Validation and measurement invariance of the multidimensional qualitative job insecurity scale

This contribution introduces the Multidimensional Qualitative Job Insecurity Scale (MQJIS). Drawing from the qualitative job insecurity literature and addressing some of other scales’ limitations, a multidimensional model is proposed and investigated by means of confirmatory factor analysis and multilevel confirmatory factor analysis. Study 1 aims to explore the psychometric properties and factorial structure of MQJIS in an Italian sample of blue-collar workers (N?=?583), showing that a model with one higher-order factor (i.e., qualitative job insecurity) and four dimensions (i.e., social relationships, employment conditions, working conditions, and work content) shows a good fit to the data and good reliability indices. Study 2 aims to investigate MQJIS measurement invariance across several groups, based on country of origin, age, and gender. Results on a sample of Belgian and Italian workers (N?=?710) show that MQJIS met the criteria for uniqueness invariance across genders and scalar invariance across countries and age groups. Significance, implications, and future directions stemming from the initial validation and the confirmed measurement invariance of this scale are discussed.

     

Development and validation of the Remote Working Benefits & Disadvantages scale

The changes that are constantly occurring in the labour sector have led organisations and companies to move towards digital transformation. This process was accelerated by the COVID-19 pandemic and  conducted to a massive recourse to the practice of remote working, which in this study is understood as the term for the way of performing work outside the usual workplace and with the support of ICT. Currently, there are no flexible scales in the literature that allow measuring the benefits and disadvantages of remote working with a single instrument. Thus, the distinction between the positive and negative consequences of working remotely, substantiated by a solid literature, provides a framework for a systematical understanding of the issue. The aim of the present study is to develop and validate a scale on remote working benefits and disadvantages (RW-B&D scale). For this end, a preliminary Exploratory Factor Analysis (EFA) with 304 participants, a tailored EFA with a sample of 301 workers and a Confirmatory Factor Analysis (CFA) with 677 workers were conducted. Participants were all Italian employees who worked remotely during the period of the COVID-19 health emergency. Data were collected between October 2020 and April 2021. The psychometric robustness of the model was assessed through bootstrap validation (5000 resamples), fit indices testing and measurement of factorial invariance. The statistical analyses demonstrated the bifactorial nature of the scale, supporting the research hypothesis. The model showed good fit indices, bootstrap validation reported statistically significant saturations, good reliability indices, and convergent and discriminant validity. Measurement invariance was tested for gender and organisational sector. The results suggested that the novel scale facilitates the quantitative measurement of the benefits and disadvantages associated with remote working in empirical terms. For this reason, it could be a streamlined and psychometrically valid instrument to identify the potential difficulties arising from remote working and, at the same time, the positive aspects that can be implemented to improve organisational well-being.

     

Testing structural equation models: the impact of error variances in the data generating process

Yet another paper on fit measures? To our knowledge, very few papers discuss how fit measures are affected by error variance in the Data Generating Process (DGP). The present paper deals with this. Based upon an extensive simulation study, this paper shows that the effects of increased error variance differ significantly for various fit measures. In addition to error variance the effects depend on sample size and severity of misspecification. The findings confirm the general notion that good fit as measured by the chi-square, RMSEA and GFI etc. does not necessarily mean that the model is correctly specified and reliable. One finding is that the chi square test may give support to misspecified models in situations with a high level of error variance in the DGP, for small sample sizes. Another finding is that the chi-square test looses power also for large sample sizes when the model is negligible misspecified. Other results include incremental fit indices as NFI and RFI which prove to be more informative indicators under these circumstances. At the end of the paper we formulate some guidelines for use of different fit measures.     

