计量经济分析 = Econometric analysis

Summary

The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models.


The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest.


One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation).


Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems.


This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

A general and simple method for obtaining R2 from generalized linear mixed-effects models

The establishment of measurement invariance across groups is a logical prerequisite to conducting substantive cross-group comparisons (e.g., tests of group mean differences, invariance of structura...

A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research

The field of organizationa l justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizationa l justice.

/pdf/justice-at-the-millennium-a-meta-analytic-review-of-25-years-b9qfmr75n2.pdf

Justice at the millennium: a meta-analytic review of 25 years of organizational justice research

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

Longitudinal Research: The Theory, Design, and Analysis of Change:

This article offers a new approach to the conceptualization of the human capital resource by developing a multilevel model connecting micro, intermediate, and macro levels of scholarship. We define human capital as a unit-level resource that is created from the emergence of individuals' knowledge, skills, abilities, or other characteristics. The model provides new insights into how strategically valuable human capital resources have their origins in the psychological attributes of individuals and are transformed through unit-level processes.

Emergence of the Human Capital Resource: A Multilevel Model

This chapter provides a brief and relatively nontechnical introduction to hierarchical linear models. The purpose of such models is first explained in graphical terms. This is followed by a description of the model and then an empirical example. Numerous primary sources are provided for readers to learn the more technical details.


Keywords:

hierarchical linear modeling;
random coefficient modeling

Hierarchical Linear Models

In this article, the authors illustrate how random coefficient modeling can be used to develop growth models for the analysis of longitudinal data. In contrast to previous discussions of random coefficient models, this article provides step-by-step guidance using a model comparison framework. By approaching the modeling this way, the authors are able to build off a regression foundation and progressively estimate and evaluate more complex models. In the model comparison framework, the article illustrates the value of using likelihood tests to contrast alternative models (rather than the typical reliance on tests of significance involving individual parameters), and it provides code in the open-source language R to allow readers to replicate the results. The article concludes with practical guidelines for estimating growth models.

Growth Modeling Using Random Coefficient Models: Model Building, Testing, and Illustrations:

Microfoundations have received increased attention in strategy and organization theory over the past decade. In this paper, we take stock of the microfoundations movement, its origins and history, and disparate forms. We briefly touch on similar micro movements in disciplines such as economics and sociology. However, our particular focus is on the unique features of the microfoundations movement in macro management. While the microfoundations movement in macro management does seek to link with more micro disciplines such as psychology and organizational behavior, it also features a unique set of questions, assumptions, theoretical mechanisms, and independent/dependent variables that complement the focus in the micro disciplines. We also discuss the disparate criticisms of the microfoundations literature and the challenges the movement faces, such as defining distinct theoretical and empirical programs for microfoundational research. The overall purpose of this manuscript is to clearly delineate the promise and uniqueness of microfoundations research in macro management, to discuss how the movement originated and where it is going, and to offer rich opportunities for future work.

Robert E. Ployhart

Papers

Longitudinal Research: The Theory, Design, and Analysis of Change:

Emergence of the Human Capital Resource: A Multilevel Model

Hierarchical Linear Models

Growth Modeling Using Random Coefficient Models: Model Building, Testing, and Illustrations:

The Microfoundations Movement in Strategy and Organization Theory