Homework #6

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					                                          Answer Sheet

1-2 are one point each; 3-9 are two points each; 10-12 are three points each; 13 is extra credit

1)     Categorical            Categorical
       or continuous?
2)     “1” on                 Night Owl
       Sleep Schedule =
3)     % Lower Class =        6.8%
4)     Strongest =            Neuroticism
5)     R2 =                   .056
6)     t-value =              -2.47
       p-value =              .01
7)     t-value =              -1.77
       p-value =              .08
8)     t-value =              -1.03
       p-value =              .31
       Meaning?               SES was unrelated to cell phone use.
9)     t-value =              3.22
       p-value =              .001
       Meaning?               People who are lower-middle class reported smoking more often than
                              people who are upper-middle class.
10)    t-value =              4.80
       p-value =              < .001
       Cohen’s d =            0.58
11)    APA-style result:      Age (r = -.13, p = .03), pro-environmental attitudes (r = .16, p = .01),
                              and anger (r = .14, p = .02) all predicted frequency of stealing.
                              However, vocabulary did not predict stealing frequency (r = -.07, p =
                              .24). Thus, people who were younger, pro-environment, or angry
                              tended to steal slightly more often than people who were not, but
                              vocabulary was unrelated to stealing. To examine the overall ability of
                              age, pro-environmental attitudes, and anger to predict stealing, a
                              multiple regression was performed. These three variables significantly
                              predicted stealing, R2 = .056, p = .001, which was a small overall effect.
                              Thus, age, pro-environmental attitudes, and anger accounted for about
                              6% of the differences in stealing frequency.
12)    APA-style result:      It was hypothesized that people who wear glasses are smarter than
                              people who do not wear glasses. However, a t-test revealed that people
                              who do not wear glasses (M = 3.47, SD = 0.46) actually had higher high
                              school GPAs than people who wear glasses (M = 3.14, SD = 0.66), d =
                              0.59, t(277) = 4.80, p < .001. Contrary to the hypothesis, people who
                              did not wear glasses had modestly better grades.
13)    Bonus (optional)       Results vary.
                                                                                Output

3)
                                21. Socioe conom ic Status (SES)

                                                                                        Cumulativ e
                                       Frequenc y        Percent      Valid Percent      Percent
 Valid      Low er Class                      19              6.8               6.8             6.8
            Low er-Middle Clas s             121             43.4              43.4           50.2
            Upper-Middle Class               135             48.4              48.4           98.6
            Upper Class                        4              1.4               1.4          100.0
            Total                            279            100.0            100.0



4)
                                                                Cor relations

                                                             83.                        90. Political       97.            104.
                                                       Self -Esteem     59. Worrying     Idealism     Attractiv eness   Neuroticism
 83. Self -Esteem           Pearson Correlation                   1           -.316**          -.042             .413**        -.472**
                            Sig. (2-tailed)                                    .000             .486             .000           .000
                            N                                     279           279               279             279            279
 59. Worrying               Pearson Correlation                 -.316**            1            .109            -.212**         .653**
                            Sig. (2-tailed)                      .000                           .069             .000           .000
                            N                                     279           279               279             279            279
 90. Political Idealism     Pearson Correlation                 -.042          .109                 1           -.075           .128*
                            Sig. (2-tailed)                      .486          .069                              .212           .033
                            N                                     279           279              279              279            279
 97. Attractiv eness        Pearson Correlation                  .413**       -.212**          -.075                1          -.221**
                            Sig. (2-tailed)                      .000          .000             .212                            .000
                            N                                     279           279              279              279            279
 104. Neurotic is m         Pearson Correlation                 -.472**        .653**           .128*           -.221**            1
                            Sig. (2-tailed)                      .000          .000             .033             .000
                            N                                     279           279              279              279            279
     **. Correlation is s ignif icant at the 0.01 level (2-tailed).
     *. Correlation is s ignif icant at the 0.05 level (2-tailed).



