Attention skills and looking to television in children from low income families

Brown, Danielle D.; Weatherholt, Tara; Burns, Barbara

doi:10.1016/j.appdev.2010.04.003

Cited by 7 publications

(10 citation statements)

References 57 publications

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“…6 studies looked at sleep [13,52,96,105,121,122], and (4) 2 studies assessed other outcomes, including TV viewing behaviours in a laboratory setting [123] and mothers' perceptions on children's physical activity [124]. For glycaemic control, both maternal and paternal household chaos scores were positively associated with HbA1c in children and adolescents with type 1 diabetes [114] and in children aged 1-13 years with type 1 diabetes mellitus [116].…”

Section: Physical Health Health Behaviours and Communication Disordersmentioning

confidence: 99%

The relationship between household chaos and child, parent, and family outcomes: a systematic scoping review

2020

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Background: Household chaos, represented by the level of disorganisation or environmental confusion in the home, has been associated with a range of adverse child and family outcomes. This review aims to (1) identify how household chaos is measured, (2) chart study details of household chaos literature, and (3) map the existing literature with respect to the relationship between household chaos and child, parent, and family outcomes. We expect that this review will highlight the need to consider the importance of household chaos in child well-being research, particularly in those families where children may be more vulnerable to the adverse effects of household chaos. Methods: We searched five electronic databases (last updated September 1st 2018) in addition to Google Scholar, and identified publications via a 3-stage screening process, which was conducted by two researchers. Published studies were included if they investigated the association between household chaos and child, parent, or family outcomes. Research that investigated household chaos as a mediator or moderator, or that investigated how the relationship between household chaos and the outcome of interest was mediated or moderated, were also included. Results: One hundred twelve studies in 111 publications were included. The majority were conducted in the United States (n = 71), and used either cross-sectional (n = 60) or longitudinal (n = 49) study designs. Outcomes of interest were categorised into seven categories: (1) cognitive and academic (n = 16), (2) socio-emotional and behavioural (n = 60), (3) communication (n = 6), (4) parenting, family, and household functioning (n = 21), (5) parent outcomes (n = 6), (6) hormone (n = 8), and (7) physical health and health behaviours (n = 19). There was consistent evidence for significant correlations between household chaos and adverse outcomes across all seven categories in diverse populations with respect to age, disease status, and socioeconomic status (SES). Conclusion: There is consistent evidence for associations between household chaos and a number of adverse child, parent, and family-level outcomes. Household chaos may also help describe variations in outcomes between low SES and child development.

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Section: Physical Health Health Behaviours and Communication Disordersmentioning

confidence: 99%

The relationship between household chaos and child, parent, and family outcomes: a systematic scoping review

2020

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“…Overall, 19 analyses were conducted that investigated the role of household chaos on health and health behaviours: (1) 9 papers specifically looked at physical health outcomes, disease, and communication disorder outcomes (13,83,102,109,(114)(115)(116)(117)(118), including glycaemic control, child health, weight status, and stutter, (2) 3 studies investigated diet and dietary behaviours (102,119,120), (3) 6 studies looked at sleep (14,53,97,106,121,122), and (4) 2 studies assessed other outcomes, including TV viewing behaviours in a laboratory setting (123) and mothers' perceptions on children's physical activity (124). For glycaemic control, both maternal and paternal household chaos scores were positively associated with HbA1c in children and adolescents with type 1 diabetes (114) and in children aged 1-13 years with type 1 diabetes mellitus (116).…”

Section: Physical Health Health Behaviours and Communication Disordersmentioning

confidence: 99%

The Relationship between Household Chaos and Child, Parent, and Family Outcomes: A Systematic Scoping Review

