Tukey hsd post hoc test excel

Practical Connection Assignment

At UC, it is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where you will demonstrate how this course research has connected and put into practice within your own career.

Assignment:
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied, or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.

Requirements:
Provide a 500 word (or 2 pages double spaced) minimum reflection.
Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.
Share a personal connection that identifies specific knowledge and theories from this course.
Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.
You should NOT, provide an overview of the assignments assigned in the course. The assignment asks that you reflect how the knowledge and skills obtained through meeting course objectives were applied or could be applied in the workplace.

Review:

t-tests

Practice #1: Independent samples t-test

Does wind or solar energy cost less for factories?

Practice #1 Answer

Wind energy (M= 474.53) was significantly less expensive than solar energy (M= 654.55) to run a factory, (t [20] = 1.9, p < .05).

Practice #2: Paired Samples t-test

Practice #2: Answer

There was a significant difference in employee participation after the seminar (M = 39.8) when compared to the participation pre seminar (M = 38.4), (t [9] = 2.50, p < .05).

ANOVA

(Analysis of Variance)

Statistical Tests:

In general, it is always important to remember the questions that are asked in any study you perform. Virtually everything you do from writing the research design to writing the study’s implications is dictated by the research questions. In our previous lesson, we looked at differences between two groups, but what if you have more than two groups? The ANOVA will tell us if there is a significant difference between three or more groups-however it will not tell us where. We will use post-hoc testing to determine where the significant difference occurs.

7

Analysis of Variance

The ANOVA

We are going to focus on chapter 11, one-way classification (one way means one independent variable).

Null hypothesis= all populations are the same. If p < .05, we can reject the null and need further analysis. This means there is a difference, we can use an independent sample t-test to determine the differences.

Excel: ANOVA Single Factor

Spatz: Table F = F Distribution

UC Handbook: p. 23-24

An example from a dissertation:

Small Group Reading Results for Ability Groups

An ANOVA test was used to compare changes in MAP scores among low, average, and high achieving students in the experimental group. Data in Table 3 indicated that there was a significant difference between academic achievements among the groups. The results of the ANOVA show that small group reading instruction produced higher academic outcomes in low achieving students (M = 23.27) when compared to average achieving students (M = 17.88), and high achieving students (M=16.15), (F [2, 109] = 3.89, p < .05).

Her data in excel:

See p < .05

(F [2, 109] = 3.89, p < .05)

F Distribution Table

(F [2, 109] = 3.89, p < .05)

Even though, the excel stats pack gives you the significance, you can also check in the Spatz book, table F. The F statistic is larger than the top value (.05), but not the bold value (.01). The same as we saw in our excel data.

Note on the dissertation results:

Since the results indicated there was a difference in scores, the researcher completed three independent sample t-tests to see where the differences occurred.

FYI: Statistically significant differences were revealed between the low and average achieving students (t [92] = 1.98, p < 0.05), and between the low and high achieving students (t [35] = 2.03, p < 0.05).

Let’s try an ANOVA

A UC doctorate student wanted to determine if there were differences in knowledge of dyslexia among Kentucky Educators. She piloted a survey, and then once reliability was determined, administered the survey to four groups.

Select an ANOVA

Enter Data in columns.

2. Select Data tab.

Select Data analysis.

Select Anova: Single Factor.

Click OK.

Next steps:

6. Input Range: Highlight all data.

7. Grouped by: select columns.

8. Click “Labels in first row.”

9. Leave alpha at 0.05.

10.Select an area for Output Range.

11. Click OK.

The results:

Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Elementary 43 2563 59.60465 307.1495
Middle 40 2538 63.45 149.0231
Special Education 32 2237 69.90625 132.6038
Guidance 31 1865 60.16129 219.7398


ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 2279.737 3 759.9125 3.668442 0.013862 2.668337
Within Groups 29415.09 142 207.1485

Total 31694.83 145
(f [3, 142] = 3.67= p < .05)

How many post-hoc tests are needed?

ANOVA:

Post-hoc testing

ANOVA: Post-hoc tests

The ANOVA is used to determine a difference in groups, but when examining three or more groups, it does not indicate where the differences occur.

Always defer to your dissertation advisor or editor on selection of post-hoc tests.

Let’s look at an example.

Post-hoc test examples:

Independent samples t-test

Tukey’s HSD

Bonferroni

Scheffe

Example:

ANOVA results

There was a significant difference between training and degree (M=91.8), training only (M = 74.67), and degree only (M=71) employee performance, (f [2,23] = 13.22, p < .001). Post-hoc tests are needed.

Excel: Independent Samples t-tests

Step 1: Select the independent samples t-test (two sample assuming equal variances). Examine columns A and B.

Step 2

Step 2: Select the independent samples t-test (two sample assuming equal variances). Examine columns A and C.

Step 3

Step 3: Select the independent samples t-test (two sample assuming equal variances). Examine columns B and C.

Post-hoc results:

There was a significant difference between training and degree (M = 91.8) and training only employees (M = 74.67), (t [17] = 5.84, p < .001).

There was a significant difference between training and degree (M = 91.8) and degree only employees (M = 71), (t [15] = 4.31, p < .001).

There was no significant difference between training only (M = 74.67) and degree only employees (M = 71), (t [14] = 0.66, p > .05).

SPSS

The Tukey HSD, Scheffe, and Bonferroni all indicate the same differences (significant differences between training and degree-training only, and training and degree-degree only.