5)
                                                                Cor relations

                                                                                         79.
                                                                                         Pro-                              110.
                                                          62. Stealing   44. A ge    Environment     96. A nger        V oc abulary
 62. Stealing                 Pearson Correlation                    1       -.129*           .161**       .139*              -.071
                              Sig. (2-tailed)                                 .031            .007         .020                .239
                              N                                    279         279             279          279                 279
 44. A ge                     Pearson Correlation                -.129*          1           -.044        -.054                .201**
                              Sig. (2-tailed)                     .031                        .465         .369                .001
                              N                                    279         279             279          279                 279
 79. Pro-Environment          Pearson Correlation                 .161**     -.044               1         .057               -.009
                              Sig. (2-tailed)                     .007        .465                         .344                .877
                              N                                    279         279             279          279                 279
 96. A nger                   Pearson Correlation                 .139*      -.054            .057            1                .010
                              Sig. (2-tailed)                     .020        .369            .344                             .865
                              N                                    279         279             279          279                 279
 110. Vocabulary              Pearson Correlation                -.071        .201**         -.009         .010                   1
                              Sig. (2-tailed)                     .239        .001            .877         .865
                              N                                    279         279             279          279                 279
     *. Correlation is s ignif icant at the 0.05 level (2-tailed).
     **. Correlation is s ignif icant at the 0.01 level (2-tailed).
                                Model Sum m ary

                                                  Adjusted             Std. Error of
  Model               R         R Square          R Square             the Estimate
  1                    .237 a       .056               .046                   1.097
       a. Predictors: (Constant), 96. Anger, 44. Age, 79.
          Pro-Env ironment



                                                         ANOVAb

                                    Sum of
  Model                             Squares               df             Mean Square                    F                  Sig.
  1           Regression              19.764                     3             6.588                    5.479                .001 a
              Residual               330.673                   275             1.202
              Total                  350.437                   278
       a. Predictors: (Constant), 96. Anger, 44. Age, 79. Pro-Environment
       b. Dependent Variable: 62. Stealing

6/7)
                                                               Group Statis tics

                                                                                                                                                   Std. Error
                                       16. Relationship Status                      N                Mean             Std. Deviation                 Mean
  37. Days per Week                    Single                                           113            3.27                   2.281                      .215
  Eating Breakf ast                    In a Relations hip                               166            3.99                   2.466                      .191
  107. Consc ientiousness              Single                                           113            6.04                   1.861                      .175
                                       In a Relations hip                               166            6.42                   1.749                      .136


                                                                   Inde pe nde nt Sam ples Te st

                                               Levene's Test f or
                                             Equality of V ariances                                       t-test f or Equality of Means
                                                                                                                                                      95% Conf idence
                                                                                                                                                       Interval of the
                                                                                                                        Mean        Std. Error           Dif f erence
                                                F           Sig.            t           df         Sig. (2-tailed)   Dif f erence   Dif f erence     Low er        Upper
 37. Day s per Week        Equal variances
                                                1.919          .167        -2.466            277            .014           -.720           .292        -1.294       -.145
 Eating Breakf as t        as sumed
                           Equal variances
                                                                           -2.503    252.586                .013           -.720           .287        -1.286       -.153
                           not assumed
 107. Consc ientiousness   Equal variances
                                                 .325          .569        -1.765            277            .079           -.386           .219         -.817        .045
                           as sumed
                           Equal variances
                                                                           -1.744    230.552                .083           -.386           .222         -.823        .050
                           not assumed
8/9)
                                                            Group Statis tics

                                 21. Socioeconomic                                                                                             Std. Error
                                 Status (SES)                                  N                 Mean              Std. Deviation                Mean
  57. Cell Phone Use             Low er-Middle Clas s                              121             6.84                    2.025                     .184
                                 Upper-Middle Class                                135             7.08                    1.697                     .146
  49. Smoking                    Low er-Middle Clas s                              121             3.00                    2.890                     .263
                                 Upper-Middle Class                                135             1.99                    2.086                     .180


