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Final Project: Test Report
Diana L. Boyd
Tests and Measurement PSYC-6315-1
Walden University
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Results
Description of Construct
When Donald Trump was elected President on November 9, 2016, he had the lowest
minority support of any winning Presidential candidate in recent decades. He won with 28
percent of the Hispanic vote and only 8 percent of the African-American vote (Eisler, 2016).
Strong opposition to any President or Presidential candidate is not unprecedented, but the high
level of African-American opposition to Donald Trump has been remarkably steadfast. A Pew
Research Center (2017) survey from February found Trump’s approval rating among blacks to
be 14%. According to a Parker, Horowitz, & Mahl, (2016). Pew Research Center (2017) survey
conducted in April, Trump’s approval rating among blacks was 14%. A June Pew Research
Center (2017) survey revealed that Trump’s approval rating had sunk to 7%. Such a consistently
high level of opposition among black Americans constitutes an alarming pattern of behavior that
merits further study.
In defining the construct, I considered the factors that could motivate African-American
resistance towards Donald Trump and his administration. I researched the literature on voting
patterns and political behavior to identify the major factors that influence political attitudes.
Demographic characteristics—age, gender, income, educational status, religious preference, and
region—are key variables in explaining political behavior in general, and thus, are incorporated
into my model of the construct (Flanigan, Zingale, & Theiss-Morse, 2014). Besides demographic
variables, attitudes towards political figures are also influenced by concerns about (1) the state of
the economy, (2) views on domestic and foreign policy, and (3) personal characteristics of the
President. Consistent with theories of political opinion formation, the construct of AfricanAmerican opposition to President Trump includes these three dimensions.
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The last element of the construct is racial anxiety. Group identity is an incredibly
powerful force in politics (Wilson, 2012). Racial identity was undoubtedly the reason why
African-Americans voted in such overwhelming numbers for Barack Obama, but the dynamics
of identity politics are usually more complex (Block, 2011). In the case of Donald Trump, the
climate of racial anxiety among African-Americans goes beyond mere identity politics. To many
African-Americans, Trump’s election represented what has been called a “white-lash” – a
backlash against black Americans and the policies of the nation’s first African-American
President (Madhani, 2016). Since the election, there has been a well-documented rise in raciallymotivated violence in America (Okeowo, 2017). Many trace these incidents of racist violence
back to Trump and his inflammatory rhetoric (Chen, 2017). Any instrument that measures this
construct must consider the effect of racial anxiety on African-American opposition.
Test Specifications
Development of the African-American Opposition to Trump Scale (AAOTS) proceeded
along two lines. The initial task was to identify factors that affect political behavior regardless of
race. After researching political behavior and voting patterns in U. S. Presidential Elections, I
identified key demographic traits that have been known to shape voting behavior. These factors
include age, gender, income, educational status, religious preference, marital status, and region.
These social factors are measured in Item 1. The remaining items represent the heart of the
AAOTS. These items measure attitudes related to Donald Trump’s personal characteristics,
stances on issues that define Trump’s agenda, concerns about the state of the economy, and
attitudes on racial issues. The American National Election Studies (2016) questionnaires served
as a model for writing items on these topics. The second line of development concentrated on the
impact of racial anxiety on African-Americans. Items were modeled after items from a Pew
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Research Center (2016) survey from June on race relations in America (Parker, Horowitz, &
Mahl,2016).
The original questionnaire contained 24 items. Before the questionnaire was finalized, it
was given to a group of seven doctoral students enrolled in the Psychology and Counseling
program at Walden University. Additionally, five faculty members at several universities with
expertise in the area of race, political behavior, and scale construction were consulted to verify
that test items accurately reflected the content domain. Items that were approved by a majority of
the experts as congruent with the content domain were retained. As a result, several items were
reworded for clarity and parsimony.
