SPSS Assignments

[ Home ] PSYC 222 (Research Methods II), Spring 2010 S. J. Gilbert, SUNY-Oneonta

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V SPSS ASSIGNMENTS FROM Cronk, "How to Use SPSS," Fifth Edition

SPSS Assignments

> The 12 SPSS assignments appear below. Each assignment includes reading and instructional material, and requires that you perform particular SPSS procedures. These procedures produce output tables and figures that you will need, in order to answer a set of questions that appear on a computer survey instrument. You access the survey by clicking on the appropriate hyperlink in each assignment.. Enter your answers to questions on the survey, along with a special "fake" name that you will create, and then press the SUBMIT button. This will send a document containing your answers, directly to me. You will receive feedback (comments and grades) from me, at a later time.

Grading of SPSS Assignments

>Assignments 1-3 are worth 4 pts. each; assignments 4-12 are worth 7 points each.

>The maximum number of points are awarded for each web survey that is submitted on time, and that contains no errors.

>Feedback concerning a web survey containing errors is given to you via email. REPLY to this email with clearly labeled corrections to each identified error (Note: Do NOT send a new email; REPLY to the email you receive from me). This process will continue until there are no errors. 1.5 points are deducted for each required resubmission. This penalty is designed to motivate you to get it right the first time!!! I encourage you to consult with me, or one of our TAs prior to submitting your web survey, rather than submitting a survey with partial answers, or answers you suspect are incorrect. The due dates for the SPSS assignments appear on the Course Calendar.

ASSIGNMENT 1: Data Definition

>>> Read Chapter 1 in the Cronk text.

>>> Dataset #1 contains the data you will be working with for Assignment 1. (Note: Use the form of Dataset #1 [A,B,C,D, or E] that you have been assigned.) Input the data from DATASET #1 into the DATAVIEW WINDOW of SPSS. Be sure to SAVE your work onto a flash drive, for future use.

DATASET #1. 20 students in a section of PSYC 100 were given their second test on either WHITE or BLUE paper. The test contained 50 multiple-choice questions; the number of correct answers constituted a student's SCORE on the test.

>>> Using the DEFINE VARIABLE WINDOW, give each variable a VARIABLE NAME (e.g., COLOR), and a VARIABLE LABEL (e.g., COLOR OF TEST).
>>> Give the VALUES of the variable SEX the LABELS "1 = male" and "2 = female".
>>> Give the VALUES of the variable COLOR the LABELS "1 = white" and "2 = blue."
>>> Be sure to SAVE the file onto a personal storage device (e.g., memory stick), as well the "P" drive of your computer; you will need it for future assignments.

Click here to Answer the Questions on the online Survey Form for Assignment 1

ASSIGNMENT 2: Descriptive Statistics I

>>> Read Chapter 2 in the Cronk text (Read Sec. 2.1, skip Sec. 2.2, pgs. 12-13, resume at top of pg. 14).

>>> Dataset #5 contains the data you will be working with for Assignment 2. Click on the form of Dataset #5 [A,B,C,D, or E] that you have been assigned. Now read this:

In her Psychology of Personality class, Rachel learned about the "Big Five" personality factors, and was interested in three of them -- Extroversion, Agreeableness, and Conscientiousness. Rachel gave the"Big Five" subscales for these three factors to 22 women living in Hays Hall. Rachel thought that the SUM of each subject's scores on the three scales would provide a measure of the construct "CHARISMA." (Rachel also measured the popularity of each subject -- but this is not relevant to our interests here.)

>>>Using COMPUTE command of SPSS, create a NEW VARIABLE, labeled CHARISMA. The CHARISMA score is the sum of subjects' Extroversion, Agreeableness, Conscientiousness scores (i.e., CHARISMA = Extroversion + Agreeableness +Conscientiousness.

>>> Using the DESCRIPTIVES procedure, generate descriptive statistics for subjects' CHARISMA SCORES. The resulting SPSS OUTPUT files should resemble the Assignment 2 sample file shown here.

>>> Save the OUTPUT FILE; you will need this output to answer the questions below.

Click here to answer the Questions on the online Survey Form for Assignment 2

Look over questions A2-4 and A2-5. If the answers are not obvious to you, then read this.....