                                                                    Inde pe nde nt Sam ples Te st

                                             Levene's Test f or
                                           Equality of V ariances                                            t-test f or Equality of Means
                                                                                                                                                             95% Conf idence
                                                                                                                                                              Interval of the
                                                                                                                           Mean        Std. Error               Dif f erence
                                              F            Sig.            t              df          Sig. (2-tailed)   Dif f erence   Dif f erence         Low er        Upper
 57. Cell Phone Use   Equal variances
                                              1.591          .208        -1.025                254              .306          -.239            .233           -.697         .220
                      as sumed
                      Equal variances
                                                                         -1.015          235.210                .311          -.239            .235           -.701         .224
                      not assumed
 49. Smoking          Equal variances
                                             25.326          .000         3.221                254              .001         1.007             .313            .392        1.623
                      as sumed
                      Equal variances
                                                                          3.166          216.055                .002         1.007             .318            .380        1.635
                      not assumed




10)
                                                           Group Statis tics

                                                                                                                                           Std. Error
                                    Wears Glass es                        N                    Mean             Std. Deviation               Mean
  41. GPA (High School)             .00                                        186              3.468                   .4634                  .0340
                                    1.00                                        93              3.141                   .6604                  .0685


                                                                     Inde pe nde nt Sam ples Te s t

                                                Levene's Test f or
                                              Equality of V ariances                                             t-test f or Equality of Means
                                                                                                                                                                95% Conf idence
                                                                                                                                                                 Interval of the
                                                                                                                               Mean          Std. Error            Dif f erence
                                                   F          Sig.             t               df         Sig. (2-tailed)   Dif f erence     Dif f erence      Low er        Upper
 41. GPA (High School)   Equal variances
                                                  16.382          .000         4.802                277            .000          .3274            .0682           .1932           .4616
                         as sumed
                         Equal variances
                                                                               4.283       138.689                 .000          .3274            .0764           .1763           .4786
                         not assumed
Homework #6
                                Due Thursday, March 27th

Begin Early: For this assignment, you will use SPSS, so plan to begin the assignment a couple
days before the deadline in case you run into computer problems or get stuck. Print it out the
night before.

A) Include a cover sheet.
B) Type your answers on the answer sheet that has been provided.
C) Attach all SPSS Output after the answer sheet.
D) Work independently and answer questions using your own words.
E) Print an extra copy for yourself so you can check your answers later.


Section 1: Review from Homework #3

Instructions: These review questions are used to increase the probability that you will remember
how to use SPSS after the course has ended. Most students will need to refer back to the
instructions in the previous computer assignment at some point:

http://www.psychmike.com/psy211/homework3.doc


1. Is Favorite Music (#32) a categorical or continuous variable?

2. What does a score of “1” on Sleep Schedule (#17) mean?

3. What percentage of people said they were “Lower Class” on the Socioeconomic Status
variable (#21)?

4. Indicate which of the following variables is most strongly correlated with self-esteem (#83):
Worrying (#59), Political Idealism (#90), Attractiveness (#97), or Neuroticism (#104).

5. Determine which of the following variables have a statistically significant correlation with
Stealing (#62): Age (#44), being Pro-Environment (#79), having Anger (#96), and Vocabulary
(#110). Take any of these variables that significantly correlate with stealing and include them in
a multiple regression. Report the R2 value.
Section 2: An Easy Example of Between-groups t-Tests

A. Overview

The between-group t-test is used when we want to see how two groups of people differ on some
continuous variable.

The t-test is similar to a z-test, except the exact value needed for statistical significance varies,
depending on sample size.

Look at the p-value to determine if a result is statistically significant. If p < .05, the difference
between groups is reliable. If not, there is no reliable difference, and we tend to ignore the result.

B. Running a t-Test

Go to the Analyze menu, point to Compare Means, and choose “Independent-Samples T Test”




In the window that pops up, we always put the independent variable (grouping or categorical
variable) in the “Grouping Variable” section of the box. In the “Test Variable(s)” box, put any
continuous dependent variables you want to examine (you can choose more than one if you like).
The analysis will tell us if the groups differ in terms of their scores on the “Test Variables”.

