Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
95% Confidence Interval of the
Std. Error
F
LIFE_SAT
Equal variances assumed
Sig.
3.443
t
.065
Equal variances not assumed
df
Sig. (2-tailed)
Mean Difference
Difference
Difference
Lower
Upper
-2.500
194
.013
-2.05584
.82238
-3.67779
-.43389
-2.555
186.578
.011
-2.05584
.80473
-3.64338
-.46830
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
95% Confidence Interval of the
Std. Error
F
LIFE_SAT
Equal variances assumed
Sig.
1.085
t
.299
Equal variances not assumed
df
N
I often worry about my classes
Pearson Correlation
1.19215
-1.03610
3.66639
1.192
37.083
.241
1.31514
1.10299
-.91956
3.54984
weekends/holidays
196
-.157* --
during weekends/holidays
.028
N
196
*. Correlation is significant at the 0.05 level (2-tailed).
Upper
1.31514
--
Sig. (2-tailed)
Lower
.271
classes during
Pearson Correlation
Difference
194
I often worry about my
LIFE_SAT
Mean Difference
1.103
Correlations
LIFE_SAT
Sig. (2-tailed)
Difference
196
Correlations
Correlations
I often feel that I
am given too
I often feel that
many
my classes are
LIFE_SAT
assignments to
LIFE_SAT
LIFE_SAT
Pearson Correlation
complete.
LIFE_SAT
--
N
Pearson Correlation
--
N
196
-.201** --
I often feel that I am given
Pearson Correlation
too many assignments to
Sig. (2-tailed)
.005
complete.
N
196
196
too difficult.
196
-.198** --
I often feel that my classes
Pearson Correlation
are too difficult.
Sig. (2-tailed)
.006
N
196
196
**. Correlation is significant at the 0.01 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
I often feel
Correlations
pressure to do
LIFE_SAT
LIFE_SAT
Pearson Correlation
N
I have had a
well in school.
difficult time
--
adjusting to
196
I often feel pressure to do
Pearson Correlation
well in school.
Sig. (2-tailed)
.108
N
196
online classes
-.115 --
during the
COVID-19
196
LIFE_SAT
LIFE_SAT
Pearson Correlation
N
-196
I have had a difficult time
Pearson Correlation
adjusting to online classes
Sig. (2-tailed)
.596
during the COVID-19
N
196
pandemic.
pandemic.
-.038 --
196
Larsa Hanouka
Luciano Voutour
The total number of participants was 196 and included various genders of different ages.
There were 147 females (75% valid), 48 males (24.5% valid), and 1 (5% valid) non-binary
participant. The study involved males, females, and one non-binary participant. The ages of the
participants ranged from 19 to 53. The average age was 22.19, with a standard deviation of
3.886.
What ethnicities were involved in the study? The study involved more than eight different
ethnicities with varying frequencies. Whites appeared the most while other undisclosed
ethnicities appeared the least. The valid percentages for whites was 34.7%, African American
2.6%, Asian/Pacific Islander 13.8%, Latin/Hispanic 33.2%, Middle Eastern 3.6%, Native
American 0.5%, Biracial 8.2%, Multiracial 2.6%, other undisclosed ethnicities 1.0%.
How many introverts were detected in the study? The study included a survey of both
introverts and extroverts. Among the 196 participants, there were 114 introverts with a valid
percentage of 58.2 and 82 extroverts with a valid percentage of 41.8. The selected group of
participants was surveyed using questionnaires. The participants’ ages, genders, ethnicities, and
other data were then recorded for the study.
The DV scale (Case Processing Summary) involves 196 valid cases (N). None of the
participants was excluded from the survey; hence the scale indicates a 100% validity based on
the selected number of participants (in-text citation for the DV scale provided). Therefore, the
Satisfaction With Life Scale is highly reliable (5 items: = .828). There was one opinion question
that involved locus of control. Participants were expected to respond by indicating whether the
outcomes of their circumstances were affected by either internal or external locus of control.
Larsa Hanouka
Luciano Voutour
The demographics questions were four, including questions about age, gender, ethnicity,
and personality (introvert or extrovert). Those asked about their ethnicity, for instance, responded
by selecting either African American, White, Asian/Pacific Islander, Latin/Hispanic, Middle
Eastern, Native American, Biracial, Multiracial, or other ethnicities.
Larsa Hanouka
Luciano Voutour
Gray-Little, B., & Hafdahl, A. R. (2000). Factors influencing racial comparisons of
self-esteem: A quantitative review. Psychological Bulletin, 126, 26–54.
doi:10.1037//0033-2909.126.1.26.
Twenge, J. M., & Campbell, W. K. (2002). Self-esteem and socioeconomic status: A
meta-analytic review. Personality and Social Psychology Review, 6, 59–71.
doi:10.1207/S15327957PSPR0601_3.
Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale.
Organizational Research Methods, 4, 62–63. doi: 10.1177/109442810141004
Gray-Little, B., & Hafdahl, A. R. (2000). Factors influencing racial comparisons of
self-esteem: A quantitative review. Psychological
Judge, T. A., Locke, E. A., Durham, C. C., & Kluger, A. N. (1998). Dispositional effects on
job and life satisfaction: The role of core evaluations. Journal of Applied Psychology,
83, 17–34. doi: 10.1037/0021-9010.83.1.17
Lab 12: Results Section— APA Paper #2
-------------------------------------------------------------------------------------------------------------------Objectives for Lab 12:
● Restate your hypotheses
● Test your hypotheses with the appropriate statistic
● Run a t-test in SPSS
● Run a correlation in SPSS
-------------------------------------------------------------------------------------------------------------------Outline for Results Section
Read and complete this outline prior to starting the lab. You may write in fragments (words,
phrases) or in complete sentences. Please note that thinking about paper construction and
outlining your thoughts will aid in writing each section of your lab report, so take advantage of
this process!
Restate your group difference hypothesis
● “It was hypothesized…”
Report your results
● Describe the relationship between your two variables of interest.
● Report your statistics correctly.
● If you did not find statistically significant results…
● You are done. Stop here.
● If you found statistically significant results…
● Follow up with descriptive information elaborating further
● Follow up with a figure “showing” those results and make reference to it (e.g.,
“As seen in Figure 1…”).
Restate your correlational hypothesis
● “It was hypothesized…”
Report your results
● Describe the relationship between your two variables of interest.
● Report your statistics correctly.
● If you did not find statistically significant results…
● You are done. Stop here.
● If you found statistically significant results…
● Follow up with descriptive information elaborating further
● Follow up with a figure “showing” those results and make reference to it (e.g.,
“As seen in Figure 1…”).
***This next part is for your reference only. You will not write this part in your paper; however,
it is good practice to ask yourself these questions so that you know you are using the correct
statistics when testing your hypotheses.***
For each hypothesis…
Test statistic:
● What type of variables do you have? (continuous, categorical)
● What type of inferential statistic are you running? Why?
● What does this statistic tell us?
● How will we know if it is statistically significant?
SPSS – HOW TO RUN YOUR DATA
**In this hypothetical example, it was hypothesized the men and women (PREDICTOR) would
differ on Life Satisfaction (OUTCOME). Life Satisfaction was a composite score of 3 items,
ranging from 1 through 5. Women were coded as 1 and Men were coded as 2.***
Analyzing the data:
(t-test for group difference)
● Click on the Analyze menu, down to Compare Means, over to Independent Samples
T-Test
● Click on your participant variable (_______________) and shift it with the arrow over to
Grouping Variable box
● Click Define Groups
■ Enter 1 in the Group 1 box
● (Note: This must match the numbers you use to define the
variables in the dataset)
■ Enter 2 in the Group 2 box
● (Note: This must match the numbers you use to define the
variables in the dataset)
■ Click continue
● Click on your DV scaled score (_______________) and shift it with the arrow to Test
Variable(s) box
● Click ok
Reading the SPSS output:
(This is only sample data, do not use these numbers!!!)
Group Statistics
gende N
r
life_sat wome
15
is
n
men
15
Mean Std.
Std. Error
Deviatio Mean
n
2.133
1.18723
3
.30654
3.400
1.45406
0
.37544
APA format for reporting statistics:
For t-tests, you need to report the test statistic (t), the degrees of freedom (df) in parentheses, and
the p value or significance level.
● The format is t(df) = ___, p =.05.
● Using our table above:
● t(28) = -2.61, p =.014
We want to make a statement about the relationship between out two variables of interest (gender
and life satisfaction):
EXAMPLE: Men reported higher levels of life satisfaction (M = 3.40, SD = 1.45) compared to
women (M = 2.13, SD = 1.19), t(28) = -2.61, p =.014, as seen in Figure 1.
For significant results, we want to follow this up with specific information about that general
relationship along with a figure (bar chart, see below how to make these) that illustrates that
relationship. For the case of a t-test, you can incorporate that follow-up information in that
general statement (see above how we added the Means and Standard Deviations). Also, we
cannot infer causation from this observational study, so we do not want to use causal
language.
Making a bar chart:
(group difference)
● Click on Graphs, down to Legacy dialogs, over to Bar
● Click on Simple, Summaries for Groups of Cases, and hit Define
● Click on your group difference variable (_______________) and shift it with the arrow
over to the Category Axis box
● Click on other statistic (e.g., mean), click on your DV scaled score variable
(_______________) and shift it with the arrow over to the Variable box
● Click on ok
For nonsignificant results, you do not need follow-up information or a figure (leave these out).