Multivariate residual‐based finite‐sample tests for serial dependence and ARCH effects with applications to asset pricing models

In this paper, we propose several finite‐sample specification tests for multivariate linear regressions (MLR). We focus on tests for serial dependence and ARCH effects with possibly non‐Gaussian errors. The tests are based on properly standardized multivariate residuals to ensure invariance to error covariances. The procedures proposed provide: (i) exact variants of standard multivariate portmanteau tests for serial correlation as well as ARCH effects, and (ii) exact versions of the diagnostics presented by Shanken ( 1990 ) which are based on combining univariate specification tests. Specifically, we combine tests across equations using a Monte Carlo (MC) test method so that Bonferroni‐type bounds can be avoided. The procedures considered are evaluated in a simulation experiment: the latter shows that standard asymptotic procedures suffer from serious size problems, while the MC tests suggested display excellent size and power properties, even when the sample size is small relative to the number of equations, with normal or Student‐t errors. The tests proposed are applied to the Fama–French three‐factor model. Our findings suggest that the i.i.d. error assumption provides an acceptable working framework once we allow for non‐Gaussian errors within 5‐year sub‐periods, whereas temporal instabilities clearly plague the full‐sample dataset. Copyright © 2009 John Wiley & Sons, Ltd.     

A conditional minimax approach in survey sampling

Summary Applying the usual minimax criterion in finite sampling theory yields complicated solutions except the parameter space has certain invariance properties. A conditional minimax criterion is suggested. After a sample is selected it is reasonable to seek an estimator that has good properties (e.g. minimaxity) for that sample. Explicit solutions are given in the case where the parameter space is described by quadratic forms.     

基于时间序列模型在物流需求预测中的应用

本文采用时间序列模型进行物流需求预测，用实例从数据平稳性的判断，平稳化、标准化，建模，经模型的识别、定价、参数估计和检验，到预测及误差和置信区间的计算，详细地说明了时间序列模型在物流需求预测中是如何应用的。结果表明该ARMA模型能够较好地拟合并可获得较高的中短期预测精度。     

Testing measurement invariance using multigroup CFA: differences between educational groups in human values measurement

This article applies the testing procedures for measurement invariance using multigroup confirmatory factor analysis (MGCFA). It illustrates these procedures by investigating the factorial structure and invariance of the Portraits Value Questionnaire (PVQ, Schwartz et al.: J. Cross Cult. Psychol. 32(5), 519–542 (2001)) across three education groups in a population sample (N  =  1,677). The PVQ measures 10 basic values that Schwartz postulates to comprehensively describe the human values recognized in all societies (achievement, hedonism, self-direction, benevolence, conformity, security, stimulation, power, tradition and universalism). We also estimate and compare the latent means of the three education groups. The analyses show partial invariance for most of the 10 values and parameters. As expected, the latent means show that less educated respondents attribute more importance to security, tradition, and conformity values.     

Leadership competency inventory: A systematic process of developing and validating a leadership competency scale

This article reports psychometric evaluation of the Penn State Leadership Competency Inventory (LCI). The 32‐item LCI was validated on a sample of 323 managers in the health care industry. Preliminary validity and reliability evidence of the LCI was established through exploratory factor analysis (EFA), item‐total correlations, Cronbach's alpha coefficients, and confirmatory factor analysis (CFA). The four‐factor leadership competency scale, comprising supervisory and managerial competencies, organizational leadership, personal mastery, and resource leadership, accounted for 58% of variance. According to CFA results, the model fit of the four latent factors of the LCI was confirmed to be appropriate. Cross‐validation with other populations is needed to confirm the factor structure. Limitations and further research recommendations are discussed.     

Integrated analysis of content and construct validity of psychometric instruments

Establishing adequacy of psychometric properties of an instrument involves acquisition and evaluation of evidence based on item content and internal structure. Content validity evidence consists of subject matter experts providing quantitative ratings of the extent to which items are a representative sample of targeted domain. Evidence of internal structure includes factor analytic studies and examination of item interrelationships based on item responses from participants. Although subject matter expert ratings and participant response data are traditionally analyzed separately, each serves to inform the other in important ways. We propose integrating subject matter experts’ and participants’ data seamlessly to establish a unified model of validity evidence. The approach is applied to an instrument designed to measure nursing home culture change (i.e., resident-centered care). The proposed method has been demonstrated to be useful with a posterior distribution resulting in stable estimates of psychometric parameters superior to traditional analytic approaches. To illustrate the efficacy of the methodology, we present a simulation study and discuss its place in psychometric methods.     