Try putting Smoker (#3) in the “Grouping Variable” area, and put College GPA (#42) and
Activism (#81) in the “Test Variables” section, so we can see if smokers differ on these
variables. At this point you will notice that the OK button is still gray, so we need to do one
more step.

Single-click where it says “smoker(? ?)” in the Grouping Variables area, and click on the Define
Groups button. SPSS needs you to tell it which numbers were used to describe the groups. In
the data file, we arbitrarily coded nonsmokers = 0 and smoker = 1, so type a 0 where is says
“Group 1” and a 1 where it says “Group 2”.
If you ever forget how a variable was coded, just look at the Data Guide file for help:
http://www.psychmike.com/psy211/data_guide.doc

Click the Continue button, and then the OK button to run the analysis. Your Output should look
something like this:

                                        Group Statis tics

                                                                                      Std. Error
                       3. Smoker           N           Mean         Std. Deviation      Mean
  42. GPA (college)    No                      114      3.343               .6120         .0573
                       Yes                     133      3.120               .5302         .0460
  81. Activis m        No                      125       5.91               1.540           .138
                       Yes                     154       6.11               1.462           .118



                                                                    Inde pe nde nt Sam ples Te st

                                             Levene's Test f or
                                           Equality of V ariances                                       t-test f or Equality of Means
                                                                                                                                                  95% Conf idence
                                                                                                                                                   Interval of the
                                                                                                                      Mean        Std. Error         Dif f erence
                                               F          Sig.            t           df         Sig. (2-tailed)   Dif f erence   Dif f erence   Low er        Upper
 42. GPA (college)    Equal variances
                                                .389         .533        3.074             245            .002          .2234           .0727      .0803        .3666
                      as sumed
                      Equal variances
                                                                         3.041       225.336              .003          .2234           .0735      .0786        .3682
                      not assumed
 81. A ctivis m       Equal variances
                                                .610         .436       -1.100             277            .272           -.198           .180      -.553         .157
                      as sumed
                      Equal variances
                                                                        -1.095       259.341              .275           -.198           .181      -.555         .159
                      not assumed



Using the top box, we see that smokers had a lower GPA (M = 3.12, SD = 0.53) than non-
smokers (M = 3.32, SD = 0.61). The second box tells us the t-value (3.074), the degrees of
freedom (a reference number, 245) and the p-value (.002). The t-value and degrees of freedom
are basically just used by the computer in order to calculate the p-value. We are mainly
interested in the p-value. The p-value basically tells the probability of getting this mean
difference by “chance” or sampling error. In other words, there is only about a .002 or .2%
probability we’d see a result this extreme by chance. If p < .05 (less than 5%), the result is
significant (trustworthy, reliable, not likely due to chance). Otherwise, the result is unreliable.

The groups do not differ significantly on activism.
C. Run a t-Test on Your Own

Run a t-test to see if Relationship Status (#16) is related to Days per Week Eating Breakfast
(#37) or Conscientiousness (#107). [Conscientiousness = work ethic, if you didn’t know]

6. Indicate the t-value and p-value for the relationship between relationship status and days
eating breakfast.

7. Indicate the t-value and p-value for the relationship between relationship status and
conscientiousness.


Section 3: A Modestly Difficult Example of Between-groups t-Tests

A. Overview

One weakness of the t-test is that it only allows us to see how two groups differ on some variable
(How do psych majors differ from PT majors on exercise habits?). A lot of times, we have
categorical variables with more than two categories (Psych majors vs. PT vs. history vs. English,
etc.). Later, we will learn how to handle such cases with an analysis called ANOVA. However,
there are some ways to handle these cases using the between-group t-test.

In our data file, some variables are dichotomous (two categories): #2-18, #111-124
Some have multiple categories: #19-33
The rest are continuous variables (numeric rating scales): #34-110


B. Running a t-Test for a multiple category variable

The easiest way to deal with these multiple category variables is to only run an analysis looking
at two of the categories.

Favorite Entertainment (#26) is coded as 1 = TV, 2 = Internet, 3 = Books, and 4 = Exercise.
Suppose we want to see if these categories predict differences in ACT score (#43). The t-test
only allows us to compare two groups at once, so let’s compare the TV watchers to the Book
readers.

