A recommendation for applied researchers to substantiate the claim that ordinal variables are the product of underlying bivariate normal distributions

A simulation study was carried out to study the behaviour of the polychoric correlation coefficient in data not compliant with the assumption of underlying continuous variables. Such data can produce relatively high estimated polychoric correlations (in the order of .62). Applied researchers are prone to accept these artefacts as input for elaborate modelling (e.g., structural equation models) and inferences about reality justified by sheer magnitude of the correlations. In order to prevent this questionable research practice, it is recommended that in applications of the polychoric correlation coefficient, data is tested with goodness-of-fit of the BND, that such statistic is reported in published applications, and that the polychoric correlation is not applied when the test is significant.     

Consistent estimation of correlations between observed interval variables with skewed distributions

Departures from multinormality due to skewness in observed distributions may result in inconsistent estimates of product-moment correlations between interval variables. Therefore, the robustness of the product-moment correlation estimator against skewness in the distributions of sample data on interval variables has been investigated. This estimator is robust against skewness of maximally about 1 in absolute value. If the observed distributions have larger skewnesses, the sample data on interval variables may be redistributed over normally distributed discrete variables with 10 categories each. The estimated polychoric correlations between these discrete variables represent consistent estimates of the product-moment correlations between the original interval variables in the population.     

The Ordinal Controversy Revisited

We present a discussion of the different dimensions of the ongoing controversy about the analysis of ordinal variables. The source of this controversy is traced to the earliest possible stage, measurement theory. Three major approaches in analyzing ordinal variables, called the non-parametric, the parametric, and the underlying variable approach, are identified and the merits and drawbacks of each of these approaches are pointed out. We show that the controversy on the exact definition of an ordinal variable causes problems with regard to defining ordinal association, and therefore to the interpretation of many recently designed models for ordinal variables, e.g., structure equation models using polychoric correlations, latent class models and ordinal response models. We conclude that the discussion with regard to ordinal variable modeling can only be fruitful if one makes a distinction between different types of ordinal variables. Five types of ordinal variables were identified. The problems concerning the analysis of these five types of ordinal variables are solved in some cases and remain a problem for others.     

Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables

Given that the use of Likert scales is increasingly common in the field of social research it is necessary to determine which methodology is the most suitable for analysing the data obtained; although, given the categorization of these scales, the results should be treated as ordinal data it is often the case that they are analysed using techniques designed for cardinal measures. One of the most widely used techniques for studying the construct validity of data is factor analysis, whether exploratory or confirmatory, and this method uses correlation matrices (generally Pearson) to obtain factor solutions. In this context, and by means of simulation studies, we aim to illustrate the advantages of using polychoric rather than Pearson correlations, taking into account that the latter require quantitative variables measured in intervals, and that the relationship between these variables has to be monotonic. The results show that the solutions obtained using polychoric correlations provide a more accurate reproduction of the measurement model used to generate the data.     

Robustness of statistical inferences using linear models with meta-analytic correlation matrices

To examine complex relationships among variables, researchers in human resource management, industrial-organizational psychology, organizational behavior, and related fields have increasingly used meta-analytic procedures to aggregate effect sizes across primary studies to form meta-analytic correlation matrices, which are then subjected to further analyses using linear models (e.g., multiple linear regression). Because missing effect sizes (i.e., correlation coefficients) and different sample sizes across primary studies can occur when constructing meta-analytic correlation matrices, the present study examined the effects of missingness under realistic conditions and various methods for estimating sample size (e.g., minimum sample size, arithmetic mean, harmonic mean, and geometric mean) on the estimated squared multiple correlation coefficient (R²) and the power of the significance test on the overall R² in linear regression. Simulation results suggest that missing data had a more detrimental effect as the number of primary studies decreased and the number of predictor variables increased. It appears that using second-order sample sizes of at least 10 (i.e., independent effect sizes) can improve both statistical power and estimation of the overall R² considerably. Results also suggest that although the minimum sample size should not be used to estimate sample size, the other sample size estimates appear to perform similarly.     

Algorithms as work designers: How algorithmic management influences the design of jobs

We review the literature on algorithmic management (AM) to bridge the gap between this emerging research area and the well-established theory and research on work design. First, we identify six management functions that algorithms are currently able to perform: monitoring, goal setting, performance management, scheduling, compensation, and job termination. Second, we show how each AM function affects key job resources (e.g., job autonomy, job complexity) and key job demands (e.g., workload, physical demands); with each of these resources and demands being important drivers of worker motivation and their well-being. Third, rejecting a deterministic perspective and drawing on sociotechnical systems theory, we outline key categories of variables that moderate the link between AM on work design, namely transparency, fairness and human influence (e.g., whether workers can control the system). We summarize our review in the form of a model to help guide research on AM, and to support practitioners and designers in the creation and maintenance of meaningful jobs in the era of algorithms.     

