SPSS Programming Worksheet

How to Recode VariablesTo create a dichotomous variable from a multi-category one, like undergrad major:
• In Data View, go to: Transform à Recode à Into Different Variables
• Move the variable you want to recode (e.g., ug_major) into the box on the right. Give the
Output Variable a new name (at most 8 characters) and a label (e.g., “psych major or not”).
Click “change”.
• Click on “old and new values”. For the 1st old value, write “psych” in the box on the left
called “value”. For the new value, type “1”, and then click “add”. For the 2nd old value, click
on “all other values” in the bottom-left corner. For the new value, type “2”, and then click
“add”. Click on “continue”.
• Press OK. A new column should be created to the right of all your other columns, containing
only 1’s and 2’s.
• In Variable View, give your recoded variable value labels. Click on the box in the “value”
column corresponding to the new variable, and type “1” for the value and type “psychology”
for the value label; click “add”. Repeat, but this time, type “2” for the value and type “other
major” for the value label; click “add”.
• Press OK.
To create a dichotomous variable from a continuous one, like the math phobia rating:
• Follow the first two steps above, but select “phobia” as the Input Variable, call the Output
Variable “phob_grp,” and label it “low vs. high math phobia.”
• Click on “old and new values”. For the 1st Old Value, click to the left of “Range: Lowest
through” and type a “4” in the blank space. For the New Value, type “1”, and then click
“Add”. For the 2nd old value, click to the left of “Range: [blank space] through highest” and
type a “5” in the blank space. For the new value, type “2”, and then click “Add” again. Click
on “continue”.
• Press OK. A new column should be created to the right of all your other columns, containing
only 1’s and 2’s.
• In Variable View, give Value Labels to your new variable – e.g., “low phobia group” for 1,
and “high phobia group” for 2.
STATISTICS FOR PSYCHOLOGISTS
STATISTICS IN APA STYLE
Section Abstract: This section describes basic rules for presenting statistical results in APA style.
All rules come from the newest APA style manual. Specific examples of mini Results summaries
(and data tables) are provided, using the analyses in the previous section of this project.
Keywords: APA style, Results sections, statistical interpretation
This document is part of an online statistics textbook.
Access to the complete textbook, along with licensing information, is available online:
http://www4.uwsp.edu/psych/cw/statistics/
Table of Contents for This Section
GENERAL RULES FOR APA STYLE RESULTS SECTIONS …………………………………………………………………………………………………….. 2
EXAMPLES OF APA STYLE ……………………………………………………………………………………………………………………………………………. 3
SUMMARY OF PARAMETRIC STATISTICS ………………………………………………………………………………………………………………………… 5
GENERAL RULES FOR APA STYLE RESULTS SECTIONS
Overview
The APA manual describes appropriate strategies for presenting statistical information. These guidelines were established
to provide basic minimal standards and to provide some uniformity across studies.
Using a Sufficient Set of Statistics
Information to Include: Significance testing “is but a starting point and that additional reporting elements such as effect
sizes, confidence intervals, and extensive description are needed to convey the most complete meaning of the results” (p.
33).
1.
2.
3.
4.
Descriptive statistics are essential and “such a set usually includes at least the following: the per-cell sample sizes; the
observed cell means (or frequencies of cases in each category for a categorical variable); and the cell standard
deviations” (p. 33).
For statistical significance tests, “include the obtained magnitude or value of the test statistics, the degrees of freedom,
the probability of obtaining a value as extreme as or more extreme than the one obtained (the exact p value)” (p. 34).
When possible, confidence intervals should be emphasized. “The inclusion of confidence intervals (for estimates of
parameters, for functions of parameters such as differences in means, and for effect sizes) can be an extremely effective
way of reporting results” (p. 34).
“For the reader to appreciate the magnitude or importance of a study’s findings, it is almost always necessary to
include some measure of effect size” (p. 34). These can be in the original (raw) units or in a standardized metric.
Information in Text versus Data Displays: “Statistical and mathematical copy can be presented in text, in tables, and in
figures. . . Select the mode of presentation that optimizes understanding of the data by the reader” (p. 116).
•
•
•
Generally speaking, the more data you have, the more likely it is that they should be presented in a table or figure. “If
you need to present 4 to 20 numbers, first consider a well-prepared table” (p. 116).