Run a t-test using Favorite Entertainment (#26) as the categorical or grouping variable and ACT
score (#43) as the continuous or test variable. You run it just like normal, but when you hit the
Define Groups button, type in 1 and 3 for the groups to examine (telling SPSS to compare TV
watcher to the Book readers).
The Output should look something like this:

                                      Group Statis tics

                 26. Favorite                                                                  Std. Error
                 Entertainment                N               Mean         Std. Deviation        Mean
  43. ACT Sc ore TV                               67           23.93               4.590             .561
                 Books                            57           25.74               3.838             .508



                                                               Inde pe nde nt Sam ples Te s t

                                     Levene's Test f or
                                   Equality of V ariances                                         t-test f or Equality of Means
                                                                                                                                            95% Conf idence
                                                                                                                                             Interval of the
                                                                                                                Mean        Std. Error         Dif f erence
                                      F            Sig.            t            df         Sig. (2-tailed)   Dif f erence   Dif f erence   Low er        Upper
 43. A CT Sc ore Equal variances
                                      3.994            .048       -2.359             122            .020         -1.811            .768     -3.332        -.291
                 as sumed
                 Equal variances
                                                                  -2.393      121.969               .018         -1.811            .757     -3.310        -.313
                 not assumed



People who enjoy reading books (M = 25.74, SD = 3.84) scored higher than people who enjoy
watching television (M = 23.93, SD = 4.59) on the ACT, and this result was statistically
significant, t(122) = -2.36, p = .02.


C. Run a t-Test on Your Own

To determine whether Socioeconomic Status (#21) is related to Cell Phone Use (#57) or
frequency of Smoking (#49), compare the “Lower-Middle Class” to the “Upper-Middle Class”
on cell phone use and smoking.

8. Indicate the t-value and p-value for the relationship between SES and cell phone use. What
does this mean?

9. Indicate the t-value and p-value for the relationship between SES and smoking. What does
this mean?
Section 4. The Most Complex Example of Between-groups t-Tests

A. Overview

One way to handle these multiple category variables is to ignore some of the categories, as we
did in Section 3. An alternative way is to re-group the variables from a high number of
categories down to just two categories.

For example, earlier we compared TV watchers to Book readers (ignoring the people who prefer
Internet or Exercise). We could re-categorize our entertainment variable so that instead of four
groups, we lump the responses into just two groups. For example, we could compare TV
watchers to all non-TV watchers (Book readers, Internet users, and Exercisers). Alternatively,
we could compare people who like Exercise to people who are physically lazy (TV watchers,
Book readers, and Internet users).

There are many combinations:

Category 1: TV watchers                          Category 2: Book readers
ignoring Internet users and Exercisers

or

Category 1: TV watchers                          Category 2: Non-TV watchers (Book readers,
                                                 Internet users, Exercisers)

or

Category 1: Exercisers                           Category 2: Physically Lazy (Book readers,
                                                 Internet users, TV watchers)

or

Category 1: TV watchers, Internet users          Category 2: Book readers
ignoring Exercisers

How we decide to group the variables likely depends on the research question we’re interested
in. If we wanted to compare the groups on health, we might use the third option above. If we
wanted to compare them on visual acuity or vocabulary, we might use the fourth grouping.
Regardless, it can be very useful to learn how to re-classify variables
B. Re-coding Variables

Go to the Transform menu, point to Recode and choose “Into Different Variables…”




The window that pops up has a number of commands. You can use this feature to take a
continuous variable and make it categorical, to re-number variables, or to re-code them in any
number of ways. We will keep it simple, but it is a powerful tool.

Let us recode the Entertainment variable (#26) we’ve been discussing such that Exercisers will
be in one group and everybody else will get classified in a second group (lazy folks). Move the
entertainment variable to the box that says “Numeric variable  Output variable” in the middle
area of the screen. Off to the right, in the “Output Variable” section type in a name for the new
variable (something simple) in the Name area and a more detailed label in the Label area. I
chose “lazy” for the name and “Enjoy Laziness” for the label. Once you’ve typed in a name and
label for the new variable you’re making, hit the Change button right below it.




