Understanding Common Study Results

Foundations ISeptember 28, 2011

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Studies and Evidence Many studies include statistical analysis Though there is a rigorous review process The

following could happen: 1. An inappropriate analysis or results slips thorugh. 2. An approach used is out dated. 3. An claim being made is not-logical.

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Objectives Interpret Confidence Intervals Define dependent vs. independent variables Interpret results from t-tests, chi-square analysis

and regression analysis Differentiate statistical from clinical significance

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Structured Arguments Claim – Statement that the researcher makes Data – The source on which they base this statement Warrant Backing Rebuttal – what are the holes you find Qualifier – Is the confidence interval Toulmin, S. (1958). The Uses of Argument. Cambridge:

Cambridge University Press.

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Obesity & PreschoolersObjective: Our objective was to determine the association between physical activity and BMI among racially diverse low-income preschoolers.

Discussion: This study suggests that, in a diverse group of preschoolers, vigorous and very vigorous activity are associated with lower odds of overweight. However, these findings require corroboration in a diverse sample of preschoolers using a longitudinal design.

Metallinos-Katsars, S.E., Freedson, S. P., Fulton, E.J., & Sherry, B. (2007). The association between an objective measure of physical activity and weight status in preschoolers. Obesity, 15, 686-694.

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Data Always look for descriptive statistics

These will help you determine the sample how much inference you can make based on this data.

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Warranting – Explicating what they did “inclusion criteria for the analyses were: completion of 4.5 days of activity assessment (27) and measurement of height and weight within the following time frame of the activity assessment: 1) children 24 to 35.99 months old: within 90 days, 2) children 36 to 59.99 months old: within 120 days. These time frames were based on the magnitude of growth velocity for the aforementioned ages (28,29). Twenty-three children (27.4%) did not complete at least 4.5 days of activity assessment and three (3.6%) did not have height and weight measured within the specified time frame. Two (2.4%) were excluded because they were 24 months old, precluding the calculation of BMI percentile for age. Thus, the final analytic sample included 56 children(66.7%).”

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When odds are close to 1 that means event is equally likely in both groups

When odds are greater than 1 it means its in favor of the first group

When odds are less than one odds are in favor of the other group

95% confident that the true odds ratio is between XX & XX

p-value 0.05 “The effect being by chance”

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What's dependent what's Independent?

Using general linear model regression and adjusting for age, sex, and race/ethnicity, those classified as overweight had significantly lower mean daily very vigorous minutes (4.6 mins vs. 2.6 mins, p 0.05) and lower mean daily total very active minutes (32.1 min vs. 22.9 mins, p 0.04) than normal-weight children. Adjusting for the time that the monitor was worn per day did not substantially alter the magnitude of the group differences.

-Examined overweight status (dependent) on Activity, Age, Sex, Race- In univariate analysis you will always have one dependent variable, and potentially multiple independent variables the idea is to examine relationship, strength and direction of relationship

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Differences Between Boys & Girls

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Another Way to Check for Differences Chi-Squares Goodness of fit test would help us to

determine if we have approximately a balances sample 50% boys 50% girls if p>0.05 then sample doesn’t differ from assumed

When you want to compare two categorical variables then you need chi-squares test which also produces a p-value eg. Sex & Being Vigorously Active

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IS This Study Important?“Although we hypothesized that children who were more sedentary would have a higher risk of overweight, we did not find significant associations between light activity or even a sedentary pattern of physical activity and overweight. One explanation may be the small sample size of this study, which limited statistical power.”

- Clinical significance VS Statistical significance

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Contact Dr. Saad Chahine

Saad.Chahine@msvu.ca