COURSE DESCRIPTION:
This course is an advanced examination of statistical analyses commonly used for research in business. It prepares the doctoral student with the skills required to plan, conduct (using SPSS), report, and interpret quantitative statistical analyses. Topics include: basic statistical knowledge, probability theory, exploratory date analysis, assumptions for statistical tests, parametric and nonparametric tests. Specific analyses include: correlation, regression (simple, multiple, and logistic), basic ANOVA and advanced ANOVA techniques.
Section 1: Research Methods, Basic Statistics, and the Fundamentals of IBM SPSS Statistics
Activity 1 Reviewing Research Methods and Basic Statistics, Entering Data, and Analysis
Instructions:
Part A
You will submit one file, a Word document. Please limit each response to 250 words or less. Name the file in the following format: lastnamefirstinitialBTM8107-1.doc (example: smithbBTM8107-1.doc).
1. Briefly describe your area of research interest (1-3 sentences is sufficient).
2. List 4 variables that you might assess in a research project related to your research area. List one for each type of measurement scale: Nominal, ordinal, interval, and ratio. If you cannot think of a variable for each measurement scale, explain why the task is difficult.
3. Create one alternate hypothesis and its associated null hypothesis related to your research area.
4. Briefly describe whether you think your area of interest is more conducive to experimental or correlational research. What are the costs/benefits of each as it relates to your research area?
5. Reliability vs. Validity. Considering your area of research interest, discuss the importance of reliability and validity. Can you have one without the other? Why or why not?
6. Sample vs. Population. Considering your area of research interest, describe the difference between a sample and population. Why is it important to understand the difference between a sample and population in a statistics course?
7. Measures of Central Tendency. Below is a set of data that represent weight in pounds for a particular sample. Calculate the mean, median and mode. Which measure of central tendency best describes this data and why? You may use Excel, SPSS, some other software program, or a hand calculator for this problem.
110.00
117.00
120.00
118.00
104.00
100.00
107.00
115.00
115.00
115.00
114.00
100.00
117.00
115.00
103.00
105.00
110.00
115.00
250.00
275.00
8. Measures of Dispersion. For the data set above, calculate the range, the interquartile range, the variance, and the standard deviation. What do these measures tell you about the "spread" of the data?
9. Descriptive Statistics. Why is it important to perform basic descriptive statistics prior to conducting inferential statistical tests?
10. Statistical Significance. Revisit the hypotheses you created above in #5. If you conducted a statistical test based on these hypotheses and found a statistically significant result, what would that mean from both a statistical and practical standpoint? (Be sure to use the phrases "null hypothesis" and "effect size" in your answer).
11. Type I and Type II Error. The concept of Type I and Type II Error is critical and will come into play not only with each and every statistical test you perform, but when you are asked to conduct an a priori power analysis for your Dissertation Proposal. Considering your answer to #10, discuss the implications of making both a Type I and Type II error.
12. After completing Assignment #1, are there any areas of concern you have that you would like to share with your course instructor?
Activity 2:
Reviewing Research Methods and Basic Statistics, Entering Data, and Analysis
Instructions:
Part B
You will submit a total of three files: two SPSS data files and one Word document.
Section A: Creating a Data File.
Open a data file in SPSS and enter the data presented in Table 3.1 on page 101. Save this SPSS data file.
Section B: Create a mock research project.
Submit your answers to the three questions below in a Word document.
1. Considering your area of research interest, briefly state your area and a possible research project related to the area (150-500 words).
2. Pose one or more null and alternative hypotheses that follow from the possible research project.
3. List at least 10 variables that would be collected in your mock research project that would be used to answer the hypotheses. After each variable, list the variable name you will use in SPSS (Section C), the level of measurement (binary, nominal, ordinal, interval, or ratio), and the possible range of scores. Feel free to be creative.
Section C: Create a mock SPSS data set.
1. Open a data file in SPSS and enter in a set of mock data for the research project you describe in Section B. (Note: It is important that you do not collect real data for this activity; you cannot collect data without IRB approval).