After that, click on the button called, Old and New Values. Here we will tell SPSS how to
recode the Entertainment variable into our new laziness variable. Re-coding is simple. You type
in the old value in the Old Value section on the left, the New Value on the right, and click the
Add button. Our goal is to re-code Exercise from a 4  0, re-code TV from 1  1, re-code
Internet from 2  1, and re-code Book from 3  1.
Type a 4 in the Old Value section, a 0 in the New Value section, and click the Add button.
Type a 1 in the Old Value section, a 1 in the New Value section, and click the Add button.
Type a 2 in the Old Value section, a 1 in the New Value section, and click the Add button.
Type a 3 in the Old Value section, a 1 in the New Value section, and click the Add button.




Then, click the Continue button. Then, in the original pop-up window click the OK button. This
tells SPSS to make Exercise a 0 and all of the physically lazy activities a 1. To check that you
did this correctly, you can go to Data View (the spreadsheet area with all the data) and scroll all
the way to the last variable, way off to the right. The last variable should say “lazy” and all
scores should be 0’s or 1’s.

C. Running a t-Test

Now, you can run an analysis using the new dichotomous “lazy” variable, using the procedures
already learned. Compare laziness to Physical Health (#86). You should get the following
Output, which indicate that the lazy people (coded as 1) are significantly less healthy than the
non-lazy or exercise group (coded as 0).
                                            Group Statis tics

                                                                                                       Std. Error
                            Enjoy Laz iness          N                Mean         Std. Deviation        Mean
  86. Phys ic al Health     .00                           24            8.08               1.018             .208
                            1.00                         255            6.09               2.029             .127

                                                                        Inde pe nde nt Sam ples Te st

                                                Levene's Test f or
                                              Equality of V ariances                                          t-test f or Equality of Means
                                                                                                                                                        95% Conf idence
                                                                                                                                                         Interval of the
                                                                                                                            Mean        Std. Error         Dif f erence
                                                 F             Sig.            t            df         Sig. (2-tailed)   Dif f erence   Dif f erence   Low er        Upper
 86. Phys ic al Health    Equal variances
                                                14.055           .000          4.759             277            .000          1.997            .420      1.171        2.823
                          as sumed
                          Equal variances
                                                                               8.199       42.882               .000          1.997            .244      1.506        2.488
                          not assumed
D. Re-coding and Running Your Own t-Test

Mike has to go to court and has a hypothesis that people who wear glasses seem smarter than
people who do not wear glasses, so he wears his glasses to court that day. Is there any scientific
basis to this perception? Re-code the Lenses variable (#29). Make a new variable, where one
group consists only of people who wear glasses, and the other group consists of people who wear
contact lenses or neither types of corrective lenses. Then compare the glasses-wearers to those
who don’t wear glasses in terms of High School GPA (#41).

10. Report the t-value, p-value, and Cohen’s d for this result. Cohen’s d requires a hand
calculation.


Section 5: APA-Format

A. Overview

Most researchers in the social sciences stick to a general format when writing up their results.
Below are some instructions and examples about reporting results in APA-style. Read this over,
and then answer questions 11 and 12.

Here are some examples of how to write results in APA-style. This is just a guide. If you are a
good writer, it is okay to deviate from this somewhat. Remember, p-values can be recorded
exactly (e.g. p = .013, p = .46, etc.) or by merely stating significance (p < .05), or by merely
stating non-significance (ns). It is okay to separate statistical results from the rest of the sentence
by enclosing statistics in parentheses or by using commas. For correlations, provide the r value
and p value. For regression, provide the initial correlational results, then conduct the regression,
and provide the R2 and p-value. For t-tests, provide the Cohen’s d (calculated by hand, do not
need to show hand-calculations this time), t-value, and p-value.


Correlation (Statistically Significant):

The correlation between IQ and hours of television watched was significant, r = -.35, p = .02.
That is, people who were smarter watched moderately less television.

