Fan Yang Wallentin

• Doctor of Statistics
• Professor at Uppsala University

The mention of the COVID-19 waves is as prevalent as the pandemic itself. Identifying the beginning and end of the wave is critical to evaluating the impact of various COVID-19 variants and the different pharmaceutical and non-pharmaceutical (including economic, health and social, etc.) interventions. We demonstrate a scientifically robust method t...

Evaluations of price-based demand response programs tend to focus on users' electricity use patterns and/or their practical experiences. Less is known about the effects that price-based demand response programs have on cognitive drivers and barriers to energy-using behaviors and habits, or how well these predict timing of households' electricity us...

This book can be used by Master and PhD students and researchers in the economic, social, behavioral and many other sciences who need to have a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a text book for courses on multivariate statistical analysis. There are man...

A controversy about the Swedish strategy of dealing with COVID-19 during the early period is how decision-making was based on evidence, which refers to data and data analysis. During the earliest period of the pandemic, the Swedish decision-making was based on subjective perspective. However, when more data became available, the decision-making sto...

The mention of the COVID-19 waves is as prevalent as the pandemic itself. We scientifically identify waves and the breaking points at which they begin and end from the beginning of the pandemic till June 2021. We use the Break Least Square method to determine the significance of COVID-19 waves for global-, regional-, and country-level data. The res...

The model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal . We develop a unified MIIV ap...

A handful of studies have been devoted to nonlinear structural equation modeling (SEM) in the literature. However, they generally overlooked the interactions among exogenous and endogenous latent variables and the interactions among endogenous latent variables. In this study, we propose a maximum likelihood approach for a nonlinear SEM model that i...

Factor score regression has recently received growing interest as an alternative for structural equation modeling. However, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. We perform a simulation study to examine how a selection of factor scoring methods compare when estimating re...

This research develops and tests a theoretical model on the impact of antecedents on technology adoption developing valuable insight into influencing levels of attitudes and expectations prior to deployment of a mandated to use the system. Two stages during the pre-deployment phase of a mission-critical, mandated system are studied. Results are rep...

An explicit goal of foreign aid is to promote female empowerment and gender equality in developing countries. We investigate if foreign aid achieves this intended goal by examining its impact on gender performance of recipient countries at the country level. Employing structural equation models, our results suggest that aid alone, even when targete...

The literature on non-linear structural equation modeling is plentiful. Despite this fact, few studies consider interactions between exogenous and endogenous latent variables. Further, it is well known that treating ordinal data as continuous produces bias, a problem which is enhanced when non-linear relationships between latent variables are incor...

Abstract The ambitious United Nations Sustainable Development Goals (SDGs) have been criticized for being universal, broadly-framed, inconsistent and difficult to quantify, implement and monitor. We contribute by quantifying and prioritising the SDGs and their impact on sustainable development. We employ structural equation models (SEM) to investig...

Background: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis mo...

Research on students’ attitudes toward statistics has attracted many statistics instructors and statistics education researchers. In this study, we use confirmatory factor analysis to analyze data collected from an introductory statistics course using the Survey of Attitudes toward Statistics. The results suggest that the items and six factors are...

Purpose: To validate the Swedish Safety Attitudes Questionnaire-operating room (SAQ-OR) version by re-evaluating its psychometric properties for the surgical team. Design: Cross-sectional questionnaire study. Methods: 541 surgical team members including perioperative nurses, physicians, and licensed practical nurses at three Swedish hospitals...

The problem of penalized maximum likelihood (PML) for an exploratory factor analysis (EFA) model is studied in this paper. An EFA model is typically estimated using maximum likelihood and then the estimated loading matrix is rotated to obtain a sparse representation. Penalized maximum likelihood simultaneously fits the EFA model and produces a spar...

Objectives To determine the internal consistency and the underlying components of our translated and adapted Swedish version of the General Medical Council's multisource feedback questionnaires (GMC questionnaires) for physicians and to confirm which aspects of good medical practice the latent variable structure reflected. Methods From October 201...

Purpose To psychometrically test the Perceived Perioperative Competence Scale-Revised (PPCS-R) in the Swedish context. Design Cross-sectional survey. Methods The 40-item PPCS-R was translated into Swedish using a forward-translation approach. A census of 2,902 registered nurse anesthetists (RNAs) and operating room (OR) nurses was drawn from a da...

We examine how the impact on women empowerment varies with respect to the location and type of group linkage of the respondent. Using household survey data from five states in India, we correct for selection bias to estimate a structural equation model. Our results reveal that in the southern states of India empowerment of women takes place through...

Purpose The purpose of this paper is to investigate how to measure the effect of unobservable factors on residential choice behaviour in an attempt to advance the understanding of how to perform advanced market analysis. This research is important to residential developers, as the diversity of preferences is increasingly driven by lifestyle-based h...

