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

This chapter introduces a variety of contextual and economic factors that challenge traditional approaches to Human Resources (HR) and training in particular. It reviews what is known about autonomous learning, and how autonomous learning contributes to the formation of human capital resources. The chapter considers the nature and relationships between autonomous learning, value creation, and value capture. The business context within which training and development are conducted has changed radically since the mid 2000s. Autonomous learning seems like a simple concept to define, but a complete understanding requires some elaboration. Induced learning occurs when content is created for the learner and the learner typically only controls the pace of learning and where the learning occurs. The HR function has been moving in this direction for some time, and research will be needed to articulate how this should be done for the training function in general and autonomous learning specifically.

Autonomous Learning, Human Capital Resources, and Value Capture

This paper argues that the impact of a turnover event on unit-level performance is manifest in two distinct stages. Drawing on collective turnover and team adaptation theory, we propose that Stage ...

A Two-Staged Longitudinal Model of Collective Turnover on Unit-Level Performance

Strategic Training and Development and Their Role in Shaping Competitive Advantage

Having the right people in the right place at the right time is vital to the long-term performance
of any ﬁrm. This is true to an even greater degree for ﬁrms that compete internationally. Global
ﬁrms face all of the same challenges as domestic organizations, plus those unique to adapting to
different cultures, countries, and people (Torbiorn, 1997). For example, issues related to human
resources (HR) are complicated enough within a single country, but increase geometrically when
the workforce exists across countries. It is the human element that is perhaps the most variable
and uncertain of the numerous resources a ﬁrm needs to implement a global competitive strategy.
After all, talent cannot be owned by the ﬁrm, so it creates a set of unique challenges for building,
maintaining, and leveraging this resource (Coff, 1997). Indeed, many ﬁrms cannot grow
internationally in the manner they wish due to talent challenges (Miller & Guo, 2014; Scullion
& Collings, 2011). At the same time, ﬁrms that are able to do so effectively are likely to see
above average returns. Thus, talent, or as it is often called, human capital resources, is an important
determinant of competitive advantage for most ﬁrms, but even more so in global ﬁrms.

Recruitment and selection in global organizations: a resource-based framework

Organizational scholars have increasingly investigated the consequences of organizational turnover on unit (e.g. firm, store, or group) performance, while extant literature has recently called for the investigation of some emergent properties of the turnover construct in order to understand it as a collective phenomenon. Among these properties are three under-developed moderators elucidated in this work: (1) the quality of the human capital that departs, (2) the time dispersion of turnover events within the study period, and (3) the quality of the remaining human capital. Using data from a large, multi-store U.S. retail chain, we longitudinally examine, confirm, and interpret the effects these moderators have on store performance when new hires leave the store. We contribute to both the turnover and human capital literatures by uncovering when and why turnover has disparate effects on unit performance.

Robert E. Ployhart

Papers

Autonomous Learning, Human Capital Resources, and Value Capture

A Two-Staged Longitudinal Model of Collective Turnover on Unit-Level Performance

Strategic Training and Development and Their Role in Shaping Competitive Advantage

Recruitment and selection in global organizations: a resource-based framework

"Collective Turnover: Who Leaves, When Did They Leave, and Who Did They Leave Behind?"