed to stressful events during and after war, and it is well established that both take a substantial toll on individuals’ mental health. Exactly how exposure to events during and after war affect mental health is a topic of considerable debate. Various hypotheses have been put forward on the relation between stressful war exposure (SWE), daily stressors (DS) and the development of post-traumatic stress disorder (PTSD). This paper seeks to contribute to this debate by critically reflecting upon conventional modeling approaches and by advancing an alternative model to studying interrelationships between SWE, DS, and PTSD variables. The network model is proposed as an innovative and comprehensive modeling approach in the field of mental health in the context of war. It involves a conceptualization and representation of variables and relationships that better approach reality, hence improving methodological rigor. It also promises utility in programming and delivering mental health support for war-affected populations.