2. You must enter 10 rows of data for each of the 10 variables (that is, create data for 10 mock participants). Each row represents the scores of each mock participant on the ten variables.
3. Participant #1 must have missing data for Variable #3. Ensure this is coded correctly. You should now have three files for Part #2.
Activity 3:
Reviewing Research Methods and Basic Statistics, Entering Data, and Analysis
Part C
You will submit one Word document. You will create this Word document by exporting SPSS output into Word.
Section A. Creating Visual Displays of Data.
For this portion of the activity, you will export output you created while working in SPSS for Chapter 4 into a Word document. Please read the instructions below to ensure you are including the correct material in your document (This chapter has you create many charts and not all are required for Part #3).
1. Using the data set: DownloadFestival.sav, create a boxplot for males and females for the variable Dayl. It is important that you change the outlier identified to 2.02 prior to creating the boxplot. Be sure to save the data set with a new name, indicating it is the corrected data set (outlier identified and corrected). Save this boxplot with an appropriate title in your Part #3 Word document.
2. Using the data set: ChickFlick.sav, create a simple bar chart for independent means. The variables you will use are: Arousal, Film, and Gender (grouping variable). Be sure to display error bars and save your chart with an appropriate title in your Part #3 Word document.
3. Using the data set: Hiccups.sav, create a clustered bar chart for related means. The variables you will use are: Baseline, Tongue Pulling, Carotid Artery Massage, Digital Rectal Massage. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your Part#3 Word document.
4. Using the data set: Text Messages.sav (Note: you may see an additional data set with the same name: TextMessages.sav - either will create the correct output), create a clustered bar chart for mixed designs. The variables you will use are: Timel, Time2, and Group. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your Part #3 Word document.
5. Using the data set: Exam Anxiety.sav, create a scatterplot that includes a regression line. The variables you will use are: Exam Performance and Exam Anxiety. Be sure to include the regression line and save your chart with an appropriate title in your Part #3 Word document.
Section B. Why Exploratory Data Analysis?
Write a short paragraph that highlights your understanding of why exploratory data analysis is a critical part of any analytical strategy (500 word limit). This answer is worth half the assigned points for this activity. To receive full credit, you must show a high level of understanding related to the importance of exploring data visually.
Section 2: Assumptions and Common Statistical Strategies - Correlation, Regression, and Comparing Means
Activity 4: Understanding and Exploring Assumptions
You will submit one Word document, including your SPSS output.
1. Why do we care whether the assumptions required for statistical tests are met? (Tip: You might also want to write your answer on a note card you paste to your computer.)
2. Open the data set that you corrected in Activity #1 for DownloadFestival.say. You will use the following variables: Dayl, Day2, and Day3 (hygiene variable for all three days). Create a simple histogram for each variable. Choose to display the normal curve (under Element Properties) and title your charts. Copy these plots into your Word document.
3. Now create probability-probability (p-p) plots for each variable. This output will give you additional information. Read over the Case Processing Summary. Notice that there is missing data for Days 2 and Day 3. Copy only the Normal p-p Plots into your Word document (you do not need to copy the beginning output nor the Detrended Normal p-p Plots).
4. Examining the histograms and p-p plots, describe the dataset with particular attention toward the assumption of normality. For each day, do you think the responses are reasonably normally distributed? (Just give your impression of the data.) Why or why not?
5. Using the same dataset and the Frequency command, calculate the standard descriptive measures (mean, median, mode, standard deviation, variance and range) as well as kurtosis and skew for all three hygiene variables. Paste your output into your Word document (you do not need to paste the Frequency Table). What does the output tell you? You will need to comment on: sample size, measures of central tendency and dispersion and well as kurtosis and skewness. You will need to either calculate z scores for skewness and kurtosis or use those given in the book to provide a complete answer. Bottom line: is the assumption of normality met for these three variables? Does this match your visual observations from question #1?
