Cross-Sectional Versus Longitudinal Studies

Johnston, Francis E.

doi:10.1007/978-1-4684-3252-7_13

Cited by 5 publications

(2 citation statements)

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“…Most population growth studies are crosssectional, often because of the time, expense, and effort associated with longitudinal studies. With sufficiently large samples sizes, cross-sectional data approximate longitudinal growth patterns (Tanner, 1986;Johnston, 1974). Longitudinal patterns, whether inferred from cross-sectional data or measured directly in longitudinal studies, are informative because they indicate the rate of growth, the timing of the adolescent growth spurt, and attainment of adult size.…”

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confidence: 99%

Application of the Preece‐Baines growth model to cross‐sectional data: Problems of validity and interpretation

Zemel

Johnston

1994

American J Hum Biol

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The Preece-Baines growth model (PBGM) is a family of curves that conform to the shape of the human growth curve. It is most often used to analyze longitudinal records on individuals, but it can be applied to cross-sectional data from population surveys. The purpose of this study was to determine empirically the validity of applying the PBGM to cross-sectional data to make inferences about the timing and nature of the adolescent growth spurt. Longitudinal records (n = 339) from the Third Harvard Growth Study were analyzed individually using the PBGM. Results for each sex were pooled to characterize longitudinal growth in this population. Ten independent random cross-sectional samples were generated from the same data set. These were divided by sex and the age means for height were analyzed using the PBGM. Comparisons between the pooled longitudinal estimates and those from the random cross-sectional samples indicated that the PBGM can accurately estimate the age at peak height velocity in males, as well as other characteristics of the adolescent growth spurt when using cross-sectional data. In females, spurious results were obtained when cross-sectional data are used. Possible sources of bias are discussed. © 1994 Wiley-Liss, Inc.

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confidence: 99%

Application of the Preece‐Baines growth model to cross‐sectional data: Problems of validity and interpretation

Zemel

Johnston

1994

American J Hum Biol

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“…In longitudinal studies, in which changes are emphasized, random errors are not crucial, since the difference between measurements is the parameter of interest (Johnston, 1975).…”

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confidence: 99%