Suppose I calculate the mean number of CDs in the dorm rooms of 200 SUCO students, along with the standard deviation. I get: M = 15.0, SD = 3.0. What does the SD statistic tell us? If we randomly choose students from the sample of 200, our best guess of the number of CDs possessed by any one of them will be 15 (the mean). But our guesses will not be perfect; students will often have more than 15 CDs (e.g., 16, 17, or 18) or fewer than 15 CDs (e.g., 14, 13, or 12). The typical student will differ from our guess (of 15) by about 3 -- the standard deviation.

But we can be more precise. If the distribution of CDs in the rooms of these 200 students is roughly NORMAL -- the classic bell-shaped curve -- then certain things will be true, by virtue of the nature of the SD statistic.

(1) About 2/3rds (around 68%) of the scores will be within 1 SD of the mean. That means that about 2/3rds of the 200 students will have between 12 CDs (Mean - 1SD = 15 - 3 = 12) and 18 CDs (Mean + 1 SD = 15 + 3 = 18).

(2) About 95% of the scores will be within 2 SDs of the mean. That means that about 95% of the 200 students will have between 9 CDs (Mean - 2SD = 15 - 6 = 9) and 21 CDs (Mean + 2 SDs = 15 + 6 = 21).

(3) About 99% of the scores will be within 3 SDs of the mean. That means that about 99% of the 200 students will have between 6 CDs (Mean - 3SD = 15 - 9 = 6) and 24 CDs (Mean + 3 SDs = 15 + 9 = 24).

Whenever a distribution is roughly normal, and the number of scores considered is reasonably large, then it will be true that around 68% of the scores will be within 1 SD, 95% will be within 2SDs, and 99% within 3 SDs of the mean. This should help you with Assignment #2.

ASSIGNMENT 3: Descriptive Statistics II

>>> Read Sections 3.1, 3.3, and 3.4 of Chapter 3 in the Cronk text.

>>> Load the form of DATASET #1 that you have been assigned (A,B,C,D, or E) into the DATAVIEW window.

>>> Using the DESCRIPTIVES procedure, generate descriptive statistics for subjects' SCORE on the test.

>>> Using the ANALYZE -> COMPARE MEANS -> MEANS procedure, generate separate descriptive statistics for the test SCORES of the MALE and the FEMALE subjects.

>>> Using the ANALYZE -> COMPARE MEANS -> MEANS procedure, generate separate descriptive statistics for the test SCORES of subjects who took BLUE tests and those who took WHITE tests.

>>> Using the ANALYZE -> COMPARE MEANS -> MEANS procedure, and employing the "LAYERING" feature, generate separate descriptive statistics for the test SCORES of subjects with each combination of sex and paper color, i.e., MEN who took BLUE tests, MEN who took WHITE tests, WOMEN who took BLUE tests, and WOMEN who took WHITE tests. The final output file should resemble the Assignment 3 sample file shown here.

>>> Save the OUTPUT FILE containing the descriptive statistics; you will need this output to answer the questions below.

Click here to Answer the Questions on the online Survey Form for Assignment 3

ASSIGNMENT 4: Independent Samples (between groups) t-test

>>> Read Alison's story in Box 3.

Box 3.
Alison was interested in determining whether the color of the paper a test was printed on, affected students' performance on the test. She had a hunch that students will do better on a blue paper test than on a white paper test. To test her hunch, Alison gave the 20 students in a particular PSYC 100 section a 50 multiple-choice question psychology test either on blue paper or white paper. Which color test a student received was determined by a flip of a coin. Later, Alison calculated the mean test score earned by the blue paper test group, and the mean for the white paper test group, and compared the two means.

>>> Read Chapter 6, Section 6.3 in the Cronk text.
>>> Load the form of DATASET #1 that you have been assigned (A,B,C,D, or E) into the DATAVIEW window.
>>> Make sure you understand the definition in Box 4.

Box 4.
The INDEPENDENT SAMPLES t-test is used when a researcher has divided subjects into TWO GROUPS, and wishes to compare the means of these two groups on a dependent variable that has been measured using an interval or ratio scale of measurement .

>>> Perform an INDEPENDENT SAMPLES t-test appropriate to test Alison's hypothesis. The relevant output tables should resemble the Assignment 4 sample tables shown here.

Click here to Answer the Questions on the online Survey form for Assignment 4.

ASSIGNMENT 5: Paired (within-subjects) t-test

>>> Read Greg's story in Box 5.