6. Using the dataset SPSSExam.sav and the Frequency command, calculate: the standard descriptive statistics (mean, median, mode, standard deviation, variance and range) plus skew and kurtosis, and histograms with the normal curve on the following variables: Computer, Exam, Lecture, and Numeracy for the entire dataset. Complete the same analysis using University as a grouping variable. Paste your output into your Word document (you do not need to paste the Frequency Table). What do the results tell you with regard to whether the data is normally distributed?
7. Using the dataset SPSSExam.sav, determine whether the scores on computer literacy and percentage of lectures attended (with University as a grouping variable) meet the assumption of homogeneity of variance (use Levene's test). You must remember to unclick the "split file" option used above before conducting this test. What does the output tell you? (Be as specific as possible.)
8. Describe the assumptions of normality and homogeneity of variance. When these assumptions are violated, what are your options? Are there cases in which the assumptions may technically be violated, yet have no impact on your intended analyses? Explain.
Your submittal should demonstrate thoughtful consideration of the ideas and concepts that are presented in the course and provide new thoughts and insights relating directly to this topic. Where applicable your submittal should reflect scholarly writing and current APA standards. Review APA Form and Style.
Activity 5: Explore Correlation and Regression
Instructions:
You will submit one Word document. You will create this Word document by cutting and pasting SPSS output into Word. Please answer the questions first and include all output at the end of the activity in an Appendix.
Part A. SPSS Assignment
Part A of Assignment #3 has you familiarizing yourself with a set of data, providing you the opportunity to perform statistical tests and then interpret the output. You will rely on all you have learned to this point and add correlation and regression strategies to your skill set.
Using the data set: Chamorro-Premuzic.sav; you will focus on the variables related to Extroversion and Agreeableness (student and lecturer). To complete Part A
1. Exploratory Data Analysis.
a. Perform Exploratory Data Analysis on all variables in the data set. Because you are going to focus on Extroversion and Agreeableness, be sure to include scatterplots for these combinations of variables (Student Agreeableness/Lecture Agreeableness; Student Extroversion/Lecture Extroversion; Student Agreeableness/Lecture Extroversion; Student Extroversion/Lecture Agreeableness) and include the regression line within the chart.
b. Compose a one to two paragraph write up of the data.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Make a decision about the missing data. How are you going to handle it and why?
3. Correlation. Perform a correlational analysis on the following variables: Student Extroversion, Lecture Extroversion, Student Agreeableness, Lecture Agreeableness.
a. Ensure you handle missing data as you decided above.
b. State if you are using a one or two-tailed test and why.
c. Write up the results in APA style and interpret them.
4. Regression. Calculate a regression that examines whether or not you can predict if a student wants a lecturer to be extroverted using the student's extroversion score.
5. Multiple Regression. Calculate a multiple regression that examines whether age, gender, and student's extroversion predict if a student wants the lecturer to be extroverted.
a. Ensure you handle missing data as you decided above.
b. State if you are using a one or two-tailed test and why.
c. Include diagnostics.
d. Discuss assumptions: are they met?
e. Write up the results in APA style and interpret them.
f. Do these results differ from the correlation results above?
Part B. Applying Analytical Strategies to an Area of Research Interest
1. Briefly restate your research area of interest.
a. Pearson Correlation: Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem as well as direction of causality.
b. Spearman's Correlation: Identify two variables for which you could calculate a Spearman's correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem as well as direction of causality.
c. Partial Correlation vs. Semi-Partial Correlation: Identify three variables for which you may be interested in calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses using your variables and research example. Which would you use and why?
d. Simple Regression: Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R2 tell you about the relationship between the two variables?
e. Multiple Regression: Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variable and which as the outcome variable? Why? Which regression method would you use and why? What would R2 and adjusted R2 tell you about the relationship between the variables?
f. Logistic Regression: Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variable and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?
Activity 6: Analyze t test and ANOVA
You will submit one Word document and one SPSS data file. You will create the Word document by cutting and pasting SPSS output into Word.
