Results 151 to 160 of about 762,527 (191)

Academic stress and mental fatigue predict subjective but not objective internal load in adolescent soccer players-a prospective cohort study. [PDF]

Front Psychol
Nijland R, Toering T, Ivarsson A, de Jong J, Lemmink KAPM.   +4 more
europepmc   +1 more source

Intensive, Repeated Self-Report Measures: Should We Be Concerned About Changes in Data Quality Over Time?

JMIR Mhealth Uhealth
Stone AA, Schneider S, Toledo MJ, Hernandez R.   +3 more
europepmc   +1 more source

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A Test of Missing Completely at Random for Multivariate Data with Missing Values

Journal of the American Statistical Association, 1988
Abstract A common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random (MCAR); that is, whether missingness depends on the variables in the data set. One way of assessing this is to compare the means of recorded values of each variable between groups defined by whether other ...
R. Little
semanticscholar   +6 more sources

Distinguishing “Missing at Random” and “Missing Completely at Random”

The American Statistician, 1996
Abstract Missing at random (MAR) and missing completely at random (MCAR) are ignorability conditions—when they hold, they guarantee that certain kinds of inferences may be made without recourse to complicated missing-data modeling. In this article we review the definitions of MAR, MCAR, and their recent generalizations.
Daniel F. Heitjan, Srabashi Basu
semanticscholar   +4 more sources

The Generalized Estimating Equation Approach When Data are Not Missing Completely at Random

Journal of the American Statistical Association, 1997
Abstract We propose two methods for handling missing data in generalized estimating equation (GEE) analyses: mean imputation and multiple imputation. Each provides valid GEE estimates when data are missing at random. Missing outcomes are imputed sequentially starting from the outcome nearest in time to the observed outcome.
M. Paik
semanticscholar   +4 more sources

Examining the Missing Completely at Random Mechanism in Incomplete Data Sets: A Multiple Testing Approach

Structural Equation Modeling: A Multidisciplinary Journal, 2012
A multiple testing procedure for examining implications of the missing completely at random (MCAR) mechanism in incomplete data sets is discussed. The approach uses the false discovery rate concept and is concerned with testing group differences on a set of variables.
Tenko Raykov, Peter A. Lichtenberg, Daniel Paulson   +2 more
semanticscholar   +4 more sources

Imputation Methods Outperform Missing-Indicator for Data Missing Completely at Random

2019 International Conference on Data Mining Workshops (ICDMW), 2019
Missing data is a ubiquitous cross-domain problem persistent in the context of big data analytics. Approaches to deal with missing data can be partitioned into methods that impute substitute values and methods that introduce missing-indicator variables.
Barata, A. Pereira, Takes, F.W., Herik, H.J. van den, Veenman, C.J.   +3 more
openaire   +3 more sources