On using multiple imputation for exploratory factor analysis of incomplete data

Nassiri, Vahid; Lovik, Anikó; Molenberghs, Geert; Verbeke, Geert

doi:10.3758/s13428-017-1013-4

Cited by 54 publications

(40 citation statements)

References 32 publications

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“…The factor retention criterion (here parallel analysis, Horn, 1965, was chosen to determine the number of factors) was either applied to each of the m imputed data sets and the most frequent solution was selected or applied to an averaged correlation matrix. Latter is quite similar to the approach of Nassiri et al (2018). Choosing the best retention criterion can be challenging as their performance varies under different conditions (e.g., Auerswald & Moshagen, 2019; van der Eijk & Rose, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Mainly default settings were used for the imputation process ( m = 5 , sequence of imputation from left to right, default tolerance for the Amelia algorithm, starting values for the EM algorithm were obtained from the observed data with listwise deletion and no priors were specified for the sufficient statistics). Five imputed data sets ( m = 5 ) seemed to be a good choice as comparable studies (e.g., Dray & Josse, 2015; Lorenzo-Seva & Van Ginkel, 2016; Nassiri et al, 2018) also used m = 5 (the latter used m = 10 ). As we did not want to estimate standard errors for model parameters, more than five imputed data sets would have been an unnecessary computational burden.…”

Section: Methodsmentioning

confidence: 99%

“…One prominent example in the context of structural equation modelling is the so-called full-information maximum likelihood where partially complete data are integrated in the likelihood function so that information about the underlying marginals of incomplete variables can reduce bias (Enders & Bandalos, 2001). However, as Nassiri, Lovik, Molenberghs, and Verbeke (2018) demonstrated that full-information maximum likelihood often yields nonpositive definite covariance matrices, we decided to not include full-information maximum likelihood in our study. Of course, there are further imputation models that can be used within the FCS framework or as an alternative multiple imputation method—namely, hot-deck imputation (Andridge & Little, 2010), which also borrows data from similar observations that serve as donors like in the predictive mean matching approach (yet without the regression model), any type of (more complex) regression models like mixed linear models or splines that might be useful for complex missing data mechanisms, or clustering-based imputation (Zhang, Zhang, Zhu, Qin, & Zhang, 2008) that uses a kernel-based method to generate data from observations that are similar to the respective observation with missingness.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

Goretzko

Heumann

Bühner

2019

Educational and Psychological Measurement

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Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis—and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation–maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions—in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

Goretzko

Heumann

Bühner

2019

Educational and Psychological Measurement

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“…Of those, 1212 answered all attitudinal questions. To compute factor loadings for a dataset with missing values, we used the multiple imputation method and software of Nassiri et al [51].…”

Section: Conflicts Of Interestmentioning

confidence: 99%

How Will the COVID-19 Pandemic Affect the Future of Urban Life? Early Evidence from Highly-Educated Respondents in the United States

et al. 2020

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Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.

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“…One suggested approach to deal with missing data in dimension reduction techniques consists in estimating the covariance matrix on the complete data (i.e. in each pair of exposures' complete data), and then perform principal component analysis or factor analysis on this matrix [40]. Bayesian model-based clustering techniques can automatically handle missing data without having to use imputations.…”

Section: Dimensionality Of the Exposomementioning

confidence: 99%

Applying the exposome concept in birth cohort research: a review of statistical approaches

et al. 2020

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The exposome represents the totality of life course environmental exposures (including lifestyle and other non-genetic factors), from the prenatal period onwards. This holistic concept of exposure provides a new framework to advance the understanding of complex and multifactorial diseases. Prospective pregnancy and birth cohort studies provide a unique opportunity for exposome research as they are able to capture, from prenatal life onwards, both the external (including lifestyle, chemical, social and wider community-level exposures) and the internal (including inflammation, metabolism, epigenetics, and gut microbiota) domains of the exposome. In this paper, we describe the steps required for applying an exposome approach, describe the main strengths and limitations of different statistical approaches and discuss their challenges, with the aim to provide guidance for methodological choices in the analysis of exposome data in birth cohort studies. An exposome approach implies selecting, pre-processing, describing and analyzing a large set of exposures. Several statistical methods are currently available to assess exposome-health associations, which differ in terms of research question that can be answered, of balance between sensitivity and false discovery proportion, and between computational complexity and simplicity (parsimony). Assessing the association between many exposures and health still raises many exposure assessment issues and statistical challenges. The exposome favors a holistic approach of environmental influences on health, which is likely to allow a more complete understanding of disease etiology.

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On using multiple imputation for exploratory factor analysis of incomplete data

Cited by 54 publications

References 32 publications

Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

How Will the COVID-19 Pandemic Affect the Future of Urban Life? Early Evidence from Highly-Educated Respondents in the United States

Applying the exposome concept in birth cohort research: a review of statistical approaches

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