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Introduction to the Independent-Measures Design - Coggle Diagram
Introduction to the Independent-Measures Design
The first research strategy, using completely separate groups, is called an
independent-measures research design or a between-subjects design
: involves separate and independent samples and makes a comparison between two groups of individuals
The second research strategy, in which the two sets of data are obtained from the same group of participants, is called a
repeated-measures research design or a within-subjects design.
**The Hypotheses for an Independent-Measures Test
null hypothesis states that there is no change, no effect, or, in this case, no difference
In a hypothesis test,
the null hypothesis sets the population mean difference equal to zero, so the independent measures t formula
The independent-measures t statistic uses
the data from two separate samples to help decide whether there is a significant mean difference between two populations or between two treatment conditions.
The independent-measures t
uses the difference between two sample means to evaluate a hypothesis about the difference between two population means. Thus, the independent-measures t.
Calculating the Estimated Sandard error:there are two sources of error.
Each sample mean is not necessarily exactly equal to the population mean from which the sample was selected because of error. The amount of error associated with each sample mean is measured by the estimated standard error
independent-measures t statistic
, we want to know the total amount of error involved in using two sample means to approximate two population means.
The Variability of Difference Scores
independent-measures t statistic adds together the two sample errors when it subtracts to find the difference between the two sample means.
Pooled**
Variance
correcting the bias in the standard error, is to combine the two sample variances into a single value
,The pooled variance is obtained by averaging or “pooling” the two sample variances using a procedure that allows the bigger sample to carry more weight in determining the final value
Estimated Standard Error
:Using the pooled variance in place of the individual sample variances, we can now obtain an unbiased measure of the standard error for a sample mean difference. The resulting formula for the independent-measures estimated standard error.
Assumptions Underlying the Independent-Measures t Formula
2 more items...
**The Estimated Standard Erro
r
the standard error in the denominator measures how much error is expected between the sample statistic and the population parameter.
In the
single-sample t
formula, the standard error measures the amount of error expected for a sample mean
Samples are not expected to be perfectly accurate, and the
standard error measures
how much difference is reasonable to expect between a sample statistic and the population parameter.
Interpreting the Estimated Standard Error
M1-M2appears in the bottom of the independent-measures t statistic can be interpreted in two ways
An
independent-measures
study comparing two treatment conditions uses 2 groups of participants and obtains 1 score(s) for each participant.