NOTE: We do not say that the results were insignificant.
EXAMPLE: Men and women did not differ in their levels of life satisfaction, t(28) = -0.613, p
>.05.
SPSS – HOW TO RUN YOUR DATA
**In this hypothetical example, it was hypothesized that depression (PREDICTOR) would be
negatively correlated with life satisfaction (OUTCOME). Life Satisfaction was a composite
score of 3 items, ranging from 1 through 5. Depression was a one-item measure also ranging
from 1 through 5.***
Analyzing the data:
(r for correlation)
● Click on the Analyze menu, down to Correlate, over to Bivariate
● Click on your predictor variable (_______________) and shift it with the arrow over to
the Variables box
● Click on your outcome variable (_______________) and shift it with the arrow over to
the Variable box
● Click ok
Reading the SPSS output:
(This is only sample data, do not use these numbers!!!)
Correlations
life_sa depressi
tis
on
life_sati Pearson
s
Correlation
1.000
Sig. (2-tailed)
-.427*
.019
N
30.000
30
depressi Pearson
on
Correlation
-.427*
1.000
Sig. (2-tailed)
N
.019
30
30.000
*. Correlation is significant at the 0.05
level (2-tailed).
First thing to note, this table gives you the same information twice. That is, the information
above the diagonal is the same as that below the diagonal. Also, correlation tables in SPSS put
stars (*, **) by the statistically significant correlations-to help you read the table ☺
APA format for reporting statistics:
For r, you need to report the test statistic (r, usually rounded to two decimal places) and the p
value or significance level (rounded to three decimal places).
● The format is r = ___, p = .05.
● Using our table above:
● r = -.43, p = .019
For significant results, we want to follow this up with specific information about that general
relationship along with a figure (scatterplot, see below) that illustrates that relationship. For the
case of a t-test, you can incorporate that follow-up information in that general statement (see
above how we added the Means and Standard Deviations). Also, we cannot infer causation
from this observational study, so we do not want to use causal language.
EXAMPLE: Depression was negatively correlated with life satisfaction , such that participants
who reported higher depression reported lower levels of life satisfaction, r = -.43, p =.019 (as
seen in Figure 2).
For nonsignificant results, you do not need follow-up information or a figure (leave these out).
NOTE: We do not say that the results were insignificant.
EXAMPLE: Life satisfaction was not significantly correlated with depression, r = -.03, p
>.05.
Making a scatterplot:
(correlation)
● Click on Graphs, down to Legacy dialogs, over to Scatter/Dot
● Click on Simple Scatter and hit Define
● Click on your predictor variable (_______________) and shift it with the arrow over to
the x-axis box
● Click on your outcome variable (_______________) and shift it with the arrow over to
the y-axis box
● Click on ok
Once you have made the scatterplot...
● Double-click anywhere inside the graph (a chart editor will pop up)
● Click on Elements, down to Fit Line at Total
● This will draw the best-fitting line, representing your r-square value. You can
close the chart editor and the line will stay in your scatterplot.
Lab Report:
● Using your outline, restate your hypotheses and report the statistical tests of your
hypotheses in the order you presented them in the introduction.
● Use a heading for the Results section (remember your name and TA name).
● Make a clear statement that your results DO or DO NOT support your hypotheses
● Group differences
■ If it is significant, report descriptive information (means and standard
deviations) about the differences between your groups
● Make reference to these differences graphically, by referring to the
bar chart that was created in SPSS. Make sure to refer to the Figure
in your text.
■ If it is not significant, do not interpret your t-test. And do not reference a
bar chart (don’t include it)
● Correlation
■ If significant, follow this up with the meaning of your correlation (e.g.,
describe the strength and direction of the relationship).
● Make reference to this relationship graphically, by referring to the
scatterplot that was created in SPSS. Make sure to refer to the
Figure in your text.
■ If it is not significant, do not interpret your correlation. And do not
reference a scatterplot (don’t include it)
● Copy and paste the graphs onto their own Figure pages and write figure captions.
If none of your statistical tests were significant, you will not have any figures.
● Make sure to read the SPSS output provided for you from the TAs!
● Carefully and correctly report all test statistics.
● You must italicize p, r, t and F.
● You also must italicize M and SD.
● Save often and check to make sure you have followed all of the computer lab and
formatting rules!
Rubric for Lab 12: Results Section— APA Paper #2
Pts
poss.
Pts
earned
Item
2
Statement of hypotheses
4
Statement/Reporting of statistics
t, df, r and p correct
Support/does not support hypotheses
2
Figure(s) correct (if applicable)
Reference to figure(s) in text
Figure captions correct
2
Spell Check and Sense Check (proofread!)