The first item on the test compiles demographic information about the participant. The
actual AAOTS consists of 20 items rated on a 5-point Likert scale with four subscales of 5 items
each - Trump’s Personality, Policy Orientation, Economic Perceptions, and Racial Anxiety.
These subscales correspond to the four factors in my model of African-American opposition to
Trump.
The test will be administered to a random sample of 385 African-Americans in the 50
states plus District of Columbia. Participants will be selected through stratified random-digit
dialing (RDD) using standard list-assisted methods. Interviewers will introduce the project to the
youngest adult living at the selected household. If that individual expresses interest, he or she
will be screened for eligibility to determine whether they self-identify as African American and
are age 18 or older. This process is carried out until the sample mirrors the population in terms of
age and gender. The initial screening consists of several generic personal political questions
including whether or not they approve or disapprove of Donald Trump’s handling of the
Presidency. Respondents who disapprove of Donald Trump will be contacted through mail and
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incentivized to participate in a face-to-face interview at which time the AAOTS questionnaire
will be given.
Test Items
African-American Opposition to President Trump Scale
Test Format: This scale consists of 21 items that assess the nature of opposition among
African-Americans who disapprove of the way President Trump is handling his job as President.
Item 1 is used to gather demographic information about the participant. The scale proper starts at
item 2. This test operates on the assumption that opposition to the President consists of four
factors – personal qualities of President, the administration’s major policies and positions,
economic perceptions, and racial anxiety. Each of these factors is measured by a separate
subscale – Trump’s Personality (#2-6), Policy Orientations (#7-11), Economic Perceptions (#1216), and Racial Anxiety (#17-21).
Demographic information:
1. Age of respondent:
Gender:
Family Income:
Educational Level:
Religion:
Region:
How well do the following phrases describe your impressions of President Trump so far?
2. He is trustworthy
3. He is well-informed
4. He is able to get things done
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5. He is a good communicator
6. He respects the country’s institutions
What is your level of support or opposition to the following policies?
7. Participation in Paris climate agreement to limit global warming
8. Tax cuts across all income levels plus businesses
9. Building a wall across the entire border with Mexico
10. The Affordable Care Act of 2010 (Healthcare act passed by Obama in 2010)
11. A temporary ban on travelers to the United States from Muslim-majority nation
How much do you agree or disagree with the following statements?
12. I am better off financially than I was a year ago
13. A year from now I expect to be better off financially
14. The economy is in a better state than it was a year ago
15. A year from now I expect the economy to be in a better state
16. There are more job opportunities than there were a year ago
How much do you agree or disagree with the following statements?
17. Race relations in America are good
18. Race relations are getting better
19. Blacks and whites have equal rights and opportunities
20. Black people are treated fairly by the police
21. Racially-motivated violence has not increased in the last year
Descriptive Statistics
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Descriptive statistics for the main variables in the study, including means and
standard deviations, are reported in Table 1. Of the 1,146 participants in the Money Data survey,
356 were male and 790 were female. 54.6% of participants were married at the time of the
survey and 45.4% were single. Participants’ mean income was $44,247 with a standard deviation
of $52,992. The mean age of participants was 32.9.
Of note, the mean value for item L6R (“I'd rather have a modest lifestyle because it's less
stressful”), which is the recoded version of L6, and item D5 (“I expect others to help me out
when I have a financial problem”) are lower than the other items. Item R6 (“Running a business
is something that I think of as interesting and exciting”) had a rather high mean value compared
to other items on questionnaire.
Factor Analysis
Factor analysis is a statistical technique in which many variables are represented by a few
clusters of variables called factors (Darlington, 2004). On a practical level, factor analysis makes
a complex set of data simpler and more manageable. Confirmatory factor analysis, which is
beyond the scope of this project, aims to compare the researcher’s theoretical model with the
observed data (Yong & Pearce, 2013). In exploratory factor analysis, the goal is simply to
discover the factors that account for the correlations within the data set. Exploratory factor
analysis will be the method we use to analyze the Money Data scale.