Marsh

Dobson

Maddison

2020

Preprint

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Background: Household chaos, represented by the level of disorganisation or environmental confusion in the home, has been associated with a range of adverse child and family outcomes. This review aims to (1) identify how household chaos is measured, (2) chart study details of household chaos literature, and (3) map the existing literature with respect to the relationship between household chaos and child, parent, and family outcomes. We expect that this review will highlight the need to consider the importance of household chaos in child well-being research, particularly in those families where children may be more vulnerable to the adverse effects of household chaos.Methods: We searched five electronic databases (last updated September 1st 2018) in addition to Google Scholar, and identified publications via a 3-stage screening process, which was conducted by two researchers. Published studies were included if they investigated the association between household chaos and child, parent, or family outcomes. Research that investigated household chaos as a mediator or moderator, or that investigated how the relationship between household chaos and the outcome of interest was mediated or moderated, were also included. Results: 112 studies in 111 publications were included. The majority were conducted in the United States (n=71), and used either cross-sectional (n=60) or longitudinal (n=49) study designs. Outcomes of interest were categorised into seven categories: (1) cognitive and academic (n=16), (2) socio-emotional and behavioural (n=60), (3) communication (n=6), (4) parenting, family, and household functioning (n=21), (5) parent outcomes (n=6), (6) hormone (n=8), and (7) physical health and health behaviours (n=19). There was consistent evidence for significant correlations between household chaos and adverse outcomes across all seven categories in diverse populations with respect to age, disease status, and socio-economic status (SES).Conclusion: There is consistent evidence for associations between household chaos and a number of adverse child, parent, and family-level outcomes. Household chaos may also help describe variations in outcomes between low SES and child development.

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“…Examples in the field of medicine include the latency period of infectious diseases (Kondo, 1977), survival times for certain types of cancer (Claret et al, 2009;Qazi, DuMez, & Uckun, 2007), and the age of onset of Alzheimer's disease (Horner, 1987). In the social sciences, an example would be the age at which people first get married (Preston, 1981), while in psychology the log-normal distribution provides a good fit to data on reaction times or response latencies (Shang-wen & Ming-hua, 2010;Ulrich & Miller, 1993; Van der Linden, 2006), as well as to data on attentional skills (Brown, Weatherholt, & Burns, 2010). With survival data, a good fit is provided not only by the log-normal distribution, but also by the exponential distribution and its extensions (Weibull, gamma, and Gompertz), as well as by the generalized gamma and log-logistic distributions (Lee & Wang, 2003).…”

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confidence: 99%

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

et al. 2012

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Using a Monte Carlo simulation and the KenwardRoger (KR) correction for degrees of freedom, in this article we analyzed the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential, and log-normal. This showed that, with homogeneous between-groups covariance and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. However, with heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null, the best fit was shown by the between-subjects heterogeneous unstructured population matrix, which was the case for all of the distributions analyzed. By contrast, with positive or negative pairings, the within-subjects and betweensubjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased-as occurs, for example, in the log-normal distribution-the robustness of KR was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis, the analysis showed that the degree of robustness increased in line with the amount of kurtosis. Keywords Longitudinal data . Linear mixed model . Kenward-Roger method . Robustness . Nonnormal distributionsOver the last three decades, the analysis of repeated measures data has centered on the linear mixed model (LMM). Laird and Ware (1982) established the basis of the LMM with the incorporation of the within-subjects error correlation. Their work was subsequently extended by Cnaan, Laird, and Slasor (1997) and Verbeke and Molenberghs (2000), who applied the LMM to longitudinal data. In contrast to analyses that are based on variances (ANOVA and MANOVA), the LMM models the structure of the covariance matrix. This enables a more efficient estimation of the fixed effects and, consequently, yields more robust statistical tests. However, when the covariance structures are not properly fitted and the sample sizes are small, the Type I error rate tends to rise (Wright & Wolfinger, 1996).With respect to the covariance structure, Keselman, Algina, Kowalchuk, and Wolfinger (1999) demonstrated that when the covariance matrix is not spherical, the degrees of freedom associated with the conventional F test are too large. One way of controlling this bias is to apply the procedure developed by Kenward and Roger (1997; henceforth, the KR method) to correct the degrees of freedom. Studies by Kowalchuk, Keselman, Algina, and Wolfinger (2004) and Vallejo and Ato (2006) showed that with an adequate covariance structure, the KR method is able, in

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Attention skills and looking to television in children from low income families

Cited by 7 publications

References 57 publications

The relationship between household chaos and child, parent, and family outcomes: a systematic scoping review

The relationship between household chaos and child, parent, and family outcomes: a systematic scoping review

The Relationship between Household Chaos and Child, Parent, and Family Outcomes: A Systematic Scoping Review

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

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