A Western Australian Test of Invariant Factorial Structure in the Work Autonomy Scale Between Managerial and Non-Managerial Employee Classifications

ABSTRACT

This paper explores the conceptualisation of work autonomy through an investigation of the factorial structure stability of the Global Work Autonomy Scale at different employee levels. After administering the Global Work Autonomy Scale (Breaugh, 1985) to employees in a public sector organisation, analysis of responses confirmed an a priori three-factor model of work autonomy (method autonomy, scheduling autonomy and criteria autonomy) in a group of non-managerial employees (n = 193). A subsequent multi-sample analysis was conducted to test the invariance of the three-factor model for a group of managerial employees (n = 205). After constraining factor loadings and factor covariances, the hypothesised model continued to provide a reasonable fit to the data, confirming the generalisability of the three-factor model to both employee classifications. The methodological and theoretical implications for research into work autonomy are discussed.     

Large panels with common factors and spatial correlation

This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the mean group and pooled estimators under heterogeneous and homogeneous slope coefficients, and for these estimators proposes non-parametric variance matrix estimators. The paper then considers the more general case of a panel data model with a multifactor error structure and spatial error correlations. Under this framework, the Common Correlated Effects (CCE) estimator, recently advanced by Pesaran (2006), continues to yield estimates of the slope coefficients that are consistent and asymptotically normal. Small sample properties of the estimators under various patterns of cross-section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross-sectionally correlated errors.     

协整向量、调节参数同为非线性的阈值协整检验

针对经济变量的长期均衡和短期调节关系可能同时存在非线性的事实,本文扩展现有阈值协整模型,提出了协整向量、调节参数都为非线性的阈值协整模型,并着重探讨了该模型的检验方法。研究表明,在协整关系的检验中,Wald统计量有较好的有限样本性质。在协整关系的非线性检验中,LMW和LMG统计量的水平扭曲和检验势都较好。在调节参数的非线性检验中,当调节参数具有显著的非线性时,LMH统计量表现出较好的有限样本性质。     

Growth and convergence in a multi-country empirical stochastic Solow model

The paper considers international per capita output and its growth using a panel of data for 102 countries between 1960 and 1989. It sets out an explicitly stochastic Solow growth model and shows that this has quite different properties from the standard approach where the output equation is obtained by adding an error term to the linearized solution of a deterministic Solow model. It examines the econometric properties of estimates of beta convergence as traditionally defined in the literature and shows that all these estimates are subject to substantial biases. Our empirical estimates clearly reflect the nature and the magnitude of these biases as predicted by econometric theory. Steady state growth rates differ significantly across countries and once this heterogeneity is allowed for the estimates of beta are substantially higher than the consensus in the literature. But they are very imprecisely estimated and difficult to interpret. The paper also discusses the economic implications of these results for sigma convergence. © 1997 John Wiley & Sons, Ltd.     

Combining quality and quantity. A psychometric evaluation of the self-anchoring scale

In this study a psychometric comparison is made between a category rating scale with fixed anchors and a self-anchoring category rating scale where respondents themselves define the end anchors by referring to their personal experiences with the construct being measured. A student-satisfaction questionnaire was administered to 301 undergraduate students on two test moments separated by a 1-month period. Both types of category rating scales were crossed with both test moments according to a 2 ×  2 mixed factorial design. Our results show that respondents perceive the construct being measured in the same way with both rating scales. A comparison concerning different indicators of reliability, i.e. test–retest reliability, internal consistency and the error variances associated with each item, also failed to find a difference between both rating scales. In a last analysis it is demonstrated that using a self-anchoring rating scale or a rating scale with fixed anchors has a small but significant effect on the ratings of the respondents. In conclusion, a self anchoring scale may be the scale of choice if additional qualitative information from the anchors is warranted, but when quantitative comparisons between groups are required, rating scales with fixed anchors are preferable thanks to their ease of use.     

