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Lecture

Kinesiology 3347A/B Lecture Notes - Sampling Error, Observational Error, Repeated Measures Design


Department
Kinesiology
Course Code
Kinesiology 3347A/B
Professor
Navy

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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

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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

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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
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