Box 5.
Greg noticed that it seemed easier to read and understand printed material when the letters were in a bold font rather than the standard font. To test out this hunch, Greg gave 20 PSYC 100 students their 100-question final exam in a special format. The even numbered questions appeared in the standard font, but the odd numbered questions were in a bold font. Greg calculated a separate mean score for the 50 standard-font (even numbered) questions and the 50 bold-font (odd numbered) questions, and compared these two means.

>>> Read Chapter 6, Section 6.4 in the Cronk text.
>>> Dataset #2 contains the data you will be working with for Assignment 2. Click on the form of Dataset #2 [A,B,C,D, or E] that you have been assigned.

>>> Make sure you understand the definition in Box 6.

Box 6.
A PAIRED t-test compares the means of two measures taken on the same people. These can be the same measure given twice (e.g., the same 50-item psychology test given first as a pretest, and later as a posttest), or two different measures that use the same or comparable scales (e.g. the 50 even and the 50 odd numbered questions on a 100 question psychology test).

>>> Do a PAIRED t-test to test Greg's research hypothesis. The relevant output tables should resemble the Assignment 5 sample tables shown here.

Click here to Answer the Questions on the online Survey Form for Assignment #5

ASSIGNMENT 6: Independent Samples (between-groups) One-way ANOVA

>>> Review Alison's story in Box 3.
>>> Read Jenny's story in Box 8.

Box 8.
Jenny was intrigued by Alison's experiment on the effect of blue vs. white paper on how students did on tests. She agreed with Alison's hunch that students will do better on a blue paper test than on a white paper test and wanted to replicate that aspect of Alison's experiment. But Jenny went further. She reasoned that students should do POORER on a test written on RED paper than one written on the standard white paper. To test her hunch, Jenny gave a different group of 20 students in a PSYC 100 section a 50 question multiple-choice psychology test either on blue, white, or red paper. Which color test a student received was determined by a roll of dice (1 or 2 = blue; 3 or 4 = white; 5 or 6 = red). Later, Jenny calculated the mean test score earned by the blue, white and red paper test groups, and compared the three means.

>>> Read Chapter 6, Section 6.5 in the Cronk text.
>>> Dataset #3 contains the data you will be working with for Assignment 6. Click on the form of Dataset #3 [A,B,C,D, or E] that you have been assigned.

>>> Read Box # 9.

Box 9.
An INDEPENDENT SAMPLES (BETWEEN GROUP) ANOVA compares the means of MORE THAN TWO groups of subjects on a dependent variable that has been measured using an interval or ratio scale of measurement.

RESEARCH hypotheses and STATISTICAL hypotheses differ. You need to understand how. So please read the following carefully.

Suppose I gather PSYC, SOC, and POLI-SCI majors and measure how funny they are. My RESEARCH hypotheses might be: (a) PSYC majors are funnier then SOC majors; and (b) SOC majors are funnier than POLI-SCI majors. These are two DIRECTIONAL RESEARCH hypotheses.

Statistically, the first step in testing these two RESEARCH hypotheses is to do an INDEPENDENT SAMPLES (BETWEEN GROUP) ANOVA, directly comparing the funniness means of the three groups. The primary STATISTICAL HYPOTHESIS in an ANOVA always is NON-DIRECTIONAL; it is the hypothesis that the means differ from each other more than would be expected by chance. If the STATISTICAL ANOVA hypothesis is NOT SUPPORTED -- if the three means DON'T differ more than would be expected by chance -- then we conclude that neither of our RESEARCH HYPOTHESES is SUPPORTED (i.e., PSYC majors are NOT funnier than SOC majors, and SOC majors are NOT funnier than POLI-SCI majors).

But, what if the STATISTICAL hypothesis of the ANOVA -- that the three groups differ in funniness -- IS SUPPORTED (at p < .05)? Then, we instruct SPSS to perform a SHEFFE POST-HOC test. This is like doing three INDEPENDENT SAMPLES t-tests (comparing the funniness means of PSYC vs. SOC majors, SOC vs. POLI-SCI majors, and PSYC vs. POLI-SCI majors). The first two comparisons are direct tests of our two RESEARCH HYPOTHESES.

>>> Perform an Analysis of Variance (ANOVA) on the data from Jenny's experiment. Be sure to instruct SPSS to include descriptive statistics (Mean & Standard Deviation of the dependent variable measure for each group), and the Sheffe Post-Hoc Comparison test. The relevant output tables should resemble the Assignment 6 sample tables shown here.