A Stochastic Frontier Production Function with Flexible Risk Properties

This paper considers a stochastic frontier production function which has additive, heteroscedastic error structure. The model allows for negative or positive marginal production risks of inputs, as originally proposed by Just and Pope (1978). The technical efficiencies of individual firms in the sample are a function of the levels of the input variables in the stochastic frontier, in addition to the technical inefficiency effects. These are two features of the model which are not exhibited by the commonly used stochastic frontiers with multiplicative error structures.An empirical application is presented using cross-sectional data on Ethiopian peasant farmers. The null hypothesis of no technical inefficiencies of production among these farmers is accepted. Further, the flexible risk models do not fit the data on peasant farmers as well as the traditional stochastic frontier model with multiplicative error structure.     

金融时间序列中加性异常值的鉴别与校正

借鉴Franses and Ghijsel[1](1999)和Charles and Darne[2](2005)提出的鉴别和校正金融序列加性异常值的方法,以GARCH模型为例,对我国的上证综合指数和深圳成分指数进行了加性异常值的鉴定与校正,并对校正后的残差进行了正态检验。结果表明该方法效果显著,进行异常值校正后的GARCH(1,1),更好地拟合金融时间序列中的尖峰厚尾和波动丛聚性的特性,纠正了正态分布的GARCH(1,1)对时间序列拟合的偏误。     

Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals

In this article, we try to realize the best compromise between in‐sample goodness of fit and out‐of‐sample predictability of sovereign defaults. To do this, we use a new regression‐tree based approach that signals impending sovereign debt crises whenever pre‐selected indicators exceed specific thresholds. Using data from emerging markets and Greece, Ireland, Portugal and Spain (GIPS) over the period 1975–2010, we show that our model significantly outperforms existing competing approaches (logit, stepwise logit, noise‐to‐signal ratio and regression trees), while balancing in‐ and out‐of‐sample performance. Our results indicate that illiquidity (high short‐term debt to reserves) and default history, together with real GDP growth and US interest rates, are the main determinants of both emerging market country defaults and the recent European sovereign debt crisis.     

自相关检验方法的检验功效研究

研究目标:探究DW检验和LM检验的检验功效及其渐近性。研究方法:运用蒙特卡罗模拟实验方法结合相关影响因素对两种检验方法进行分析与比较。影响因素包括样本容量、解释变量的随机性及自相关性、随机误差项的自相关程度以及分布形态。研究发现:DW和LM检验功效与样本容量和随机误差项的自相关程度正相关,与解释变量的自相关程度负相关;解释变量的随机性对DW和LM检验功效无显著影响;误差项的几种常见分布形态的变化对DW和LM检验功效的影响可以忽略;在误差项存在一阶自相关的情况下,DW检验效果优于LM检验效果。研究创新:以DW检验和LM检验的假设条件为出发点,探究比较不同条件下自相关检验方法的检验功效。研究价值:在实证研究背景下为有效地选择自相关检验方法提供借鉴参考。     

Time Series Based Errors and Empirical Errors in Fertility Forecasts in the Nordic Countries*

We use ARCH time series models to derive model based prediction intervals for the Total Fertility Rate (TFR) in Norway, Sweden, Finland, and Denmark up to 2050. For the short term (5–10 yrs), expected TFR‐errors are compared with empirical forecast errors observed in historical population forecasts prepared by the statistical agencies in these countries since 1969. Medium‐term and long‐term (up to 50 years) errors are compared with error patterns based on so‐called naïve forecasts, i.e. forecasts that assume that recently observed TFR‐levels also apply for the future.     

Psychological contract breach,high-performance work system and engagement: the mediated effect of person-organization fit

Based on social exchange theory, we developed and tested a mediated moderation model to investigate the effects of psychological contract breach (PCB), person–organization fit (P–O fit) and high-performance work system (HPWS) on employee engagement. Using a sample of 255 employees in China, our results revealed that PCB has significant negative effect on employee engagement, and P–O fit partially mediates this relationship. We further found that high levels of perceived HPWS aggravate not buffer the negative effect of PCB on employee engagement and P–O fit. In addition, the interaction of HPWS and PCB on employee engagement is mediated by P–O fit. Implications for the research and practices, limitations and directions for future research are discussed.