A psychologist at a private mental health facility was asked to determine whether there was a clear difference in the length of stay for patients with different categories of diagnosis. Looking at the last seven patients in each of the three major categories, the results (in terms of weeks of stay) were as follows. Using the data below, test whether the length of stay for patients vary based on category of diagnosis. Diagnosis Category Affective Disorders Cognitive Disorders Drug-related Conditions 7 12 8 6 8 10 5 9 12 6 11 10 9 11 9 5 10 11 6 9 12 The above scenario requires a one-way Anova, with a bar graph. Question - I need to write an explanation for the output given. I am to include a statistical notation and explanation as to whether the results are significant or not significant. Examples of Explanation: Non-significant: A One-Way ANOVA was conducted to examine whether a preceding situation (watching a video of helping behavior, seeing first-hand someone help another person, or a neutral control condition) influenced the number of helping behaviors expressed by people. The null hypothesis failed to be rejected F (2, 21) = 4.993, p = .35, Ƞ2= .05. There is no difference between the control, video, and live conditions in number of helping behaviors shown. Significant: A One-Way ANOVA was conducted to examine whether a preceding situation (watching a video of helping behavior, seeing first-hand someone help another person, or a neutral control condition) influenced the number of helping behaviors expressed by people. The null hypothesis was rejected - there is a difference between the control, video, and live conditions in number of helping behaviors F (2, 21) = 4.993, p = .017, Ƞ2 = .3222. Tukey post hoc analyses revealed that seeing someone first-hand help another person resulted in significantly more helpful behaviors than being in the neutral control condition (p = .013; see Figure 1). No other post hoc comparisons were significant (p > .05)
A psychologist at a private mental health facility was asked to determine whether there was a clear difference in the length of stay for patients with different categories of diagnosis. Looking at the last seven patients in each of the three major categories, the results (in terms of weeks of stay) were as follows. Using the data below, test whether the length of stay for patients vary based on category of diagnosis.
|
Diagnosis Category |
||
|
Affective Disorders |
Cognitive Disorders |
Drug-related Conditions |
|
7 |
12 |
8 |
|
6 |
8 |
10 |
|
5 |
9 |
12 |
|
6 |
11 |
10 |
|
9 |
11 |
9 |
|
5 |
10 |
11 |
|
6 |
9 |
12 |
The above scenario requires a one-way Anova, with a bar graph.
Question - I need to write an explanation for the output given. I am to include a statistical notation and explanation as to whether the results are significant or not significant.
Examples of Explanation:
Non-significant:
A One-Way ANOVA was conducted to examine whether a preceding situation (watching a video of helping behavior, seeing first-hand someone help another person, or a neutral control condition) influenced the number of helping behaviors expressed by people. The null hypothesis failed to be rejected F (2, 21) = 4.993, p = .35, Ƞ2= .05. There is no difference between the control, video, and live conditions in number of helping behaviors shown.
Significant:
A One-Way ANOVA was conducted to examine whether a preceding situation (watching a video of helping behavior, seeing first-hand someone help another person, or a neutral control condition) influenced the number of helping behaviors expressed by people. The null hypothesis was rejected - there is a difference between the control, video, and live conditions in number of helping behaviors F (2, 21) = 4.993, p = .017, Ƞ2 = .3222. Tukey post hoc analyses revealed that seeing someone first-hand help another person resulted in significantly more helpful behaviors than being in the neutral control condition (p = .013; see Figure 1). No other post hoc comparisons were significant (p > .05).
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