When fathers’ supportiveness matters most: Maternal and paternal parenting and children’s school readiness.

Martin, Anne; Ryan, Rebecca M.; Brooks‐Gunn, Jeanne

doi:10.1037/a0018073

Cited by 132 publications

(111 citation statements)

References 62 publications

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“…In recent years, research has increased concerning the associations of fathers' (including guardians') contributions to their children's psychosocial and academic outcomes (e.g., TamisLeMonda, Shannon, Cabrera, & Lamb, 2004;Martin, Ryan, & Brooks-Gunn, 2010). Studies have documented the unique contribution of fathers' activities and behaviors, since mothers and fathers differ in terms of parenting styles (Simons & Conger, 2007), parental care (Mikelson, 2008), and parental involvement (Lamb, 2004).…”

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confidence: 99%

Fathers’ Importance in Adolescents’ Academic Achievement

Whitney

Prewett

Wang

et al. 2018

IJCYFS

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Many studies have investigated mothers' impact on students' achievement, yet little is known about how various father types impact students' school performance. This study examines 6 mutually exclusive categories of father type: resident biological fathers, resident stepfathers, resident adoptive fathers, nonresident biological fathers, unknown biological fathers, and deceased fathers. Adolescents' school performance from seventh through twelfth grade is examined using data from 3 waves of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative United States secondary data source. Findings indicate different types of fathers have distinct and independent positive associations with adolescents' school achievement, after controlling for mother involvement. Adolescents with resident biological fathers had higher school performance than adolescents with nonresident fathers. Adolescents with stepfathers had higher rates of school failure than their peers living with their biological parents. The lowest achievement and the highest risk of school failure and course failure were experienced by those adolescents who did not have a resident father figure and didn't know the identity of their fathers. Implications include the need to model for the unique influence of father involvement and father type on academic achievement, and the inclusion of unique family contexts in efforts to increase adolescents' school involvement and integration.

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confidence: 99%

Fathers’ Importance in Adolescents’ Academic Achievement

Whitney

Prewett

Wang

et al. 2018

IJCYFS

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“…This result indicates whatever area they had an education, as fathers' education level increases, it supports children's school readiness level. Study results conducted by Balat (2003), Çıkrıkçı (1999) Gonca (2004) Erkan & Kırca (2010) Unutkan (2003), Alakoç Pirpir, Büyüktaşkapu Soydan, Angın (2016), Martin, Ryan & Brooks-Gunn (2010) , Yazıcı (2002) have stated that when parents' education level increases children's school readiness level also increases. These findings support the results of the current study.…”

Section: Discussionmentioning

confidence: 99%

“…While preparing effective parenting practices and stimulating environments at home, the most important factor that affects parents' decision process is thought to be their education status. Thus, in conducted studies it was stated that when the education status of the parents increases, children's school readiness level also increases (Balat, 2003, Gonca, 2004Erkan & Kırca, 2010;Martin, Ryan & Brooks-Gunn, 2010). In addition to that Unutkan (2003) asserted that parents' education level and their professions positively affected children's readiness to primary school.…”

Section: Introductionmentioning

confidence: 99%

Some variables predicting the school readiness of preschool children

Soydan

2017

Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi

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ABSTRACT.The main purpose of this study is determining the predictive power of the levels of the school readiness in the preschool children by some variables such as social skills, social interaction practices and education status of the parents. The population of the study in the general screening model is composed of 60 to 66-month old children attending to independent preschools registered by the Ministry of National Education in Konya, Turkey. The sample of this study is selected through stratified sampling method among schools, which represent families and their children of three different socio-economic (low, mid and high), in three central districts of Konya. These levels are determined by the Directorate of National Education. Sample group is composed of 42 preschool teachers and 210 children in total, which is sufficient in terms of the number of the participants. Social Skills Test for Preschool-aged Children, Social Interaction Practices for the Preschool Years (SIPPY) Questionnaire and Metropolitan School Readiness Test were employed in the study. Multiple regression analysis and stepwise regression analysis were used for the analyses of the study. By employing linear multiple regression analysis, it was determined that to what extent the variables social skills, social interaction practices, and education status of the parents predict the school readiness level. At the end of the stepwise regression analysis that was utilized to find out whether aforementioned variables have a significant contribution to predict school readiness level, it was found that 10 % of the school readiness level of 60 to 66-month old children attending pre-school education was explained by environmental strategies about social interaction practices applied by preschool teachers, 4% of it was explained by communication skill that is one of the social skills of the children, and 3 % of it was explained by the education status of the father.

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“…The coefficients b 1 and b 2 from ADD represent the partial relationships and the coefficient c 3 from MMR represents the interaction qualifying those two effects (e.g., Kitayama et al, 2015). Some researchers decide the partial relationships are not interesting because they are qualified by an interaction so they only report and discuss c 3 from MMR (e.g., Duffy & Chartrand, 2015) and perhaps at spotlighted (Irwin & McClelland, 2001) or pick-a-point (Hayes, 2013) values of the other variable such as conditional relationship tests for the slopes of different groups (e.g., Chugani, Irwin, & Redden, 2015;Duffy & Chartrand, 2015) or floodlight (Bauer & Curran, 2005;Spiller, Fitzsimons, Lynch, & McClelland, 2013) to test all possible simple effects (e.g., Disatnik & Steinhart, 2015;Martin, Ryan, & Brooks-Gunn, 2010).…”

Section: Interpreting Coefficients In Mmrmentioning

confidence: 99%

“…1. Spiller et al (2013) provide the details of constructing this graph and describe it as a Bfloodlight^illuminating all possible tests of the simple slope of for X for any fixed level of Z (for a use of this graph in psychology, see Martin, Ryan, & Brooks-Gunn, 2010, see also examples in Hayes, 2013). The coefficient for X in the raw MMR equals -0.066 and represents the conditional relationship or equivalently the slope relating X to Y when and only when Z = 0 (the left-most mark on the line in Fig.…”

Section: Mmr-rawmentioning

confidence: 99%

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016)

et al. 2016

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Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.Keywords Moderated multiple regression . Interactions . Multicollinearity . Regression analysis . Tutorial Saunders (1955Saunders ( , 1956) introduced moderated multiple regression (MMR), a simple but very general statistical method for determining whether the relationship between two variables, say Y and X, depends on or is moderated by a third variable Z. The analysis determines whether adding the product XZ to an additive regression model (ADD) containing the components X and Z significantly increases the explained variation or, equivalently, whether the coefficient for the product XZ is statistically significant. Specifically, for these two estimated models . The model is agnostic as to which variable is moderating the other so researchers often simply refer to a significant contribution from the product XZ as an Binteraction.^Interactions form the basis for many psychological theories and data analyses. For example, Riglin et al. (2016), investigated whether the relationship between stress (X) and depressive symptoms (Y) was buffered or moderated by cognitive ability (Z, a continuous variable) and by gender (G, a dichotomous variable).Iacobucci, Schneider, Popovich, and Bakamitsos (2016, this issue, hereinafter ISPB) attempt to sort out various multicollinearity issues in the testing of moderated regression models. Unfortunately their attempt is substantially flawed and is likely to leave many readers confused about how best to test moderated regression models. The reasoning of ISPB

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When fathers’ supportiveness matters most: Maternal and paternal parenting and children’s school readiness.

Cited by 132 publications

References 62 publications

Fathers’ Importance in Adolescents’ Academic Achievement

Fathers’ Importance in Adolescents’ Academic Achievement

Some variables predicting the school readiness of preschool children

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016)

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