Missing Data Pattern

Missing data pattern and mechanism
There is need for a consideration of pattern and mechanism of missing data before deciding on the strategy to be employed in dealing with missing values in a dataset (Penny & Atkinson 2012). Common examples of missing data pattern are Univariate, Monotone and Arbitrary(J L Schafer & Graham 2002). A univariate missing data pattern as described by Schafer and Graham (2002) is when all missing values in a dataset are only observed in a single variable and the data in other variables are completely observed. An example is fig (1.2) which indicates that the variable y has missing values but observations in a set of p variables X1 . . . Xp is complete no missing values. Also in univariate pattern, y may represent

The effect of these factors on data missing cannot be overlooked because evidence from past studies have shown that demographic differences of study participants affects the validity of study results and findings(Goldberg et al. 2001; Galea & Tracy 2007c). This literature review is aimed at examining factors that could influence non-response and continued participation of samples in cohort studies. It will highlight the impact of sociodemographic characteristics of subjects on missing data and study quality. Morse so, with the declining participation rate, the review will attempt to address the issues bordering participation of different groups of subjects and how these could be addressed, accommodated or adjusted in study design, planning and data

A multivariate analyses of respondents’ demographic characteristics in a longitudinal autism research, identified increasing child age and decreasing maternal age as strong predictors of non-response to a health survey questionnaire (Kalb et al 2012) Adolescents whose characteristics are associated with poor health are less likely to participate in health surveys. These characteristics include low maternal income or education; less favourable lifestyle which includes substance misuse and alcohol. in addition, young adults with young mothers; living in urban areas; low cognitive performance; and those with psychiatric illness, are less likely to participate in health survey (Kalb et, al 2012) In general, individuals of low socioeconomic status have lower response and participation rates in health surveys(Goldberg et al. 2001; Galea & Tracy 2007c; Fekete et al. 2015) . There were also variations on the pattern of participation based on age and gender of an individual. Participation and response rate for men and women increases with increasing age for both men and women, it eventually reaches a maximum point and thereafter started decreasing with decreasing age (Boshuizen et al. 2006). A study by Volken ( 2013) aimed at finding determinants and bias in the outcomes in Swiss health survey shows that interaction between gender and age to be significantly associated with