Question

5). On the next page is a set of two SPSS simple effects tests using the split file function. After looking at the charts, I interpret it this way:

“Following the significant original interaction, F(1, 116) = 14.09, p < .001, follow-up simple effects tests showed significance for the FITD condition, F(1, 58) = 2.19, p < .05, with participants showing less willingness to participate in the study in the hour long study when the model was present (M = 7.39, SD = 1.16) than when the model was absent (M = 7.84, SD = 0.96). However, the simple effects test did not show significance for No FITD condition, F(1, 58) = 12.70, p < .05. Thus participants showed a similar willingness to participate in the hour long study when the model was either present (M = 7.53, SD = 0.96) or absent (M = 6.02, SD = 1.71).”

Is this a correct interpretation of those simple effects tests? Why or why not?

A. Yes, it is a correct interpretation in its entirety (there was a significant difference between model present and absent in the FITD condition but no significant difference between model present and absent in the No FITD condition)

B. It is partially correct, as both simple effects were actually significant (there was a significant difference between model present and model absent in the FITD condition and there was a significant difference between model present and model absent in the No FITD condition)

C. It is partially incorrect, since neither simple effect were significant (there was no significant difference between model present and model absent in the FITD condition and there was no significant difference between model present and model absent in the No FITD condition)

D. The interpretation is backwards (there was no significant difference between having a model present versus absent in the FITD condition, but there was a significant difference between having the model present versus absent in the No FITD condition)

E. There is not enough information in the tables to determine whether this interpretation is correct.

Answer 1

A) The interaction of Model X Condition was not significant, F(1, 116) = 14.09, p > .05. Since this is not significant, there was no need to run simple effects follow-up tests.

This Statement is False. Because at F(1, 116) = 14.09, p < 0.05. Since this is significant, there was need to run simple effects follow-up tests.

B) The interaction of Model X Condition was not significant, F(1, 116) = 24.78, p > .001. Since this is not significant, there was no need to run simple effects follow-up tests.

This Statement is False. Because at F(1, 116) = 24.78, p < 0.001. Since this is significant, there was need to run simple effects follow-up tests.

C) The interaction of Model X Condition was significant, F(1, 116) = 14.09, p < .001. Since this is significant, we would need to run four simple effects tests.

A). We would look at the model present condition only (to see if FITD differs from No FITD).

B). We would look at the model absent condition only (to see if FITD differs from No FITD).

C). We would look at the FITD condition only (to see if model present differs from model absent).

D). Finally, we would look at the No FITD condition only (to see if model present differs from model absent).

This Statement is True. Because at F(1, 116) = 14.09, p < 0.001. Since this is significant, there was need to run simple effects follow-up tests.

D) The interaction of Model X Condition was significant, F(1, 116) = 14.09, p < .001. Since this is significant, we would need to run two simple effects tests.

A). We would look at the model present only (to see if FITD differs from No FITD).

B). We would look at the model absent only (to see if FITD differs from No FITD).

This Statement is True. Because at F(1, 116) = 14.09, p < 0.001. Since this is significant, there was need to run simple effects follow-up tests.

E) The interaction of Model X Condition was significant, F(1, 120) = 14.09, p < .001. Since this is significant, we would need to run four simple effects tests.

A). We would look at the model present condition only (to see if FITD differs from No FITD).

B). We would look at the model absent condition only (to see if FITD differs from No FITD).

C). We would look at the FITD condition only (to see if model present differs from model absent).

D). Finally, we would look at the No FITD condition only (to see if model present differs from model absent).

This Statement is True. Because at F(1, 120) = 14.09, p < 0.001. Since this is significant, there was need to run simple effects follow-up tests.