scholarly journals The influence of input on connective acquisition: a growth curve analysis of English because and German weil

2012 ◽  
Vol 40 (5) ◽  
pp. 1003-1031 ◽  
Author(s):  
ROSIE VAN VEEN ◽  
JACQUELINE EVERS-VERMEUL ◽  
TED SANDERS ◽  
HUUB VAN DEN BERGH
Keyword(s):  
Growth Curve ◽  
Great Part ◽  
Growth Curves ◽  
Curve Analysis ◽  
Growth Curve Analysis ◽  
Age Range

ABSTRACTThe current study used growth curve analysis to study the role of input during the acquisition of the English causal connective because and its German counterpart weil. The corpora of five German and five English children and their adult caretakers (age range 0;10–4;3) were analyzed for the amount as well as for the type of connective use – imitated, elicited, and independent. The growth curves showed that children's elicited use developed faster than their independent use; imitations were rare. Adult connective input was not found to function as a scaffold of children's connective use. Rather, the adult why/warum-questions played an important role in the acquisition of because and weil. In turn, children also used why/warum-questions to elicit causal responses from their caretakers, which shows that children were responsible for a great part of their own input.

scholarly journals Hypothesized, Directly-Coded Curve Shapes in Growth Curve Analysis: An Example

2013 ◽  
Vol 3 (2) ◽  
pp. 13 ◽  
Author(s):  
Patricia M. Herman ◽  
Lee Sechrest
Keyword(s):  
Hierarchical Linear Modeling ◽  
Growth Curve ◽  
Degrees Of Freedom ◽  
Growth Curves ◽  
Curve Analysis ◽  
Small Samples ◽  
Growth Curve Analysis ◽  
Linear Modeling ◽  
Individual Level ◽  
The Individual

Growth curve analysis provides important informational benefits regarding intervention outcomes over time. Rarely, however, should outcome trajectories be assumed to be linear. Instead, both the shape and the slope of the growth curve can be estimated. Non-linear growth curves are usually modeled by including either higher-order time variables or orthogonal polynomial contrast codes. Each has limitations (multicollinearity with the first, a lack of coefficient interpretability with the second, and a loss of degrees of freedom with both) and neither encourages direct testing of alternative hypothesized curve shapes. Especially in studies with relatively small samples it is likely to be useful to preserve as much information as possible at the individual level. This article presents a step-by-step example of the use and testing of hypothesized curve shapes in the estimation of growth curves using hierarchical linear modeling for a small intervention study. DOI:10.2458/azu_jmmss_v3i2_herman

A Growth Curve Analysis of Self-Esteem and Depressive Symptomatology Throughout Emerging Adulthood: The Role of Family

2019 ◽  
pp. 216769681881328 ◽  
Author(s):  
Kayla Reed-Fitzke ◽  
Mathew C. Withers ◽  
Anthony J. Ferraro ◽  
Mallory Lucier-Greer ◽  
James M. Duncan
Keyword(s):  
Emerging Adulthood ◽  
Growth Curve ◽  
Depressive Symptomatology ◽  
Self Esteem ◽  
Curve Analysis ◽  
Growth Curve Analysis

scholarly journals Hypothesized, Directly-Coded Curve Shapes in Growth Curve Analysis: An Example

2013 ◽  
Vol 3 (2) ◽  
pp. 13
Author(s):  
Patricia M. Herman ◽  
Lee Sechrest
Keyword(s):  
Hierarchical Linear Modeling ◽  
Growth Curve ◽  
Degrees Of Freedom ◽  
Growth Curves ◽  
Curve Analysis ◽  
Small Samples ◽  
Growth Curve Analysis ◽  
Linear Modeling ◽  
Individual Level ◽  
The Individual

Growth curve analysis provides important informational benefits regarding intervention outcomes over time. Rarely, however, should outcome trajectories be assumed to be linear. Instead, both the shape and the slope of the growth curve can be estimated. Non-linear growth curves are usually modeled by including either higher-order time variables or orthogonal polynomial contrast codes. Each has limitations (multicollinearity with the first, a lack of coefficient interpretability with the second, and a loss of degrees of freedom with both) and neither encourages direct testing of alternative hypothesized curve shapes. Especially in studies with relatively small samples it is likely to be useful to preserve as much information as possible at the individual level. This article presents a step-by-step example of the use and testing of hypothesized curve shapes in the estimation of growth curves using hierarchical linear modeling for a small intervention study. DOI:10.2458/azu_jmmss_v3i2_herman

scholarly journals Do Trajectories of At-Home Dementia Caregiving Account for Burden After Nursing Home Placement? A Growth Curve Analysis

2010 ◽  
Vol 49 (8) ◽  
pp. 734-752 ◽  
Author(s):  
Katherina A. Nikzad-Terhune ◽  
Keith A. Anderson ◽  
Robert Newcomer ◽  
Joseph E. Gaugler
Keyword(s):  
Nursing Home ◽  
Growth Curve ◽  
Curve Analysis ◽  
Nursing Home Placement ◽  
Growth Curve Analysis ◽  
Dementia Caregiving ◽  
Home Placement ◽  
At Home

Cognitive Development of Lead Exposed Children from Ages 6 to 15 Years: An Application of Growth Curve Analysis

2003 ◽  
Vol 9 (1) ◽  
pp. 10-21 ◽  
Author(s):  
Juliet M. Coscia ◽  
M. Douglas Ris ◽  
Paul A. Succop ◽  
Kim N. Dietrich
Keyword(s):  
Cognitive Development ◽  
Growth Curve ◽  
Curve Analysis ◽  
Growth Curve Analysis
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