“If you present descriptive statistics in a table or figure, you do not need to repeat them in text, although you should
(a) mention the table in which the statistics can be found and (b) emphasize particular data in the narrative when
they help in interpretation” (p. 117).
As a result, it is necessary that the text include a description of the variable(s) under study and a description of the
statistical procedures used. The text often includes a description of whether the results support the hypotheses.
Note: In the texts, for the variables I have used “IV”, “DV”, etc., If you report your analysis, then you
should use the names for your constructs or variables instead. So, you should write, e.g., “Intergroup
contact had a significant positive effect on prejudice, b = ….” and NOT “The independent variable had a
significant positive effect on the dependent variable = …”.
Note: All non-Greek statistical symbols, including p, z, t, r, M, F, N, and MSE, should be in ITALICS as
you are typing up results. If you are handwriting results (i.e., on an exam or in R script or any stats
package where you cannot change the italics), don’t worry about noting the italics.
Page 2
All quotations pertaining to reporting results are taken from: American Psychological Association. (2010).
Publication manual of the American Psychological Association (6th Ed.). Washington, DC: APA.
Page 3
EXAMPLES OF APA STYLE RESULTS IN THE TEXT
Descriptive Statistics: The purpose of the descriptive statistics is to provide the reader with an idea about the basic elements
of the group(s) being studied. Note that this also forms the basis of the in-text presentation of descriptive statistics for the
inferential analyses below.
On the quiz, the nine students had a mean score of 7.000 (SD = 1.225). Scores
of 6.000, 7.000, and 8.000 represented the 25th, 50th, and 75th percentiles,
respectively.
Correlations- Pearson or Spearman: Correlations provide a measure of statistical relationship between two variables. Note
that correlations can be tested for statistical significance (and that this information should be summarized if it is available
and of interest).
For the nine students, the scores on the first quiz (M = 7.000, SD = 1.225) and
the first exam (M = 80.889, SD = 6.900) were strongly and significantly
correlated, r(8) = .695, p = .038.
Linear Regression:
Perception/Appropriate Response was found to be significantly related to likeability
F(2, 126) = 20.55, p < .001 with an adjusted R2 = .234, 95% CI [.32,.89.]. One Sample t Test: In this case, a sample mean has been compared to a user-specified test value (or a population mean). Thus, the summary and the inferential statistics focus on that difference. A one sample t test showed that the difference in quiz scores between the current sample (N = 9, M = 7.000, SD = 1.225) and the hypothesized value (6.000) were statistically significant, t(8) = 2.449, p = .040, 95% CI [0.059, 1.941], d = 0.816. Note: the number of df should always be put in parentheses after the t because that information is required to determine the critical value for t. Also, the sample standard deviation should be included along with the mean. Independent Samples t Test: For this analysis, the emphasis is on comparing the means from two groups. Here again the summary and the inferential statistics focus on the difference. An independent sample t test showed that the difference in quiz scores between the control group (N = 4, M = 6.000, SD = 0.817) and the experimental group (N = 4, M = 8.000, SD = .817) were statistically significant, t(6) = -3.464, p = .013, 95% CI [-3.413, -0.587], d = -2.449. Note: the number of df should always be put in parentheses after the t because that information is required to determine the critical value for t. Also, the sample standard deviation should be included along with the mean. Matched /Dependent Samples t Test: A t test for correlated samples revealed that the phonic method produced significantly better reading performance (M = 2.22) than the visual method (M = 2.04) when the pupils were tested at the end of 6 months, t(9) = -2.42, p < .05 (two-tailed). Page 4 Note: the number of df should always be put in parentheses after the t because that information is required to determine the critical value for t. Also, the sample