Click here to Answer the Questions on the online Survey Form for Assignment #6

ASSIGNMENT 7: Repeated Measures One-Way ANOVA

>>> Review Greg's story in Box 5.
>>> Read Ken's story in Box 10.

Box 10.
Ken could see how it would be easier to read and understand material in a bold font than material in a standard font. Ken noticed that in some of his textbooks, certain blocks of material (like a quotation) are written in italics, and he always found that hard to read. Ken speculated that just as students would do better on test questions written in a bold font (than in a standard font), they would do poorer on questions written in italics (than in a standard font). To test these predictions, Ken gave 21 students in a section of ANTH 100 their 150 question final exam in a special format. Questions 1,4,7,10....148 appeared in a standard font, questions 2,5,8,11....149 were in a bold font, and questions 3,6,9,12....150 were in an italics font. Ken calculated the students' mean scores on the standard, bold, and italic questions, and then compared the means.

>>> Read Chapter 6.7 in the Cronk text.
>>> Dataset #4 contains the data you will be working with for Assignment 7. Click on the form of Dataset #4 [A,B,C,D, or E] that you have been assigned.

>>> Make sure you understand the definition in Box 11.

Box 11.
A REPEATED MEASURES ONE-WAY ANOVA in many ways resembles the PAIRED SAMPLE t-test (Assignment #5), but it compares the means of MORE than two measures (rather than just two measures) taken on the same people. As with the PAIRED SAMPLE t-test, it only makes sense to compare the means of measures that are taken on the same scale.

Note: The following discussion will resemble one that you read in Box 9, for Assignment 6. There the discussion related to a BETWEEN (or INDEPENDENT) GROUPS design. Here it relates to a REPEATED MEASURES (or WITHIN -SUBJECT) design.

RESEARCH hypotheses and STATISTICAL hypotheses differ. Suppose I gather 100 students, have them listen to 10 minute samples of CLASSICAL MUSIC, 50's ROCK, and HIP-HOP music, and measure how much they like each on a 1 (hate) to 10 (love) scale. My RESEARCH hypotheses might be: (a) students will like HIP-HOP better than ROCK; and (b) students will like CLASSICAL less than ROCK. These are two DIRECTIONAL research hypotheses.

Statistically, the first step in testing these two hypotheses is to do a ONE-WAY REPEATED MEASURES ANOVA, directly comparing the mean ratings given to the three kinds of music. The primary STATISTICAL HYPOTHESIS in an ANOVA always is NON-DIRECTIONAL; it is the hypothesis that the means differ from each other more than would be expected by chance.

If the STATISTICAL ANOVA hypothesis is NOT SUPPORTED -- if the three means DON'T differ more than would be expected by chance -- then we conclude that neither of our RESEARCH HYPOTHESES is SUPPORTED (i.e., students do NOT like HIP-HOP better than ROCK, and students do NOT like CLASSICAL less than ROCK).

But, what if the STATISTICAL ANOVA hypothesis -- that the three mean music ratings differ -- IS SUPPORTED (at p < .05)? Then, you must do what you learned in Assignment 5! Instruct SPSS to do a PAIRED SAMPLES (WITHIN-SUBJECTS) t-test, comparing the means for HIP-HOP and ROCK (testing the first RESEARCH HYPOTHESIS), and a second t-test comparing the means for CLASSICAL and ROCK (testing the second RESEARCH HYPOTHESIS). These two t-tests directly test your two RESEARCH HYPOTHESES.

>>> Do a REPEATED MEASURES ONE-WAY ANOVA on these three variables. But before you hit the 'OK' button to run the analysis, read this note....

Note: You will want the output to include the mean and standard deviation for each variable. In order to obtain these statistics, you must click on the OPTIONS button on the lower right hand corner of the 'Repeated Measures' dialog box, and then click on DESCRIPTIVES. Then click on the CONTINUE button, and finally, on the OK button.

The relevant output tables should resemble the Assignment 7 sample tables shown here.

Click here to Answer the Questions on the online Survey Form for Assignment #7

ASSIGNMENT 8: Correlation Coefficient

>>> Read Rachel's story in Box 12.

Box 12.
In her Psychology of Personality class, Rachel learned about the "Big Five" personality factors, and was interested in how three of them -- Extroversion, Agreeableness, and Conscientiousness -- related to each other, and to how many friends a student has. Rachel gave the three "Big Five" subscales that measure Extroversion, Agreeableness, and Conscientiousness, to 22 women living in Hays Hall. In addition, she asked the women to make a list of people on campus whom they consider to be a friend. Rachel counted the number of names on each subject's list and used that number as a measure of popularity. Rachel expected each personality scale to show a positive relationship with her measure of popularity.