Asymptotic robustness against misspecification of the underlying distribution for the polychoric correlation estimation is studied. The asymptotic normality of the pseudo-maximum likelihood estimator is derived using the two-step estimation procedure. The t distribution assumption and the skew-normal distribution assumption are used as alternatives...

This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regress...

Data collected from questionnaires are often in ordinal scale. Unweighted least squares (ULS), diagonally weighted least squares (DWLS) and normal-theory maximum likelihood (ML) are commonly used methods to fit structural equation models. Consistency of these estimators demands no structural misspecification. In this article, we conduct a simulatio...

Errors-in-variables (EIV) identification refers to the problem of consistently estimating linear dynamic systems whose output and input variables are affected by additive noise. Various solutions have been presented for identifying such systems. In this study, EIV identification using Structural Equation Modeling (SEM) is considered. Two schemes fo...

A multi-group factor model is suitable for data originating from different strata. However, it often requires a relatively large sample size to avoid numerical issues such as non-convergence and non-positive definite covariance matrices. An alternative is to pool data from different groups in which a single-group factor model is fitted to the poole...

We examine how the impact on women empowerment varies with respect to the location and type of group linkage of the respondent. Using household survey data from five states in India, we correct for selection bias to estimate a structural equation model. Our results reveal that in the southern states of India empowerment of women takes place through...

We examine how the impact on women empowerment varies with respect to the location and type of group linkage of the respondent. Using household survey data from five states in India, we correct for selection bias to estimate a structural equation model. Our results reveal that in the southern states of India empowerment of women takes place through...

Two approaches for errors-in-variables identification are compared. Covariance matching (CM) is known to be a computationally efficient method with good performance. Structural equation modeling (SEM) has been used for many years for static problems, particularly for social science applications. It is shown here how the SEM approach can be applied...

Background Tens of millions of patients worldwide suffer from avoidable disabling injuries and death every year. Measuring the safety climate in health care is an important step in improving patient safety. The most commonly used instrument to measure safety climate is the Safety Attitudes Questionnaire (SAQ). The aim of the present study was to es...

In the investigation of the effect of attention deficit hyperactivity disorder (ADHD) symptoms on school careers there is a need to study the role of adolescent and childhood ADHD symptoms and academic achievement, and to incorporate measures that include the individual's perspective. Our aim was to gain an overview of the long-term development of...

A pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and fitted both in an exploratory and confirmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and three-stage limited information estimation methods, na...

Normal-distribution-based maximum likelihood (ML) and multiple imputation (MI) are the two major procedures for missing data analysis. This article compares the two procedures with respects to bias and efficiency of parameter estimates. It also compares formula-based standard errors (SEs) for each procedure against the corresponding empirical SEs....

We evaluate the impact of economic and non-economic factors on women’s empowerment of Self-Help Group (SHG) members. We estimate a structural equation model (SEM) and correct for ordinality in the data to account for the impact of the latent factors on women’s empowerment. Our SEM results reveal that for the SHG members, the economic factor is the...

Previous research has almost exclusively examined sexism (negative attitudes toward women) from either a personality or a social-psychology perspective. In two studies (N = 379 and 182, respectively), we combine these perspectives and examine whether sexism is best explained by personality (Big-Five factors, social dominance orientation, and right-...

Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to...

Previous research has examined prejudice from either a personality or a social psychology perspective. In the present study (N = 182), we combine these perspectives and examine whether ethnic prejudice is best explained by personality (Big-Five factors, social dominance orientation, and right-wing authoritarianism) or social psychology (ethnic grou...

Microfinance programmes like the Self Help Bank Linkage Program in India have been increasingly promoted for their positive economic impact and the belief that they empower women. However, only a few studies rigorously examine the link between microfinance and women's empowerment. This article contributes to this discussion by arguing that women's...

Microfinance programs like Self Help Group Bank linkage program (SHG), aim to empower women through provision of financial services. We investigate this further to determine whether it is the economic or the non-economic factors that have a greater impact on empowering women. Using household survey data on SHG from India, a general structural model...

http://www.wikigender.org/index.php/Poverty_in_Focus:_Gender_Equality

Purpose The purpose of this paper is to empirically investigate the relationship underlying the often used adage “what gets measured gets managed”. Design/methodology/approach The paper starts by reviewing the critique of the adage and then testing it by surveying 109 managers from 41 organizations. The paper includes the idea of mobilizing in the...

Many models in social psychology, which have been developed to explain behavior, postulate interaction effects between explanatory latent variables. In the last years, there have been many new developments for estimating interactions between latent variables in structural equation modeling. However, there have been very few applica-tions with real...

Theory and methods for structural equation modeling with latent variables have been very well developed for the case when the relationships between variables are linear. In recent years there has been considerable interest in extending and applying structural equation models to situations where there are nonlinear relationships involving latent var...