standard deviation should be included along with the mean. One Way ANOVA: The ANOVA provides an omnibus test of the differences across multiple groups. Because the ANOVA tests the overall differences among the groups, the text discusses the differences only in general. A one way ANOVA showed that the difference in quiz scores between the control group (N = 3, M = 4.000, SD = 1.000), the first experimental group (N = 3, M = 8.000, SD = 1.000), and the second experimental group (N = 3, M = 9.000, SD = 1.000) were statistically significant, F(2,6) = 21.000, p = .002, η2 = .875. One Way ANOVA with Post Hoc Tests: Post hoc tests build on the ANOVA results and provide a more focused comparison among the groups. Notice that the post hoc summary duplicates the presentation of the omnibus ANOVA statistics. A one way ANOVA showed that the difference in quiz scores between the control group (N = 3, M = 4.000, SD = 1.000), the first experimental group (N = 3, M = 8.000, SD = 1.000), and the second experimental group (N = 3, M = 9.000, SD = 1.000) were statistically significant, F(2,6) = 21.000, p = .002, η2 = .875. Tukey’s HSD tests showed that both experimental groups scored statistically significantly higher than the control group. However, the two experimental groups did not differ significantly. Repeated Measures ANOVA: The RMD ANOVA tests for overall differences across the repeated measures. As such, its summary parallels that of the One Way ANOVA. A repeated measures ANOVA showed that, for the five people, the difference in quiz scores between the first time point (M = 6.400, SD = 1.140) and second time point (M = 7.800, SD = 0.837) were statistically significant, F(1,4) = 32.667, p = .005, partial η2 = .875. Factorial 2 Way ANOVA: The factorial ANOVA provides statistics for all of the main effects and interactions in a factorial design. Each effect would be summarized in a style analogous to a One Way ANOVA. A 2 (Factor A) x 2 (Factor B) ANOVA was conducted on the quiz scores. Neither Factor A, F(1,8) = 0.000, p = 1.000, partial η2 = .000, nor Factor B, F(1,8) = .750, p = .412, partial η2 = .086, had a statistically significant impact on quiz scores. However, the interaction was statistically significant, F(1,8) = 6.750, p = .032, partial η2 = .458. The descriptive statistics for these analyses are presented in Table 1. Mixed Design ANOVA: The mixed design is for one independent variable between groups and a second independent variable that is a repeated measures The phobia intensity ratings were submitted to a 2x3 mixed design ANOVA, in which treatment group (experimental versus placebo control) served as the between-subjects variable, and time (before versus after versus follow-up) served as the within-subjects variable. The main effect of treatment group did not attain significance,F(1,6) = 3.61, MSE = 2.6, p > .05, but the main
effect of time did reach significance, F(2,12) = 9.19, MSE = .89, p< .05. The results of the main effects are qualified, however, by a significant group by time interaction, F(2,12) = 3.94, MSE = .89, p < .05. The cell means reveal that the before-after decrease in phobic intensity was greater, as predicted, for the phobia treatment group and that this group difference was maintained Page 5 at follow-up. In fact, at follow-up, the control group’s phobic intensity had nearly returned to its level at the beginning of the experiment. Chi-Square Test: The 4x3 contingency table revealed a statistically significant association between the method of treatment and the direction of clinical improvement, χ2(6, n = 80) = 21.4, p < .05. The first number in the parentheses following χ2 is the number of df associated with it; the second number is the sample size. Page 6 SUMMARY OF PARAMETRIC STATISTICS Statistic What Its Purpose Is How To Report It Mean To provide an estimate of the population from which the sample was selected. M= Standard Deviation To provide an estimate of the amount of variability/dispersion in the distribution of population scores. SD = What It Indicates Descriptive Statistics Indicates the center point of the distribution and serves as the reference point for nearly all other statistics. Indicates the variability of scores around their respective mean. Zero indicates no variability. Measures of Effect Size