Principled missing data methods for researchers

The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication

Using Multinomial Logistic Models To Predict Adolescent Behavioral Risk

Multinomial logistic regression was applied to data comprising 432 adolescents’ self reports of engagement in risky behaviors. Results showed that gender, intention to drop from the school, family structure, self-esteem, and emotional risk were effective predictors collectively. Three methodological issues were highlighted: (1) the use of odds ratio, (2) the absence of an extension of the Hosmer and Lemeshow test for multinomial logistic models, and (3) the missing data problem. Psychologists and educators can utilize findings to plan prevention programs, as well as to apply the versatile and effective logistic technique in psychological, educational, and health research concerning adolescents

Constructing Confidence Intervals for Effect Sizes in ANOVA Designs

A confidence interval for effect sizes provides a range of plausible population effect sizes (ES) that are consistent with data. This article defines an ES as a standardized linear contrast of means. The noncentral method, Bonett’s method, and the bias-corrected and accelerated bootstrap method are illustrated for constructing the confidence interval for such an effect size. Results obtained from the three methods are discussed and interpretations of results are offered

Handling Missing Data in Single-Case Studies

Multiple imputation is illustrated for dealing with missing data in a published SCED study. Results were compared to those obtained from available data. Merits and issues of implementation are discussed. Recommendations are offered on primal/advanced readings, statistical software, and future research

The Use Of Hierarchical ANCOVA In Curriculum Studies

Many educational studies are carried out in intact settings, such as classrooms or groups in which individual data were collected before and after a treatment. Researchers advocate either the use of individual scores as the unit of analysis or class means. Both approaches suffer from conceptual and methodological limitations. In this article, the use of hierarchical ANCOVA for analyzing quasiexperimental data including baseline measures is designed and promoted. It is illustrated with a realworld data set collected from a curriculum study. Results showed that the hierarchical ANCOVA is a conceptually and methodologically sound approach, and is better than ANCOVA based on individual scores or ANCOVA based on class means. The potential of using hierarchical ANCOVA designs for curriculum studies is discussed in terms of statistical power and congruence with study plans

Modeling Strategies In Logistic Regression With \u3cem\u3eSAS\u3c/em\u3e, \u3cem\u3eSPSS\u3c/em\u3e, \u3cem\u3eSystat\u3c/em\u3e, \u3cem\u3eBMDP\u3c/em\u3e, \u3cem\u3eMinitab\u3c/em\u3e, And \u3cem\u3eSTATA\u3c/em\u3e

This paper addresses modeling strategies in logistic regression within the context of a real-world data set. Six commercially available statistical packages were evaluated in how they addressed modeling issues and in the accuracy of their regression results. Recommendations are offered for data analysts in terms of each package\u27s strengths and weaknesses

Power Analysis Software for Educational Researchers

Forthcoming in Journal of Experimental Education, Jan. 2012.Given the importance of statistical power analysis in quantitative research and the repeated emphasis on it by AERA/APA journals, we examined the reporting practice of power analysis by the quantitative studies published in 12 education/psychology journals between 2005 and 200910. It was surprising to uncover that less than 2% of the studies conducted prospective power analysis. Another 3.54% computed observed power, a practice not endorsed by the literature on power analysis. In this paper, we clarify these two types of power analysis and discuss functionalities of eight programs/packages (G*Power 3.1.3, PASS 11, SAS/STAT 9.3, Stata 12, SPSS 19, SPSS/Sample Power 3.0.1, Optimal Design Software 2.01, and MLPowSim 1.0 BETA) to encourage proper and planned power analysis. Based on our review, we recommend two programs (SPSS/Sample Power and G*Power) for general-purpose univariate/multivariate analyses, and one (Optimal Design Software) for hierarchical/multilevel modeling and meta-analysis. Recommendations are also made for reporting power analysis results and exploring additional software. The paper concludes with an examination of the role of statistical power in research and viable alternatives to hypothesis testing

Algorithms for Assessing Intervention Effects in Single-Case Studies

Free web-based resources or popular software to assess six data features recommended by the What Works Clearinghouse: Procedures and Standards Handbook (IES, 2013 February) to determine intervention effects in a single-case study (Lambert, Cartledge, Heward, & Lo, 2006) are demonstrated. Lambert et al. (2006) employed a reversal (or ABAB) design and visual inspection to investigate the effectiveness of the report-card treatment in reducing disruptive behaviors in students. In our demonstration, we assessed each of the six data features separately; then integrated six assessments into one comprehensive analysis of the intervention effect. A simple approach to the determination of intervention effects illustrates how researchers and practitioners can be empowered to interpret data comprehensively and formulate evidence-based conclusions logically from well-designed and well-executed single-case studies

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries(1,2). However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world(3) and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health(4,5). However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol-which is a marker of cardiovascular riskchanged from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million-4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.Peer reviewe

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms