test-retest

THE TEST-RETEST RELIABILITY METHOD IS ONE OF THE SIMPLEST WAYS OF TESTING THE STABILITY AND RELIABILITY OF AN INSTRUMENT OVER TIME.

 If a group of students takes a test, you would expect them to show very similar results if they take the same test a few months later. This definition relies upon there being no cofounding factor during the intervening time interval.

Instruments such as IQ tests and surveys are prime candidates for test-retest methodology
because there is little chance of people experiencing a sudden jump in IQ or suddenly changing their opinions.

Educational tests are often not suitable, because students will learn much more information over the intervening period and show better results in the second test.

 

Test-Retest Reliability and the Ravages of Time

 

For example

 if a group of students take a science  test just before the end of semester and one when they return to school at the beginning of the next, the tests should produce broadly the same results.

 

The intervening lessons are assumed have improved the ability of the students after the test and retest are taken at the beginning and at the end of the semester.. Thus, test-retest reliability will be compromised and other methods, such as split testing are better.

 

It was possible that the subjects will remember some of the questions from the previous test, hence perform better. However, there will always be some degree of error while answering the questions for the second time. apart from that, some participants might not answer the first test seriously or lack of understanding and for these reasons, retakes of test by same subjects will make them aware to be more well-prepared to answer the questions.

 

For instant, the first test or tasks should not be influenced by the internal factors or external factors like economic income and etcetera. For example, people may have been asked about their favourite type of bread. At first, most of the participants prefer to have Gardenia bread but as the price of the bread become more expensive over time, participants will change their mind and perhaps choose another bread as their favourite.

Test-Retest Reliability and Confounding Factors

To give an element of quantification to the test-retest reliability,statisticaltest factor this into the analysis and generate a number between zero and one, with 1 being a perfect correlation between the test and the retest.

Perfection is impossible and most researchers accept a lower level, either 0.7, 0.8 or 0.9, depending upon the particular field of research.

However, this cannot remove confounding factors  completely, and a researcher must anticipate and address these during the research design to maintain test-retest reliability.

To dampen down the chances of a few subjects skewing the results, for whatever reason, the test for  correlation  is much more accurate with large  subject groups, drowning out the extremes and providing a more accurate result.

 

Pearson's Correlation Coefficient, r

Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables

 

 

step to plot graph

 

1.Draw a scatter plot of the variables to check for linearity .(The correlation coefficient should not be calculated if the relationship is not linear )

 

2.Conventionally, the independent (or explanatory) variable is plotted on the x-axis

(horizontally)

 

3.The dependent (or response) variable is plotted on the y-axis (vertically).

Values of Pearson's correlation coefficient

Pearson's correlation coefficient (r) for continuous (interval level) data ranges from -1 to +1:

r = -1		data lie on a perfect straight line with a negative slope
r = 0		no linear relationship between the variables
r = +1		data lie on a perfect straight line with a positive slope

Positive correlation indicates that both variables increase or decrease together, whereas negative correlation indicates that as one variable increases, so the other decreases, and vice versa.

Worked example

Nine students held their breath, once after breathing normally and relaxing for one minute, and once after hyperventilating for one minute. The table indicates how long (in sec) they were able to hold their breath. Is there an association between the two variables?

 

Subject	A	B	C	D	E	F	G	H	I
Normal	56	56	65	65	50	25	87	44	35
Hypervent	87	91	85	91	75	28	122	66	58

The chart shows the scatter plot (drawn in MS Excel) of the data, indicating the reasonableness of assuming a linear association between the variables.Hyperventilating times are considered to be the dependent variable, so are plotted on the vertical axis.