Cohen’s d To provide a standardized measure of an effect (defined as the difference between two means). d= Correlation To provide a measure of the association between two variables measured in a sample. r(df) = Eta-Squared To provide a standardized measure of an effect (defined as the relationship between two variables). η2 = Indicates the size of the treatment effect relative to the within-group variability of scores. Indicates the strength of the relationship between two variables and can range from –1 to +1. Indicates the proportion of variance in the dependent variable accounted for by the independent variable. . . Confidence Intervals CI for a Mean To provide an interval estimate of the population mean. Can be derived from both the z and t distributions. % CI [ , ] CI for a Mean Difference To provide an interval estimate of the population mean difference. Can be derived from both the z and t distributions. % CI [ , ] Indicates that there is the given probability that the interval specified covers the true population mean. Indicates that there is the given probability that the interval specified covers the true population mean difference. Significance Tests One-Way ANOVA To compare a single sample mean to a population mean when the population standard deviation is not known To compare two sample means when the samples are from a single-factor between-subjects design. To compare two sample means when the samples are from a single-factor within-subjects design. To compare two or more sample means when the means are from a single-factor between-subjects design. Repeated Measures ANOVA To compare two or more sample means when the means are from a single-factor within-subjects design. One Sample t Test Independent Samples t Test Related Samples t Test Factorial ANOVA t(df) = F(df1,df2) = To compare four or more groups defined by a multiple variables in a factorial research design. ,p= ,p= A small probability is obtained when the statistic is sufficiently large, indicating that the two means significantly differ from each other. . . A small probability is obtained when the statistic is sufficiently large, indicating that the set of means differ significantly from each other. Note. Many of the statistics from each of the categories are frequently and perhaps often appropriately presented in tables or figures rather than in the text. Page 7 IDNo Gender 1F 2F 20 F 3F 7F 12 M 29 F 10 M 5F 21 F 22 M 31 F 4F 32 F 6F 11 M 15 F 23 M 24 M 26 M 37 F 13 M 17 F 27 M 30 F 36 M 9M 34 F 35 F 18 F 28 M 25 M 8F 16 F 33 F 14 F 19 F MAProg MathFear 2 2 1 2 2 2 3 2 2 1 1 3 2 3 2 2 1 1 1 1 2 1 1 3 3 2 3 3 1 1 1 2 1 3 1 1 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 4.0 4.0 4.0 4.0 4.0 4.0 5.0 5.0 5.0 6.0 6.0 6.5 7.0 8.0 9.0 StatsLiking SPSSExperience DiagQuiz GREMath 1.0 0.0 95 740 8.0 5.0 790 8.0 0.0 100 800 0.0 2.0 95 740 9.0 4.0 100 800 10.0 4.0 680 9.0 2.0 95 500 9.0 2.0 90 600 4.0 3.0 100 780 5.0 3.0 100 760 7.0 2.0 100 790 8.0 4.0 95 9.0 1.5 100 670 0.0 2.5 790 1.0 4.0 80 680 5.0 4.0 80 600 5.0 2.0 95 690 0.0 1.0 95 750 8.0 3.0 100 710 8.0 3.5 95 740 5.0 2.5 690 10.0 3.0 100 780 5.0 3.0 95 750 4.0 3.0 95 670 3.0 3.0 4.0 3.0 100 710 6.0 2.0 95 780 0.0 4.0 90 700 1.0 3.0 680 2.0 2.0 85 750 6.0 0.0 100 690 5.0 0.0 75 730 0.0 3.0 100 780 0.0 0.0 95 640 2.0 3.0 700 6.0 5.0 80 2.0 0.0 90 590 Major Height 4 1 1 1 1 1 2 4 1 1 1 1 1 3 2 1 1 1 1 1 3 3 2 2 4 1 3 3 4 1 3 1 1 1 1 2 1 Age 67.00 66.00 65.00 67.00 63.00 67.00 71.00 69.00 63.00 64.00 62.00 65.00 67.00 64.00 71.00 66.00 74.00 68.00 70.00 63.00 74.00 63.00 69.00 65.00 73.00 72.00 62.00 67.00 64.00 74.00 70.00 63.00 65.00 65.00 62.00 66.00 28.00 23.00 23.00 29.00 23.00 24.00 28.00 30.00 25.00 22.00 24.00 31.00 24.00 25.00 29.00 24.00 27.00 22.00 22.00 29.00 24.00 27.00 25.00 27.00 26.00 24.00 35.00 24.00 33.00 27.00 39.00 26.00 22.00 23.00 25.00 32.00 22.00 Commute Before PrepCourse quiz After Prep Course 22.00 5 9.00 5.00 6 8.00 15.00 5 8.00 15.00 6 8.00 15.00 4 8.00 25.00 4 8.00 25.00 3 8.00 40.00 3 7.00 60.00 4 2.00 2.00 2 7.00 45.00 1 8.00 30.00 4 8.00 40.00 4 6.00 20.00 4 8.00 35.00 4 7.00 115.00 3 9.00 50.00 9 8.00 20.00 4 7.00 60.00 9 6.00 45.00 4 7.00 180.00 4 7.00 30.00 4 6.00 15.00 9 7.00 3.00 4 5.00 30.00 4 7.00 40.00 4 6.00 50.00 4 7.00 45.00 7 5.00 105.00 7 8.00 15.00 7 9.00 25.00 7 10.00 40.00 5 10.00 5.00 5 9.00 160.00 4 9.00 10.00 10 5.00 25.00 5 5.00 1 psych 2 business 3 social sciences 4 other 1 female 2 male 1 IO 2 General 3 Others Computer Assignment #1 Analyze the Class Data Set using SPSS to answer the questions below. Hand in a brief written report with relevant sections of your SPSS output pasted in. The SPSS sections for each chapter posted and given out in class will be helpful, as well as the documents on Recoding variables. Data Codes IDno: ID number This is not a variable, it just identifies the subject Gender: 1 = female 2 = male MaProgram: Masters Program = are they in Industrial/Organizational I/O, General program or Other Program I/O = 1, General =2, Others = 3 Major: = what is their undergraduate major Psych = 1, Business = 2, Social Sciences = 3, Others = 4 DiagQuiz = Diagnostic quiz score 1(low) -100 (high) assessing math skills GREMath = score on Math portion of GRE standardized test 0-1000 SPSSExperience= How much experience do you have with SPSS? Scale of 1(low) to 10 (high) MathFear = How much fear of math do you have? 0 (low) to 10 (high) StatsLiking =How much do you like Statistics? 0 (low) to 10 (high) Height = in inches Age = in years Commute = Time in minutes to commute to work BeforePrepCourse = 0 (low) to 10 (high) proficiency in statistics before taking a preparation course AfterPrepCourse = 0 (low) to 10 (high) proficiency in statistics after taking a preparation course • • • • • • • Please put a written summary in the front and all the graphs and output relevant within the text. Make sure you report APA style the means and standard deviations for each new group in questions 5 and 6 using the APA Template uploaded on Brightspace Make sure you organize your document to look neat and clean. Upload everything in ONE PDF document Do not include the raw data. Include ALL statistics at the end, regardless of if you use it in the report so I can see what you did. Change ALL labels to names in the output so when I look at the statistical output there is a label name, and not a number. 1. Clean up the data by putting data labels for the nominal variables and making sure your dataset has the correct scale of measurement for each variable (nominal, ordinal, scale). 2. Identify and name the nominal/ GROUPING variables listed above. a. Why are these called grouping variables? 3. Identify and name which variables are interval/ratio or ordinal variables (20 points). 4. Use Recode to create two new dichotomous variables from Major and MaProgram, respectively. LABEL EVERYTHING so I know which variable means which, do NOT just put 1 and 2 – change 1 the label NAMES. (20 pts) a. Create a variable from Major on which Psych majors have a value of “1,” and all others have a value of “2.” b. Create a variable from MaProg on which I/O students have a value of “1,” and all others have a value of “2. 5. Get the descriptive statistics for Diagnostic Quiz, SPSS Experience and Math Fear, but make sure you split the file by the recoded groups from question 4a so I see psych majors and non-psych majors separately. (30 points). You will have 2 groups for each variable = total of 6 that you should describe the following: a. b. c. d. e. Create frequency distributions for these data points and simple bar charts Describe each variable – mean, median, mode, standard deviation and variance. Use APA style to report the mean and standard deviations and n’s for each subgroup. Is each group normally distributed? Do psych and non-psych majors look different as far as these descriptive statistics you see? Explain what you see. 6. Get the descriptive statistics for Diagnostic Quiz, SPSS Experience and Math Fear, but make sure you split the file by groups from question 4b, so I see I/O and other non I/O MAProgram subjects separately. (30 points). You will have 2 groups for each variable = total of 6 that you should describe the following: a. Create frequency distributions for these data points and simple bar charts. b. Describe each variable – mean, median, mode, standard deviation and variance c. Use APA style to report the mean and standard deviations and n’s for each subgroup. d. Is the data normally distributed? e. Do psych and non-psych majors look different as far as these descriptive statistics you see? Explain what you see. 2