Evaluating estimation methods for ordinal data in structural equation modeling

This study examined the performance of two alternative estimation approaches in structural equation modeling for ordinal data under different levels of model misspecification, score skewness, sample size, and model size. Both approaches involve analyzing a polychoric correlation matrix as well as adjusting standard error estimates and model chi-squared, but one estimates model parameters with maximum likelihood and the other with robust weighted least-squared. Relative bias in parameter estimates and standard error estimates, Type I error rate, and empirical power of the model test, where appropriate, were evaluated through Monte Carlo simulations. These alternative approaches generally provided unbiased parameter estimates when the model was correctly specified. They also provided unbiased standard error estimates and adequate Type I error control in general unless sample size was small and the measured variables were moderately skewed. Differences between the methods in convergence problems and the evaluation criteria, especially under small sample and skewed variable conditions, were discussed.     

中国经济周期特征与太阳黑子均衡

本文在Benhabib、Farmer（1994）、Farmar、Guo（1994,1995）的不确定性均衡模型框架上,构建了一个考虑了信念偏差所代表的不确定因素和政府支出冲击的太阳黑子RBC模型来解释宏观经济的周期波动;并利用中国改革开放以来的经济数据,对该模型进行了实证检验。我们发现,该模型能够解释80%以上的中国经济波动特征;可以合理预测各宏观经济变量与产出之间的协动关系,合理预测各变量与劳动生产率之间的相关关系;对中国经济的解释力远远高于标准RBC模型,说明这一模型比较适合解释中国经济波动。另外,还发现信念冲击与政府支出冲击均是中国宏观经济波动的重要冲击来源,分别可以解释约30%、60%的经济波动,表明二者均是中国经济波动不可忽略的重要影响因素。我们的发现从不确定均衡和财政政策变动角度对宏观经济波动(比如,消费、就业、投资)的一些现象提供了合理解释。     

Statistical efficiency in the linear combination of forecasts

In seeking an efficient combination of forecasts which minimises the forecast error variance, many methods have been suggested. Through analysis, simulation and case studies, this paper seeks to develop insights into the statistical circumstances which influence the relative accuracy of six of these methods. The six methods chosen have all been advocated in various publications and consist of ‘equal weighting’ (i.e., pooled average), ‘optimal’ (i.e., error variance minimising), ‘optimal with independence assumption’ (i.e., error variance minimising assuming zero correlation between individual forecast errors) and three variations on the formulation of a Bayesian combination based upon posterior probabilities. The statistical circumstances reflected varying conditions of relative forecast errors, error correlations and outliers.     

Bias corrections for two-step fixed effects panel data estimators

This paper introduces large-T bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These models include systems of equations with limited dependent variables and unobserved individual effects, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time varying heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control variable from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control variable accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two-step nature of the procedure. As these two-step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.     

A person-centered approach to the study of commitment

Theories of workplace commitment have become increasingly complex with propositions regarding its multiple-component structure (e.g., affective, normative, continuance) and multiple foci (e.g., organization, supervisor, team). To date, most research has taken a variable-centered approach (e.g., regression, SEM) to address the additive and interactive effects of commitment components and foci on behavior and well-being. This assumes that research samples are homogeneous and that the same theoretical framework and empirical findings apply uniformly to employees in general. More recently, it has been proposed that a sample can contain subgroups and that the variables of interest (e.g., commitment components or foci) might combine and relate differently to other variables within these subgroups. Consequently, there has been an increase in the use of person-centered strategies (e.g., cluster analysis, latent profile analysis) to identify and compare these subgroups. We provide an overview of commitment theory and research to demonstrate how use of a person-centered research strategy can provide new insights into the nature and implications of commitment. We also provide a critical evaluation of person-centered strategies with the objective of encouraging greater use of more advanced analytic procedures in future research. Finally, we discuss the benefits of person-centered research for theory and practice.     

Factor analysis of the Big Five Questionnaire using polychoric correlations in children

Although the Big Five Questionnaire for children (BFQ-C) (C. Barbaranelli et al., Manuale del BFQ-C. Big Five Questionnaire Children, O.S. Organizazioni, Firenze, 1998) is an ordinal scale, its dimensionality has often been studied using factor analysis with Pearson correlations. In contrast, the present study takes this ordinal metric into account and examines the dimensionality of the scale using factor analysis based on a matrix of polychoric correlations. The sample comprised 852 subjects (40.90% boys and 59.10% girls). As in previous studies the results obtained through exploratory factor analysis revealed a five-factor structure (extraversion, agreeableness, conscientiousness, emotional instability and openness). However, the results of the confirmatory factor analysis were consistent with both a four and five-factor structure, the former showing a slightly better fit and adequate theoretical interpretation. These data confirm the need for further research as to whether the factor ‘Openness’ should be maintained as an independent factor (five-factor model), or whether it would be best to omit it and distribute its items among the factors ‘Extraversion’ and ‘Conscientiousness’ (four-factor model).     