Part A. Dependent t test
For this assignment, we are interested in finding out whether participation in a creative writing course results in increased scores of a creativity assessment. For this part of the activity, you will be using the data file "Activity 4a.sav". In this file, "Participant' is the numeric student identifier, "CreativityPre" contains creativity pre-test scores, and "CreativityPost" contains creativity post-test scores. A total of 40 students completed the pre-test, took the creativity course, and then took the post-test.
1. Exploratory Data Analysis/Hypotheses
a. Perform exploratory data analysis on CreativityPre and CreativityPost. Using SPSS, calculate the mean and standard deviation of these two variables.
b. Construct an appropriate chart/graph that displays the relevant information for these two variables.
c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).
2. Comparison of Means
a. Perform a dependent t test to assess your hypotheses above (note that many versions of SPSS use the term "paired samples t test" rather than "dependent t test"; the test itself is the same.
b. In APA style, write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t test.
Part B. Independent t test
We will start with the data file used in Part A ("Activity 4a.sav"). Suppose, however, you [the researcher] encountered a small problem during data collection: after the post-tests were collected, you realized that the post-test form did not ask for the students' identification number. As such, it will be impossible to match pre-test scores to post-test scores. Rather than simply give up, you start thinking about the data you do have and try to determine whether you can salvage your project. In assessing the situation, you realize that you have 40 pre-test scores and 40 post-test scores, but no way to link them. While it will result in a weaker comparison, you determine that you are still able to compare pre-test vs. post-test scores; you will use a between-subjects design rather than a within-subjects design.
1. Create the data set.
a. Using the "Activity 4a.sav" file as a starting point, create a new dataset that you can use with the between subjects design. Hint: you will no longer need the variables CreativityPre and CreativityPost. Instead, you have only one variable for the score on the creativity test. A second (or grouping) variable will serve to indicate which test the student took.
b. Save this dataset as Activity 4b.sav and remember to submit the file.
2. Exploratory Data Analysis/Hypotheses.
a. Perform exploratory data analysis on the creativity variable in the new dataset,
Activity 4b.say. Using SPSS, calculate the mean and standard deviation using the
grouping variable.
b. Construct appropriate charts/graphs that display the relevant information using
the grouping variable.
c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).
3. Comparison of Means
a. Perform an independent t test to assess your hypotheses above.
b. In APA style, write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t test.
4. Comparison of Designs
a. You used the same dataset to analyze both a between- and within-subjects design. Create a single paragraph (using the material you wrote above), that presents both sets of results.
b. Explain, in 300-500 words, whether the two tests resulted in the same findings. Did you expect this to be the case? Why or why not? What have you learned in this assignment?
Part C. ANOVA
All of us have had our blood pressure measured while at our physician's office. How accurate are these measurements? It may surprise you to learn that there is something called "white coat syndrome"-the tendency of some people to exhibit elevated blood pressure in clinical (medical) settings only. In other words, for these people, the very fact that the physician is taking their blood pressure causes it to increase In this activity, you will be using the "Activity 4c.sav" data file to determine whether you find support for the existence of white coat syndrome. In this study, 60 participants were randomly assigned to one of three groups. The "settings" variable indicates the location in which the participants' blood pressure was recorded: 1=home, 2=in a doctor's office, and 3=in a classroom setting. The "SystolicBP" variable contains the participants' systolic pressure (the "upper" number). The "DiastolicBP" variable contains the participant's diastolic pressure (the "lower" number).
1. Exploratory Data Analysis/Hypotheses.
a. Perform exploratory data analysis on both the SystolicBP and DiastolicBP variables. Using SPSS, calculate the mean and standard deviation of these two variables. Be sure that your analysis is broken down by setting (e.g., you will have six means, six SD's, etc.).
b. Create two graphs-one for systolic and one for diastolic pressure. Each graph should clearly delineate the three groups.
c. Write a null and alternative hypothesis for the comparison of the three groups (note that your hypothesis will state that the three groups are equivalent; be sure to word your null hypothesis correctly).