>>> Read Chapter 5, Section 5.1 in the Cronk text. Review Chapter 4, Section 4.4.
>>> Load the form of DATASET #5 that you have been assigned (A,B,C,D, or E) into SPSS.

>>> The first question on the web survey for this assignment will be: (A8-1) Name two variables that Rachel expected to have a positive relationship with each other.

>>> Produce a SCATTERPLOT that graphically shows the actual relationship between the variables you chose in (A8-1). One of the questions on the web survey for this assignment will refer to the scatterplot.

>>> Perform the CORRELATION procedure, making an inter-correlation table showing the relationships among all four variables that Rachel measured. The relevant output tables should resemble the Assignment 8 sample tables shown here.

This assignment will ask questions about NULL Hypotheses, Research Hypotheses, and statistical significance, as they apply to correlational studies. So, before you go to the web survey, read this: Guide to Understanding Hypothesis Testing in Correlation Studies.

Click here to Answer the Questions on the online Survey Form for Assignment #8

ASSIGNMENT 9: Multiple Regression Analysis

>>> Read Chapter 5, Sections 5.3 & 5.4 in the Cronk text.
>>> Review Rachel's story in Box 12, and the inter-correlation table you produced for Assignment 8.
>>> Read Dan's story in Box 13.

Box 13.
Rachel showed Dan the inter-correlation table she had made. Dan was intrigued, and wondered how well you could predict a young woman's popularity, from knowledge of her levels of Extroversion, Agreeableness, and Conscientiousness. To find out, Dan obtained Rachel's data, and performed a Multiple Regression Analysis, with POPULAR as the DEPENDENT (Y) VARIABLE, and Extroversion (X1), Agreeableness (X2), and Conscientiousness (X3) as three INDEPENDENT, or PREDICTOR variables..

>>> Read Box 14.

Box 14.
The Multiple Regression also tells us whether each predictor (independent) variable significantly contributes to the prediction of the outcome (dependent) variable after the contribution of the other predictor variable(s) have been factored out--in effect telling us whether each predictor accounts for a UNIQUE portion of the variance in the outcome variable -- a portion NOT already explained by the other predictors.

>>> Load the form of DATASET #5 that you have been assigned (A,B,C,D, or E) into SPSS.
>>> Do the MULTIPLE REGRESSION that Dan did. . The relevant output tables should resemble the Assignment 9 sample tables shown here.

Click here to Answer the Questions on the online Survey Form for Assignment #9

ASSIGNMENT 10: Two-Way Chi Sq test

>>> Load the form of DATASET #1 that you have been assigned (A,B,C,D, or E) into SPSS.
>>> Review Alison's story in Box 3, Assignment 4.
>>> Read Box 15.

Box 15.
The INDEPENDENT VARIABLE of Alison's experiment was COLOR of PAPER

(white vs. blue). Alison made sure that exactly 10 of her 20 subjects got a WHITE paper test, and 10 got a BLUE paper test. What Alison didn't do was assure that MEN and WOMEN were equally represented in the WHITE and the BLUE groups. Ideally, the selection of subjects should have produced the following breakdown.

COLOR OF PAPER

	WHITE	BLUE
MEN	n = 5	n = 5	N(men) = 10
WOMEN	n = 5	n = 5	N(women) = 10
	N(white) =10	N(blue) = 10

With such a breakdown, differences in the mean test score of subjects in the WHITE vs. BLUE test groups could not be attributed to one group containing mostly men and the other group containing mostly women, because both groups had the same proportion of men to women.

But what if the breakdown looked like this?

COLOR OF PAPER

	WHITE	BLUE
MEN	n = 8	n = 2	N(men) = 10
WOMEN	n = 2	n = 8	N(women) = 10
	N(white) =10	N(blue) = 10

Here, the proportion of MEN to WOMEN in the WHITE group appears to be SIGNIFICANTLY different from the proportion of MEN to WOMEN in the BLUE group. In such a situation, differences between the mean test scores of subjects in the WHITE and the BLUE group MIGHT be due, at least in part, to differences between how well MEN (who make up most of the WHITE group) and WOMEN (who make up most of the BLUE group) do on tests. This would seriously compromise the INTERNAL VALIDITY of Alison's experiment.

