Aspects of Data Quality in Psychology: Missing Data and Aberrant Responses

Data preprocessing is an integral step prior to analyzing data in the social sciences. The purpose of this article is to report the current practices psychological researchers use to address data preprocessing or quality concerns with a focus on issues pertaining to aberrant responses and missing data in self report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. We found that nearly half of the studies did not report any missing data treatment (111/240; 46.25%) and if they did, the most common approach to handle missing data was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. We also found that most studies do not report any methodology to address aberrant responses (194/240; 80.83%). For studies that reported issues with aberrant responses, a study would classify 4% of the sample, on average, as suspect responses. These results suggest that most studies are either not transparent enough about their data preprocessing steps or maybe leveraging suboptimal procedures. We outline recommendations for researchers to improve the transparency and/or the data quality of their study.

Intravenous Immunoglobulin Treatment in Kawasaki Disease Decreases the Incidence of Myopia

Kawasaki disease (KD) is a systemic vasculitis that primarily affects children under the age of 5 years old. The most significant complication is coronary artery lesions, but several ocular manifestations have also been reported. Recently, one study revealed an increasing incidence of myopia among KD patients. Therefore, the aim of this study was to assess the difference in myopic incidence between Kawasaki disease (KD) patients treated with aspirin and intravenous immunoglobulin (IVIG). Materials and methods: We carried out a nationwide retrospective cohort study by analyzing the data of KD patients (ICD-9-CM code 4461) from Taiwan’s National Health Insurance Research Database (NHIRD) during the period of 1996–2013. Results: A total of 14,102 diagnosed KD were found in Taiwan during the study period. After excluded missing data, treatment strategy and age distribution, a total of 1446 KD patients were enrolled for analysis including 53 of which received aspirin (without IVIG) and 1393 of which were treated with IVIG. Patients who had myopia, astigmatism, glaucoma, cataract, etc. prior to their KD diagnosis were excluded. The age range was 0 to 6 years old. According to the cumulative curves, our results demonstrated that the myopic incidence in the IVIG group was significantly lower than the aspirin group (hazard ratio: 0.59, 95% confidence intervals: 0.36~0.96, p = 0.02). Treatment with IVIG for KD patients may have benefit for myopia control. Conclusion: Compared to aspirin, IVIG may decrease the myopic risk in KD patients. However, it needs further investigation including clinical vision survey of myopia due to the limitations of this population-based study.

Reporting Practice in Multilevel Modeling: A Revisit After 10 Years

Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results. Ten years have passed since the guidelines for reporting multilevel studies were initially published. This study reviewed new advancements in MLM and revisited the reporting practice in MLM in the past decade. A total of 301 articles from 19 journals representing different subdisciplines in education and psychology were included in the systematic review. The results showed improvement in some areas of the reporting practices, such as the number of models tested, centering of predictors, missing data treatment, software, and estimates of variance components. However, poor practices persist in terms of model specification, description of a missing mechanism, power analysis, assumption checking, model comparisons, and effect sizes. Updates on the guidelines for reporting multilevel studies and recommendations for future methodological research in MLM are presented.

Performance Evaluation of Various Missing Data Treatments in Crash Severity Modeling

Data quality, including record inaccuracy and missingness (incompletely recorded crashes and crash underreporting), has always been of concern in crash data analysis. Limited efforts have been made to handle some specific aspects of crash data quality problems, such as using weights in estimation to take care of unreported crash data and applying multiple imputation (MI) to fill in missing information of drivers’ status of attention before crashes. Yet, there lacks a general investigation of the performance of different statistical methods to handle missing crash data. This paper is intended to explore and evaluate the performance of three missing data treatments, which are complete-case analysis (CC), inverse probability weighting (IPW) and MI, in crash severity modeling using the ordered probit model. CC discards those crash records with missing information on any of the variables; IPW includes weights in estimation to adjust for bias, using complete records’ probability of being a complete case; and MI imputes the missing values based on the conditional distribution of the variable with missing information on the observed data. Those missing data treatments provide varying performance in model estimations. Based on analysis of both simulated and real crash data, this paper suggests that the choice of an appropriate missing data treatment should be based on sample size and data missing rate. Meanwhile, it is recommended that MI is used for incompletely recorded crash data and IPW for unreported crashes, before applying crash severity models on crash data.

Estimation of Missing Streamflow Data Using Anfis Models and Determination of the Number of Datasets for Anfis: The Case of Yeşilırmak River

Good data analysis is required for the optimal design of water resources projects. However, data are not regularly collected due to material or technical reasons, which results in incomplete-data problems. Available data and data length are of great importance to solve those problems. Various studies have been conducted on missing data treatment. This study used data from the flow observation stations on Yeşilırmak River in Turkey. In the first part of the study, models were generated and compared in order to complete missing data using ANFIS, multiple regression and Normal Ratio Method. In the second part of the study, the minimum number of data required for ANFIS models was determined using the optimum ANFIS model. Of all methods compared in this study, ANFIS models yielded the most accurate results. A 10-year training set was also found to be sufficient as a data set.