# Importance of team height to winning games in the National Basketball Associatio

Teramoto, M., & Cross, C. L. (2017). Importance of team height to winning games in the National Basketball Association. International Journal of Sports Science & Coaching, 13(4), 559–568. https://doi.org/10.1177/1747954117730953

1.
Describe the main reason for the study.

Normally the
topic sentence

2.
What type of study is it? (pick one)

a.       Experiment
(assigned
treatments)

b.       Observational
(observed, no
treatments)

3.
Identify the Explanatory and Response Variables

a.
Label each as quantitative (numerical) or
qualitative (categorical)

numerical
= a number (anything that can have decimals or it makes sense to take an
average of)

categorical
= groups (groupings that do not have an order to them (colors, majors, etc…))

Explanatory:

Treatments or
the thing they think is responsible for the change

Response:

What is being measured in
the study

4.
Describe the population (who can the results be
extended out to?)

Look
for inclusion and exclusion criteria.

5.
What type of sampling was used?

A
lot of volunteer sampling occurs, especially with medical studies. Occasionally
there is more than one scheme in a study.

6.
Are there any potential sources of bias in the
sample?

Think
about groups that were excluded either accidentally or on purpose. Hint:
Volunteer samples are ALWAYS biased.

7.
Was the study prospective or retrospective?

Prospective =
future – got the participants and then measured the data (record what you’ll
eat for breakfast tomorrow)

Retrospective
= past – data point already occurred then put the participant in the study
(what did you eat for breakfast yesterday)

8.
Were the subjects randomized to treatments? If
so, how?

Only
applies if it is an experiment. Look for key words like blocks or simple random
sample

9.
What results are presented? (only list a few for
each category)

a.
Descriptive Statistics

Summaries of the data
including: means, medians, standard deviations, ranges, proportions

b.
Inferential Statistics

Hypothesis
tests, “p-value”, confidence intervals, chi-square, z-test, proportion test,
ANOVA.

10.
Are there any Confounding or lurking variables
that could impact the results?

Confounding =
measured in the study. Different level for treatment groups, impacts results

Lurking
variable = not measured in the study. Different level for treatment groups,
impacts results.

Favorite
example is socioeconomic status. Impacts where you live, what resources you
have available, and general lifestyle.

11.
What were the final conclusions?

Based on the explanatory
and response variables, what are the findings? Did it works, under which
conditions?

12.
What aspects of the study could be improved or
included for next time?

Opinion
question. Often discussed by the author in the limitations section of the
paper.

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