About and beyond leading uniqueness and belongingness: A systematic review of inclusive leadership research

The purpose of this systematic review of 107 papers is to address the conceptual confusion about what inclusive leadership (IL) behavior entails and understand the theoretical development of IL. Synthesizing the divergent conceptualizations of inclusive leader behaviors, we propose a multi-level (i.e., employee, team, organizational) model of IL behavior consisting of four dimensions namely, fostering employee's uniqueness (e.g., promoting diversity); strengthening belongingness within a team (e.g., building relationships); showing appreciation (e.g., recognizing efforts and contributions); and supporting organizational efforts (e.g., promoting organizational mission on inclusion). Further, we provide a summary of studied variables as a nomological network in relation to inclusive leadership and an overview of the different theories (e.g., social exchange, intrinsic motivation) supporting the respective relationships and explaining the underlying mechanisms (e.g., reciprocity, motivation). We propose future research to empirically test the multi-level model of IL and examine the predictive value in terms of employee and organizational outcomes.     

Applying structural equation modeling to report psychometric properties of Chinese version 10-item CES-D depression scale

This study investigates the psychometric properties of Chinese-version short-form 10-item CES-D (i.e., CESD-10) in clinical depression patients and non-clinical college students by structural equation modeling approach. Several statistical procedures are applied to investigate the psychometric properties of CESD-10. First, the single factor model of CESD-10 shows merely mediocre fit due to “error term correlations”. The two pairs of error term correlations indicate that CESD-10 has redundant items and negative-worded effect. The revised model, based on the modification index, demonstrates good fit, and is adopted for subsequent analysis. Results of the SEM-based method show that configure invariance holds, while weak factorial invariance is not supported. A comparison the psychometric properties of the two groups shows that the clinical sample has better reliability, while the non-clinical sample has superior factorial validity. This finding confirms that the Chinese-version CESD-10 is most appropriate for the nonclinical, general population, although it is also valid for the clinically depressed.     

On understanding mutual fund terminations

We examine the determinants of US mutual fund terminations and provide estimates of mutual fund hazard functions. We find that mutual fund termination correlates with a variety of fund specific variables as well as with market variables such as the S&P 500 index and the short-term interest rate. We also test the underlying assumptions of the semi-parametric Cox model and reject proportionality, thus calling to question the use of this model in forming estimates of mutual fund hazard functions. We find that different fund categories exhibit distinct hazard functions depending on the fund’s investment objectives.

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

Instrumental variable estimation in the presence of many moment conditions

This paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by shrinking the influence of a subset of instrumental variables. The procedure can be understood as a two-step process of shrinking some of the OLS coefficient estimates from the regression of the endogenous variables on the instruments, then using the predicted values of the endogenous variables (based on the shrunk coefficient estimates) as the instruments. The shrinkage parameter is chosen to minimize the asymptotic mean square error. The optimal shrinkage parameter has a closed form, which makes it easy to implement. A Monte Carlo study shows that the shrinkage method works well and performs better in many situations than do existing instrument selection procedures.     

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) for predicting/fitting quarterly variables (e.g. real GDP). This paper proposes a multivariate singular spectrum analysis (MSSA) based method for mixed-frequency interpolation and forecasting, which can be used for any mixed-frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed-frequency modelling and forecasting, and useful extensions of this method, are also discussed.     

A new approach to maximum likelihood estimation of sum-constrained linear models in case of undersized samples

Maximum likelihood procedures for estimating sum-constrained models like demand systems, brand choice models and so on, break down or produce very unstable estimates when the number of categories ( n ) is large as compared with the number of observations ( T ). In applied research, this problem is usually resolved by postulating the contemporaneous covariance matrix of the dependent variables to be known apart from a constant of proportionality. In this paper we develop a maximum likelihood procedure for sum-constrained models with large numbers of categories, which does not require too many observations, but nevertheless allows for n covariance parameters to be estimated freely.     

Information systems project manager soft competencies: A project‐phase investigation

This article investigates the soft competencies by project phase that information systems (IS) project managers require for project success. The authors conducted 33 qualitative interviews to collect data from a sample of 22 IS project managers and business leaders located in Calgary, Alberta, Canada. The authors identified the key competencies for each of the IS project phases (initiation, planning, implementation, and closeout). The competencies were sorted into competency categories: personal attributes (e.g., eye for details), communication (e.g., effective questioning), leadership (e.g., create an effective project environment), negotiations (e.g., consensus building), professionalism (e.g., lifelong learning), social skills (e.g., charisma), and project management competencies (e.g., manage expectations). Each of the most important competencies is discussed and interconnections among competencies identified. How this research can be used by the practitioner and academic communities and the broader implications of this research are examined.