Brown (1991) found that a measure of aerobic fitness...

Name: Problem 3, Chapter 7: Research Design Explained
Uploaded: 2021-07-27T20:26:13-08:00
Duration: 5 min 29 s
Channel: Angela Chemidlin
Description: Brown (1991) found that a measure of aerobic fitness correlated +.28 with a self-report measure of how much people exercised. He also found that the measure of aerobic fitness correlated -.41 with resting heart rate. Is resting heart rate or self-report of exercise more closely related to the aerobic fitness measure?

Brown (1991) found that a measure of aerobic fitness correlated +.28 with a self-report measure of how much people exercised. He also found that the measure of aerobic fitness correlated -.41 with resting heart rate. Is resting heart rate or self-report of exercise more closely related to the aerobic fitness measure?

Brown (1991) found that a measure of aerobic fitness correlated +.28 with a self-report measure of how much people
exercised. He also found that the measure of aerobic fitness correlated -.41 with resting heart rate. Is resting heart rate or self-report of exercise more closely related to the aerobic fitness measure?

Key Concepts

Correlation Coefficient

A correlation coefficient is a statistical measure that describes the strength and direction of the relationship between two variables. Its absolute value indicates the strength of the association, while the sign shows whether the relationship is positive or negative. This concept is key in understanding how closely related two measures are.

Measurement Validity

Measurement validity refers to how accurately a method measures what it is intended to assess. In exercise science, comparing a direct physiological measure (such as resting heart rate) with a self-report measure highlights differences in accuracy and reliability. Objective physiological measures are often considered more valid because they are less influenced by personal bias than self-reports.

Transcript

00:02 To start this problem, the first step we need to do is we need to do something that is called a t -test or a t -statistic.

00:10 That's what we're finding, the t -statistic.

00:13 The reason why is because in order to find the p -value or the significance of the test, we need to have this number.

00:20 So that's where we're going to start.

00:23 So to do a t -test, there's several different equations of how to get there.

00:28 I'm going to write both down so you can use whichever one you want.

00:32 But we're only going to use one.

00:34 So this is one option right here.

00:39 It's more of a straight forward option because you just kind of get t as your answer.

00:46 Remember this is the e right here, the symbol and this symbol looks like an e, represents the sum like this and then this is over...

00:59 That's a bad line.

01:01 Wait a second.

01:03 There we go.

01:05 That's better.

01:07 This is just a little little bit bulky for my liking so i don't really do this one as much but this is one option like i was saying another way that this can be performed is how we're going to do it or how i'm going to do it so i'm going to bar x1 minus x bar 2 over square root and remember this symbol is the standard deviation for a population over n1 plus this continues right here.

01:47 It's going to do deviations, swear it again, but for the second, the second one over n2.

01:55 So this is what we're gonna use right now.

01:57 And when we use that, we know that are numbers, right? the given numbers we have for regular exercise, i'll draw it over here just so it's easier to see.

02:08 This is for regular exercise.

02:13 The n we had was 40.

02:18 For none, i'll just not, none for no regular exercise, was 30.

02:24 This is bar x.

02:26 This is going to be, we're going to represent regular exercise as bar x1 at 63...

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