Formatting: your TA will reference these mistakes and may deduct points (see WWS, pp.
94-95)
Headings correct (as applicable)
1” margins on top, bottom, and sides
Double spacing
Correct font and font size
No blank lines within text
Indented paragraphs
Name: Last name on file, submit online, and first and last in
Word file
Lab 12:
Results
PSY301 Research Methods Lab
Objectives
1
2
3
Restate
your
hypotheses
Report your
statistics
Attach
figure(s), if
applicable
Restate
Your
Hypotheses
If no changes have been
made to your hypotheses,
copy-paste them into your
results section.
If corrections were
suggested, please make
those edits.
Reporting Your Statistics
PAST TENSE
• Examine the output in order to find the following values:
GROUP DIFFERENCE (t-test):
• t value
• Degrees of freedom (df
• Two-tailed significance value (pvalue
Correlational analysis (Pearson’s r):
• r value
• Significance value (p-value
If your r value is negative, that means there
is a negative correlation
If your r value is positive, that means there
is a positive correlation.
)
.
)
)
Do your findings SUPPORT your hypotheses or not?
Attach Figure(s)
If you found statistically
significant results
supporting ONE of your
hypotheses, please
provide a figure for that.
If you found statistically
significant results
support BOTH of your
hypotheses, please
provide a figure for
EACH.
Don’t forget figure captions! Refer to Lab 4
slides for instructions on formatting.
If you did not find
statistically significant
results for EITHER of
your hypotheses, you’re
off the hook. Do not
provide any figures.
Tips!
• Read your lab manual and use it to guide your reporting of statistics. Make sure
you are following all formatting guidelines (i.e., spacing, italics, etc.).
• Double-check your work against the rubric. Have you included all required/
necessary information?
• Checked all resources and still have questions? Email your TA!
The total number of participants was 196 and included various genders of different ages.
There were 147 females (75% valid), 48 males (24.5% valid), and 1 (5% valid) non-binary
participant. The study involved males, females, and one non-binary participant. The ages of the
participants ranged from 19 to 53. The average age was 22.19, with a standard deviation of
3.886.
What ethnicities were involved in the study? The study involved more than eight different
ethnicities with varying frequencies. Whites appeared the most while other undisclosed
ethnicities appeared the least. The valid percentages for whites was 34.7%, African American
2.6%, Asian/Pacific Islander 13.8%, Latin/Hispanic 33.2%, Middle Eastern 3.6%, Native
American 0.5%, Biracial 8.2%, Multiracial 2.6%, other undisclosed ethnicities 1.0%.
How many introverts were detected in the study? The study included a survey of both
introverts and extroverts. Among the 196 participants, there were 114 introverts with a valid
percentage of 58.2 and 82 extroverts with a valid percentage of 41.8. The selected group of
participants was surveyed using questionnaires. The participants’ ages, genders, ethnicities, and
other data were then recorded for the study.
The DV scale (Case Processing Summary) involves 196 valid cases (N). None of the
participants was excluded from the survey; hence the scale indicates a 100% validity based on
the selected number of participants (in-text citation for the DV scale provided). Therefore, the
Satisfaction With Life Scale is highly reliable (5 items: = .828). There was one opinion question
that involved locus of control. Participants were expected to respond by indicating whether the
outcomes of their circumstances were affected by either internal or external locus of control.
Larsa Hanouka
Luciano Voutour
The demographics questions were four, including questions about age, gender, ethnicity,
and personality (introvert or extrovert). Those asked about their ethnicity, for instance, responded
by selecting either African American, White, Asian/Pacific Islander, Latin/Hispanic, Middle
Eastern, Native American, Biracial, Multiracial, or other ethnicities.
Larsa Hanouka
Luciano Voutour
Gray-Little, B., & Hafdahl, A. R. (2000). Factors influencing racial comparisons of
self-esteem: A quantitative review. Psychological Bulletin, 126, 26–54.
doi:10.1037//0033-2909.126.1.26.
Twenge, J. M., & Campbell, W. K. (2002). Self-esteem and socioeconomic status: A
meta-analytic review. Personality and Social Psychology Review, 6, 59–71.
doi:10.1207/S15327957PSPR0601_3.
Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale.
Organizational Research Methods, 4, 62–63. doi: 10.1177/109442810141004
Gray-Little, B., & Hafdahl, A. R. (2000). Factors influencing racial comparisons of
self-esteem: A quantitative review. Psychological
Judge, T. A., Locke, E. A., Durham, C. C., & Kluger, A. N. (1998). Dispositional effects on
job and life satisfaction: The role of core evaluations. Journal of Applied Psychology,
83, 17–34. doi: 10.1037/0021-9010.83.1.17

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