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Unit 2.docx

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Department
Kinesiology
Course
Kinesiology 3347A/B
Professor
Navy
Semester
Winter

Description
1 Unit 2: The Study, Measurement and Analysis of Growth Chapter 1 page 8-12; 14-18 Introduction Do we understand growth? - Door frame measures vs. comparisons to standardized data  Should make comparisons to relevant group Common Data Presentation: o Growth curves o Tables o Ordered chronologically o Meta-analyses Use of Chronological Age: 5 5.5 6 6.5 7 Average Age = 6.0 Average Age = 6.5 - Different studies had different ranges around average age - A lot of error - Variability exists as a result of how data was compiled – what age are we really looking at, could be a larger age group than we think Terminology Status: o Attained size (height/weight) o Level of maturity o Level of physical fitness or performance 2 o Nutritional status o Individual (in comparison to a relevant group) o Group (in comparison to a larger relevant group)  Not useful if nothing to compare to (no groups) o How an infant compares to his/her relevant group  Similar age, sex, demographic Progress: o Given several measures of status (over a period of time, can calculate a rate) o Can refer to rate  Growth  Maturation Prediction: o Calculating adult stature o Use of status measures and progress calculations o Can make predictions about children Tracking: o Stability of a characteristic o Stability of an individual’s rank o Determine useful indicators  E.g., BMI o Multiple measures over time needed o Tracking estimates:  Initial age  Time interval (between measurements)  Measurement variability  Equipment variability  Accuracy and precision  Environmental factors 3 Comparison: o Reference data  “Standards” – not changing, but really they are  “Norms” – doesn’t exist anymore  Don’t use these words o Group relevance  General samples  E.g., Same sex  Case-specific samples  Exposure to similar environmental factors, diseases, etc. Types of Observational Studies o Cross-Sectional  Measured only once  Snapshot of large group at one point in time o Longitudinal  Monitor over time at intervals  See what happens to a participant over time o Mixed-Longitudinal Longitudinal Study: o One cohort (usually a chronological age cohort) o Multiple measures o Defined intervals o Constant = cohort characteristic (usually age) o Variable = time  Other variables ... height, weight, etc.  Time is primary cause of changes seen or measured o GRAPH: Performance curve for adults Criteria for Longitudinal Design: 1. Changes must be observed over a sufficient period of time 2. Multiple observations 3. Measurement of change must be made leading up to the change (before the change occurs) 4. All observations must be made on same subjects 4 Longitudinal Design Strategies: 1. Repeated measures  Same participant, whole subject pool  People likely to drop out  Individual growth curves 2. Independent measures  ‘Panel study’  Random sample selected from appropriate cohort  Easier (can randomly select people from cohort at each interval 3. Retrospective  Old surveys, medical records to piece together a timeline of measurements and events  Weakest Longitudinal Study Advantages: o Status and rate (progress) o Individual trends o Economizes on subjects o Subjects serve as own control group o Between-subject variation excluded from error o Can identify more efficient estimates than cross-sectional designs with same number and pattern of observations o Can distinguish aging effects (changes over time within individuals) from cohort effects (differences between individuals over time) o Sensitive to long-term changes in individuals (cross-sectional studies are not) Disadvantages: o Difficult to secure participants (very long, considerable time commitment) o Selective and non-participation o Mortality and mobility o Attrition o Small n → generalizability o Time consuming – time for findings o Expensive o Difficult to conduct  Staff  Methods  Introduce more errors 5 o Survivor bias  Remaining participants tend to be of a type (certain personality characteristic)  Motivation inflates performance measures o Test effects  Causes:  Repeated measures – bias  Familiarity with experimenters  Results:  Skill  Motivation  Learning  Improvements in score due to technique and practice  Habituation  Adaptive change in participant, scores improve o T
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