1. Experimental Design

2. Objectives Assignments and quizzes review
Be familiar with the elements of experimental design
Differentiate between treatment and control
Differentiate between independent and dependent variables
Hypothesis testing and statistical analysis

3. Assignments and quizzes review
Lit Review is graded
Cover letter is graded
Survey is due today
Methodology Due in two weeks
Week 8 Quiz: open today for a week

4. Method chapter
Strat with an introductory paragraph to present the purpose and objectives of the study.
Study Sample:
Demographic: Age, gender, etc.
Inclusion-exclusion criterion
Data collection: method and procedure
Data analysis:
Descriptive
Inferential

5. Experiment
An experiment deliberately imposes a treatment on a group of objects or subjects in the interest of observing the response.
Differs from an observational study, which involves collecting and analyzing data without changing existing conditions

6. Experimental Controls
The control group is practically identical to the treatment group, except for the single variable of interest whose effect is being tested, which is only applied to the treatment group.
An example would be a drug trial.
The group receiving the drug would be the treatment group and the one receiving the placebo would be the control group.

7. Treatment Groups
In experiments, a treatment is something that researchers administer to experimental units .
The treatment groups are the groups of subjects that received a particular treatment
For example, in a drug test, three different groups of subjects received three different types of drugs
The treatment is the administration of a particular drug type

8. Experimental Design
The proper organization of the experiment ensures that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible.
This process is called experimental design.
Because the validity of a experiment is directly affected by its construction and execution, attention to experimental design is extremely important

9. Factors
In an experimental design, a factor in an experiment is a controlled independent variable
A variable whose levels are set by the experimenter
A factor consists of categories of treatments
Remember: Factors are independent variables
From a statistical standpoint,
the researcher looks for differences in the averages of the dependent variable(s) across the groups of independent variables

10. Experimental Bias
When researchers fail to control for the effects of the differences in subjects, it can lead to experimental bias
Experimental bias is the favoring of certain outcomes over others

11. Randomization
Because it is generally extremely difficult for experimenters to eliminate bias using only their expert judgment, the use of randomization in experiments is common practice.
In a randomized experimental design, objects or individuals are randomly assigned (by chance) to an experimental group.

12. Replication
To improve the significance of an experimental result, replication, the repetition of an experiment on a large group of subjects, is required.
If a treatment is truly effective, the long-term averaging effect of replication will reflect its experimental worth.
If it is not effective, then the few members of the experimental population who may have reacted to the treatment will be negated by the large numbers of subjects who were unaffected by it.

13. Formats Experimental Designs are defined by:
their formats
Examples of these formats include:
Student t-test- two groups
One-way Analysis of Variance
Multivariate Analysis of Variance
Factorial Analysis of Variance
Correlation
Regression

14. Hypothesis Testing Null and Alternative hypotheses
Choose appropriate statistical test
Select Alpha Level (.o5)
Run your statistical test
Compare your test result
Make a decision (reject/do not reject Null)
Draw conclusions

15. Comparing Two Means The alternative hypothesis is:
A researcher wishes to determine if there two means are significantly different from each other.
The null hypothesis is:
There is no significant difference between the average number of lost work days experienced by males and females.
The alternative hypothesis is:
There is a significant difference between the average number of lost work days experienced by males and females.

16. Data The researcher collected the data using a survey.
One question on the survey asked the person’s gender
Another question asked the person how many lost workdays they experienced due to a MSD over the past year.
There were 15 respondents, 10 males and 5 females

17. Data Table Case Females Males 1 2 3 4 5 6 7 8 9 10 Average 1.00 2.702 3 4 5 6 7 8 9 10
Average
1.00
2.70
Sample Standard Deviation (s)
1.41
2.71 N

18. T-test Procedure
The t-test for independent means is the appropriate test statistic
Small number of cases
Testing significance between two independent means

19. T-test Calculation

20. T-table
To determine the cutoff score, there are Na+Nb-2=13 degrees of freedom.
At .05 (use .025 column), the critical score is 2.16.

21. Decision Your calculated t-test score is -1.30.
The cutoff score is In this example, look at the negative side of the curve, so you use
Your t-score would fall into the unshaded area of the curve so you do not reject the Null hypothesis and conclude the means are not significantly different from one another.

22. One-way Analysis of Variance
Production
Office
Maintenance 3 1 7 4 8 2 5 9 6
4.00
1.17
7.00

23. One-way Analysis of Variance Example
The researcher randomly selects 11 subjects for each of the different training program, formats
If the subjects are not randomly selected, what could occur?
The researcher will compare the average number of forklift accidents incurred by each group to determine if there is a significant difference between the averages

24. Classroom
Hands-on
Combination 2 1
1.0000
Average

25. Classroom
Hands-on
Combination 2 1

26. Performing the Analysis
The ANOVA utilizes the F-ratio to determine if there is a significant difference between the group averages
The null hypothesis is:
Ave Grp 1 = Ave Grp 2 = Ave Grp 3
The alternative hypothesis is:
Ave Grp 1 NE Ave Grp 2 NE Ave Grp 3
A significant F-ratio indicates there is a significant difference between the group averages

27. ANOVA Results Anova: Single Factor SUMMARY Groups Count Sum Average
Variance
Class Training 11 1
0.8
Hand on only 5
Both
ANOVA
Source of Variation SS df MS F
P-value
F crit
Between Groups 2
Within Groups 30
Total 32

28. Interpretation
The ANOVA procedure found the significance of the F-ratio to be
If an Alpha level of .05 is used, then because .012 is less than .05, one can conclude there is a significant difference between the group averages
The odds of these results occurring totally due to random chance is
Another way of saying this is “The researcher has a percent chance of rejecting the Null Hypothesis when the Null Hypothesis is in fact true”

29. Post-Hoc Tests
When the ANOVA test result is found to be significant, the next step is to run a post-hoc test to determine where the significance lies between groups.
There are a number of different post-hoc tests that can be run (Scheffe’s, Tukey’s, etc.)
For example, is Group 1 significantly different from Groups 2 or 3
Is Group 2 significantly different from Groups 1 or 3
Etc.

30. Multivariate Analysis of Variance

31. One-way Analysis of Variance Example
A One-way Analysis of Variance identifies significant differences between group averages
In a One-way Analysis of Variance, the researcher randomly selects subjects and assigns them to one of three different forklift driving training programs.
The three different programs are:
Classroom based only
Hands-on only
Combination hands-on and classroom
Our treatment variable (or factor) is “forklift training program” and it has three levels (listed above)

32. Correlation