2. ANOVA.
a. Using the "Activity 4c.sav" data file, perform two single factor ANOVAs: one using SystolicBP and one using DiastolicBP as the dependent variable.
b. If appropriate for either or both of the ANOVAs, perform post hoc analyses to determine which groups actually differ.
c. Write one paragraph for each ANOVA (be sure to use APA style). At a minimum, each paragraph should contain the three means, three SD's, ANOVA results (F, df), post hoc tests (if applicable), effect size, and an interpretation of these results.
Section 3: Advanced Statistical Techniques
Activity 7: Apply ANCOVA and Factorial ANOVA
Instructions:
You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into Word.
This assignment consists of two parts. In the first part, you will utilize an existing dataset to compute a factorial ANOVA. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a hypothetical ANCOVA output table for variables related to your area of research interest.
Part A. SPSS Assignment
The "Activity 5.sav" file contains a dataset of a researcher interested in finding the best way to educate elementary age children in mathematics. In particular, she believes that 5th grade girls do better in small class sizes while boys excel in larger classes. Through the school district, she has arranged a pilot program in which some classroom sizes are reduced prior to the state-wide mathematics competency assessment. In the dataset, you will find the following variables:
Participant: unique identifier Gender. Male (M) or Female (F) Classroom:
Small (1) - no more than 10 children Medium (2) - between 11 and 19 children
Large (3) - 20 or more children
Score: final score on the statewide competency assessment.
To complete this assignment
1. Exploratory Data Analysis.
a. Perform exploratory data analysis on all variables in the data set. Realizing that you have six groups, be sure that your exploratory analysis is broken down by group. When possible, include appropriate graphs to help illustrate the dataset.
b. Compose a one to two paragraph write up of the data.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Factorial ANOVA. Perform a factorial ANOVA using the "Activity 5.sav" data set.
a. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case.
b. Is there a main effect of classroom size? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case.
c. Is there an interaction between your two variables? If so, using post hoc tests, describe these differences.
d. Is there support for the researcher's hypothesis that girls would do better than boys in classrooms with fewer students? Explain your answer.
e. Write up the results in APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.
Part B. Applying Analytical Strategies to an Area of Research Interest
3. Briefly restate your research area of interest.
Analysis of Covariance. Using your area of interest, identify one dependent and two independent variables, such that the independent variables would likely be covariates. Now, assume you conducted an ANCOVA that shows both the independent variable as well as the covariate significantly predicts the dependent variable. Create a mock ANCOVA output table (see SPSS Output 11.3 in your text for an example) that supports the relationship shown above. Report your mock finding APA style.
Activity 8: Apply Repeated-Measures
Instructions:
This assignment consists of two parts. In the first part, you will utilize an existing dataset to analyze the dataset from repeated-measures experimental design. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a dataset for a hypothetical repeated-measures experimental design. Finally, you will answer questions about your hypothetical dataset.
Part A. SPSS Assignment
The "Activity 6.sav" file contains a dataset of a high school teacher interested in determining whether his students' test scores increase over the course of a 12 week period. In the dataset, you will find the following variables:
Participant: unique identifier
Gender. Male (M) or Female (F)
Score_O- score on the initial course pre-test (first day of class)
Score_2 - score at the end of week 2 Score_4 - score at the end of week 4 Score _6 - score at the end of week 6 Score_8 - score at the end of week 8 Score 10 - score at the end of week 10 Score_12- score at the end of the course (week 12)
To complete this Activity
1. Exploratory Data Analysis.
a. Perform exploratory data analysis on the relevant variables in the dataset. When possible, include appropriate graphs to help illustrate the dataset.
b. Compose a one to two paragraph write up of the data.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Repeated-Measures ANOVA. Perform a repeated-measures ANOVA using the "Activity 6.sav" data set. You will use Score_0 through Score_12 as your repeated measure (7 levels), and gender as a fixed factor.
a. Is the assumption of sphericity violated? How can you tell? What does this mean in the context of interpreting the results?
b. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case.
c. Is there a main effect time (i.e., an increase in scores from Week 0 to Week 12)? If so, explain the effect. Use post hoc tests when necessary or explain why they are not required in this specific case. Examine the output carefully and give as much detail as possible in your findings.
d. Write up the results in APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.