The correlation between IQ and hours of television watched was significant, r = -.35, p < .05.
That is, people who were smarter watched moderately less television.

For correlations of magnitude < .10, we say something to the effect of “no sizeable relationship.”
For correlations of magnitude .10 to .29, say the relationship is “small” or use a related synonym.
For correlations of magnitude .30 to .49, say the relationship is “medium” or “modest” or some
other synonym. For correlations of .50 or greater, say “large” or some other synonym.
Correlation (Non-Significant):

IQ and number of hours of television watched were not significantly related, r = .08, p = .67.
Thus, one’s level of intelligence was not related to time spent watching television.

IQ and number of hours of television watched were not sizably related, r = .08, ns. Thus, one’s
level of intelligence was not related to time spent watching TV.


Multiple Regression (after discussion of correlational results):

Family stress (r = .48, p < .05), work stress (r = .56, p < .05), and school stress (r = .21, p < .05)
all significantly predicted overall life stress. However, social support did not predict level of life
stress, r = .03, ns. Thus, although social support was not related to life stress, one’s level of
school stress was slightly related, family stress was modestly related, and work stress was
strongly related to level of life stress. To examine the overall contribution of the three
significant predictors (school stress, family stress, and life stress) in accounting for life stress,
multiple regression was used. The results of the multiple regression analysis indicate that these
three predictors accounted for a large proportion of the variance in life stress, R2 = .40, p < .05.
Thus, school stress, family stress, and work stress together account for 40% of the differences in
overall life stress.


t-tests (Statistically Significant)

Males (M = 2.0, SD = 0.6) differed from females (M = 5.0, SD = 0.4) in terms of number of pairs
of shoes owned. This difference was large and statistically significant, d = 6.0, t(128) = 3.89, p <
.05. In conclusion, males tend to own fewer pairs of shoes than females.

Cohen’s d is a measure of how big the relationship is (effect size), see 10/31 PPT notes for
details. d = (M1 – M2) / s where M1 is the mean of the first group (2.0), and M2 is the mean of
the second group (5.0), and s is the average standard deviation across groups [(.6+.4)/2 = .5].
d = (2-5)/.5 = -3/.5 = -6. You can make d positive if you like , just make sure you interpret it
correctly (males are lower than females). When d has a magnitude less than .2, we say
something like “there was no relationship;” .2-.49 means “a small relationship;” .5-.79 means “a
modest relationship;” .8 or higher means “a large relationship.” There is no maximum value for
d.

The values in the parentheses after the t is the degrees of freedom for the whole sample, which is
provided by SPSS, but also equals the total sample size minus 2, that is N-2.
dftotal = df1 + df2 = (n1 – 1) + (n2 – 1) = N -2
t-tests (non-Significant)

Males (M = 2.9, SD = 0.6) did not differ from females (M = 3.1, SD = 0.4) in terms of number of
pairs of shoes owned. This difference was small and not statistically significant, d = 0.4, t(128)
= 1.12, ns. In conclusion, males tend to own about as many pairs of shoes as females.


B. Reporting Your Results

You will need to be able to report results in APA-format for the term paper, so check with me if
you are unsure how to do it.

11. Report the results of problem 5 in APA format, as best you can.

12. Report the results of problem 10 in APA format, as best you can.


C. Bonus

13. (Optional, extra points). Out of your own curiosity, conduct an interesting analysis using the
re-coding function, and report the result in APA-format.
                                          Answer Sheet

1-2 are one point each; 3-9 are two points each; 10-12 are three points each; 13 is extra credit

1)     Categorical
       or continuous?
2)     “1” on
       Sleep Schedule =
3)     % Lower Class =
4)     Strongest =
5)     R2 =
6)     t-value =
       p-value =
7)     t-value =
       p-value =
8)     t-value =
       p-value =
       Meaning?
9)     t-value =
       p-value =
       Meaning?
10)    t-value =
       p-value =
       Cohen’s d =
11)    APA-style result:
12)    APA-style result:
13)    Bonus (optional)

				
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