Alison wants to know whether the proportion of MEN:WOMEN in one group (WHITE) is significantly different than in the other group (BLUE). If it is NOT, then she can assume that the internal validity of her experiment is not compromised. But if the proportion of MEN:WOMEN in the WHITE and BLUE groups DOES significantly differ, then her experiment does suffer from compromised internal validity. To find this out, Alison needs to do a two-way CHI-SQ test.

>>> Read Chapter 7, Section 7.2 in the Cronk text.

>>> Load the form of DATASET #1 that you have been assigned (A,B,C,D, or E) into SPSS

To determine whether the proportion of MEN to WOMEN SIGNIFICANTLY differed in the two groups, Alison did a CHI SQUARE test of INDEPENDENCE, with SEX and COLOR as the two variables.

>>> Make an appropriate CROSSTABULATION TABLE, and perform Alison's CHI SQUARE test. The relevant output tables should resemble the Assignment 10 sample tables shown here.

Click here to Answer Questions on the online Survey Form for Assignment #10

ASSIGNMENT 11: Two-Way Between Groups ANOVA

>>> Review Alison's story in Box 3.
>>> Read Sonia's story in Box 16.

Box 16.
Sonia was Alison's research partner, and also was interested in the effect of blue vs. white paper on how students did on tests. Sonia wondered whether the color of paper would affect men and women the same way. That's why Sonia insisted that they record the sex of her subjects. So Sonia reanalyzed the data, looking at how both the role of the color of the paper (BLUE vs. WHITE) AND the sex of subject (MALE vs. FEMALE) affected test scores. Sonia predicted that men would perform equally well on blue and white paper tests, but that women would perform significantly better on blue tests than on white tests. In other words, Sonia expected that the color of paper would have no effect on the scores of men, but would have a substantial effect -- favoring blue tests -- on the scores of women.

>>>Box 17 represents the structure of Sonia's experiment. The letters A-G represent means. For example, the letter A represents the mean test score of men who took the test on blue paper. The letter H represents the mean test score for all women.

Box 17.

	BLUE	WHITE
MEN	A	B	G
WOMEN	C	D	H
	E	F

>>> Read Chapter 6, Section 6.6 in the Cronk text.
>>> Load the form of DATASET #1 that you have been assigned (A,B,C,D, or E) into SPSS.

>>> Perform the appropriate Analysis of Variance (ANOVA) on the data from Sonia's experiment. Be sure to instruct SPSS to include descriptive statistics. The relevant output tables should resemble the Assignment 11 sample tables shown here.

Click here to Answer Questions on the online Survey Form for Assignment #11

ASSIGNMENT 12: Two-Way Mixed (Between & Within) ANOVA

>>> Review Greg's story in Box 5.

>>> Review Ronnie's story in Box 17.

Box 17.
Ronnie was Greg's research partner, and also was interested in the effect of standard vs. bold font on how students did on tests. Ronnie wondered whether the type of font would affect men and women the same way. That's why Ronnie insisted that they record the sex of the subjects. So Ronnie reanalyzed the data, looking at how both the role of the type of font (STANDARD vs. BOLD) and the sex of subject (MALE vs. FEMALE) affected test scores. Ronnie predicted that women would perform equally well on standard and bold font questions, but that men would perform significantly better on bold font questions than on standard font questions. In other words, Ronnie expected that the type of font would have no effect on the scores of women, but would have a substantial effect -- favoring the bold font -- on the scores of men.

>>>Box 19 represents the structure of Ronnie's experiment. The letters A-G represent means. For example, the letter A represents the mean test score of men on the standard font questions. The letter H represents the mean test score for all women.

Box 17.

	STAND	BOLD
MEN	A	B	G
WOMEN	C	D	H
	E	F

>>> Read Chapter 6, Section 6.8 in the Cronk text.
>>> Load the form of DATASET #2 that you have been assigned (A,B,C,D, or E) into SPSS.

>>> Perform a Mixed-Design Analysis of Variance (ANOVA) on the data from Ronnie's experiment. Be sure to instruct SPSS to include descriptive statistics. Assignment 12 sample tables shown here.

Click here to Answer Questions on the online Survey Form for Assignment #12

[ Home ] PSYC 222 (Research Methods II), Spring 2010 S. J. Gilbert, SUNY-Oneonta