Part B. Applying Analytical Strategies to an Area of Research Interest
3. Briefly restate your research area of interest.
a. Identify at least 2 variables for which you would utilize a repeated-measures ANOVA in your analysis. Describe the variables and their scale of measurement. Identify whether each factor is fixed or repeating. Where on the SPSS output would you look to find out if you violated the assumption of sphericity? If the data did violate this assumption, what would the impact be on your analysis?
Activity 9: Apply Non-Parametric Tests
Instructions:
You will submit one Word document. In the first part your assignment document, provide short answers to the following questions (250 words or less). Part A. Questions about non-parametric procedures
1. What are the most common reasons you would select a non-parametric test over the parametric alternative?
2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?
3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:
a. Dependent t test
b. Independent samples t test
c. Repeated measures ANOVA (one-variable)
d. One-way ANOVA (independent)
e. Pearson Correlation
Part B. SPSS Assignment
In this part of the assignment you will perform the non-parametric version of the tests you used in Week 4. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the relevant data files from Assignment #4, complete the following tests and paste your results into a Word document:
1. The non-parametric version of the dependent t test (use the SPSS dataset from Assignment #4, Part A);
2. The non-parametric version of the independent t test (use the SPSS dataset created in Assignment #4, Part B); and,
3. The non-parametric version of the single factor ANOVA (use the SPSS dataset in Assignment #4, Part C).
Activity 10: Apply MANOVA and Prepare a Course Reflection
Part A. SPSS Assignment
In this exercise, you are playing the role of a researcher that is testing new medication designed to improve cholesterol levels. When examining cholesterol in clinical settings, we look at two numbers: low-density lipoprotein (LDL) and high-density lipoprotein (HDL). You may have heard these called "good" (HDL) and "bad" (LDL) cholesterol. For LDL, lower numbers are better (below 100 is considered optimal). For HDL, 60 or higher is optimal.
In this experiment, you will be testing three different versions of the new medication. In data file "Activity 8.sav" you will find the following variables: group (0=control, 1=Drug A, 2=Drug B, 3=Drug C), LDL, and HDL (cholesterol numbers of participants after 12 weeks).
Using a MANOVA, try to ascertain which version of the drug (A, B or C) shows the most promise. Perform the following analyses and paste the SPSS output into your Word document.
1. Exploratory Data Analysis.
a. Perform exploratory data analysis on the relevant variables in the dataset. When possible, include appropriate graphs to help illustrate the dataset.
b. Compose a one to two paragraph write up of the data.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Perform a MANOVA. Using the "Activity 8.sav" data set, perform a MANOVA. "Group" is your fixed factor and LDL and HDL are your dependent variables. Be sure to include simple contrasts to distinguish between the drugs (group variable). In the same analysis, include descriptive statistics and parameter estimates. Finally, be certain to inform SPSS that you want a post-hoc test to help you determine which drug works best.
a. Is there any statistically significant difference in how the drugs perform? If so, explain the effect. Use the post hoc tests as needed.
b. Write up the results using APA style and interpret them.
Part B. Reflection
Reflect on your experience throughout the course and how you will use your new statistical skill set in the dissertation phase of your degree program. Include a brief assessment of what you have learned. In 2-3 paragraphs, cover the following:
1. What were the three most important concepts you learned?
2. How will the material in this course help you in your dissertation work?
3. What would you like to have seen covered that wasn't or what would you have liked more practice with?
Your submittal should demonstrate thoughtful consideration of the ideas and concepts that are presented in the course and provide new thoughts and insights relating directly to this topic. Where applicable your submittal should reflect scholarly writing and current APA standards. Review APA Form and Style.