Test Retest reliability of the ABL and Acusport in measuring resting and exercise blood lactate concentrations

Abstract

In this paper I have tried to Test Retest reliability of the ABL and Acusport in measuring resting and exercise blood lactate concentrations. For this purpose I have carried correlational analysis, Paired Sample T test. The test shows strong correlation among ABL 1 and ABL2. The t value is -.512 i.e. it is less than 2, which also shows that there is no significant relationship. Correlation also shows that there is no significant relationship in between Acusport. Also, the t value is 1.3 i.e. it is less than 2, which also shows that there is no significant relationship.

Introduction

Reliability refers to the consistency of a measure. A test is considered reliable if we get the same result repeatedly. For example, if a test is designed to measure a trait (such as introversion), then each time the test is administered to a subject, the results should be approximately the same. Unfortunately, it is impossible to calculate reliability exactly, but it can be estimated in a number of different ways.

Test-Retest Reliability

To gauge test-retest reliability, the test is administered twice at two different points in time. This kind of reliability is used to assess the consistency of a test across time. This type of reliability assumes that there will be no change in the quality or construct being measured. Test-retest reliability is best used for things that are stable over time, such as intelligence. Generally, reliability will be higher when little time has passed between tests.

Methodology

For testing and retesting of the ABL and Acusport, the methodology I have used is Correlation analysis and Paired sample T test.

Correlation Analysis

Calculations of two-dimensional criteria such relationships are based on the formation of binary values, which are formed from a consideration of dependent samples. If for example we take the data on the level of cholesterol for the first two moments of time from the study of hypertension, in this case we should expect a fairly strong bond: high values in the initial moment of time are a valid reason to wait for large values of and 1 month (Berenson et al 2002).

For a graphical representation of such a link, you can use a rectangular coordinate system with axes, which correspond to both variables. Each pair of values is marked by a certain character. The resulting cluster of points shows that the surveyed patients with high baseline values tend to have high values of cholesterol, and when re-poll in a month. This, of course, is not unexpected; this example was chosen to demonstrate the clear relationship (Sherri 2011).

Statistics shows the correlation between two variables and indicates the strength of the link with a criterion of relationship, called the correlation coefficient. This ratio is always denoted by a Latin letter R, can take values between -1 and +1, and if the value is closer to 1, it indicates a strong connection, and if close to 0, then the ...