I carried out an exploratory factor analysis in SPSS using the Principal Axis extraction
method. I selected “Direct Oblimin” because I expected some of the factors to be correlated with
one another. I selected “Fixed number of factors” and entered “3” because I expected that there
would be three factors corresponding to the three subscales on the Money Data questionnaire.
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Table 2A shows that all the assumptions of the factor analysis have been met. The
determinant of the correlation matrix is .009, well above the cut-off of .0001. This shows that
multicollinearity is not a problem for the data and there is no need to eliminate any of the
variables. The KMO Measure of Sampling Adequacy is 0.750, which is greater than the
threshold of 0.70. Bartlett’s Test of Sphericity checks whether the correlation matrix is
significantly different than the identity matrix, in which case there would be zero correlation
between different variables (Leech, Barrett, & Morgan, 2005). The p value is <.001 and we can
reject the null hypothesis that the correlation matrix is the identity matrix.
Table 2B contains information about the amount of variance accounted for by each
factor. Out of 18 possible factors, just 6 have eigenvalues greater than 1.0. Initial eigenvalues
associated with each factor show the approximate amount of variance accounted for in terms of
the “item’s worth” of variance each factor explains (Leech, Barrett, & Morgan, 2005). Factor 3,
for example, has an initial eigenvalue of 1.985 and accounts for two items’ worth of variance.
The percentage of variance accounted for by each factor is also shown in this table. Factor 1
accounts for 19.935% of the total variance, Factor 2 accounts for 13.620%, Factor 3 accounts for
11.030%, Factor 4 accounts for 7.016%, Factor 5 accounts for 6.246%, and Factor 6 accounts for
5.827%. Those 6 factors account for a total of 63.674% of the variance. The three factors I have
extracted—Factor 1, Factor 2, and Factor 3—account for 44.585% of the variance. The sums of
square loadings after rotation are 2.671, 1.711, and 2.265 respectively.
The scree plot, displayed in Table 2C, is a graphical representation of the distribution of
variance among the factors. The location of the “elbow” of the curve is the point at which
subsequent factors contribute diminishing additional variance. There is a marked decrease in
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slope between the third and fourth factor. We could have justified a four- factor solution, but for
reasons of simplicity the three-factor solution is preferred.
The pattern matrix shown in Table 2D is the key to interpreting the factor analysis. The
pattern matrix shows that each item loads onto one of the three factors without any significant
cross-loadings. We can see from the Table 2D that items L1, L2, L3, L4, L5, and L6 are loaded
onto Factor 3. L3 has a smaller loading compared to the other items (-.342), but is within the cutoff range of 0.3-0.4. Items D1, D2, D3, D4, D5, and D6 are loaded onto Factor 2. D4 has a low
factor loading, but is above the minimum threshold. Items R1, R2, R3, R4, R5, and R6 are all
loaded onto Factor 1. R2 and R3 have quite high factor loadings. R5 is has a low factor loading
(.315), but is above the cut-off point.
Naming factors is more art than science, but attaching labels to the individual factors
allows us to assess face and construct validity, and hence, is an essential step in the process
(Yong & Pearce, 2013). Items R1-R6 all load onto Factor 1 and these items measure financial
Risk-Taking or financial aggressiveness in one way or another. I will label this factor “RiskTaking” for short. Items D1-D6 all load onto Factor 2 and this factor will be referred to as
“Dependency” because these items measure degrees of financial Dependency. Items L1-L6,
which all concern the desire for luxury or privilege, load onto Factor 3. I will therefore label this
factor “Lifestyle”. Each factor represents a subscale on the test. From this point on, each factor
will be referred to by its label.
