A researcher conducts a t test for dependent means in which it is predicted that there will be a decrease in unemployment from before to after a particular job-skills training program. The cutoff "t" needed is -1.8333. The standard deviation of the distribution of means of change scores is 2.0 and the mean change score for the sample studied is an increase of 5.2.

What is the appropriate conclusion?

To determine the appropriate conclusion for the researcher's t test, we need to compare the obtained t-value to the cutoff t-value and consider the direction of the predicted effect.

Given that the cutoff t-value needed is -1.8333, and the obtained t-value is not provided, we can calculate it using the formula:

t = (M - μ) / (s / √n)

Where:
M = mean change score for the sample studied = 5.2 (increase)
μ = population mean change score (which would be 0 in this case since the predicted effect is a decrease)
s = standard deviation of the distribution of means of change scores = 2.0
n = sample size (which is not given)

Assuming we have the sample size value, we can substitute the given values into the formula to calculate the obtained t-value. Let's say the sample size is 30:

t = (5.2 - 0) / (2.0 / √30)

Simplifying the equation, we get:

t = 5.2 / (2.0 / √30) ≈ 7.339

Now we can compare the obtained t-value of 7.339 to the cutoff t-value of -1.8333. Since the obtained t-value is positive and much larger than the negative cutoff t-value, we can conclude that there is a significant increase in unemployment from before to after the job-skills training program.

In other words, the results of the study indicate a substantial positive change, contrary to the predicted decrease. It suggests that the training program might have had unintended consequences or limitations in achieving its initial goal. Further analysis and investigation are recommended to understand the reasons behind this unexpected outcome.