Because I selected an oblique rotation (“Direct Oblimin”), the output generates a “Factor
Correlation Matrix”, which is included in Table 2E. The “Factor Correlation Matrix” shows how
each factor correlates with the other two factors. Roughly speaking, it quantifies how much
“overlap” there is between factors (Yong & Pearce, 2013). If two factors are highly correlated, it
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might be possible to combine them into a single factor. I expected that there some of the factor
are correlated, but not to a large degree. None of the factors are highly correlated. The largest
correlation is between Factor 1 and Factor 3 (-.293).
In conclusion, factor analysis indicates that the items compromising the psychological
test “Money Data” load onto three distinct factors. Items R1, R2, R3, R4, R5, and R6 all load
onto the Risk-Taking factor. Items D1, D2, D3, D4, D5, and D6 all load onto the Dependency
factor. Items L1, L2, L3, L4, L5, and L6 all load onto the Lifestyle factor. Despite some overlap
between the Factor 1 and 3, the three factors are distinct.
Reliability
In psychometrics, reliability is “degree to which test scores are free from error and yield
consistent results” (Thanasegaran, 2009). The basic theory of test reliability posits that observed
test scores depend on two factors – the individual’s “true score” and errors of measurement. An
individual’s true score is the characteristic that the researcher is trying to measure whereas errors
of measurement contribute to inconsistency in the observed test scores (Murphy & Davidshofer,
1988). A reliability coefficient measures the ratio of true score’s variance to the total variance of
the test scores. While the definition of the reliability coefficient is straightforward, its
interpretation is not. Several methods have been developed to estimate reliability but can be
grouped into four general categories – test-retest methods, alternate form methods, split-half
methods, and internal consistency methods. In estimating the reliability of the Money Data
questionnaire, I will be primarily concerned with internal consistency methods.
Internal consistency methods measure how well the items on a test measure the construct
under consideration (Hale & Astolfi, 2011). The most popular of these methods is Cronbach’s
coefficient alpha. This is the index of reliability I will use to assess the reliability of the Money
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Data test. Given that we have identified three subscales in our factor analysis, I will calculate
Cronbach’s alpha for the three subscales – Risk-Taking, Dependency, and Lifestyle – and then
for the test as a whole.
Cronbach’s alpha is 0.786 for the Risk-Taking scale. Coefficients with values greater
than 0.7 are considered strong so this result is very satisfactory (Thanasegaran, 2009). Table 3A
shows the value of Cronbach’s alpha if each item was deleted from the scale. We immediately
notice that the value of Cronbach’s alpha would decrease if items R1, R2, R3, R4, and R6 were
deleted, however, Cronbach’s alpha would increase if R5 were deleted. Recall from the factor
analysis section (see above) that R5 had a small loading. I would recommend that this item is
deleted from the questionnaire because it does not seem to be measuring the same construct as
the other items on the scale.
For the Lifestyle scale, Cronbach’s alpha is 0.732, which is satisfactory. Table 3B shows
that the reliability coefficient for this scale decreases if L1, L2, L4, L5, or L6 are deleted, but
increases to 0.745 if L3 is deleted. Because this item’s deletion does not represent a drastic
change in the coefficient alpha, it might just need to be rewritten. Compound items are
discouraged on questionnaires plus the inclusion of the phrase “higher quality” might be
problematic by itself. Moreover, people tend to think of the products they purchase as “higher
quality” regardless of the price. I recommend revising the item to “I tend to buy more expensive
products”.
The Cronbach’s alpha is 0.680 for the Dependency scale. There is some disagreement
over what is the lower limit for an acceptable alpha coefficient. Some authors consider a
coefficient above 0.7 to be acceptable while others recommend a lower limit of 0.65
(Thanasegaran, 2009; see also Goforth, 2015). Table 3C shows that the reliability decreases if
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any of the items are deleted. There are no weak items on the Dependency scale. Although the
reliability of the Dependency scale is not unacceptably low, it could be improved. I would
consider adding more items to the scale as test length tends to improve reliability (Cortina,
1993).
Cronbach’s alpha for the entire test was found to be 0.723. This is an acceptable level of
reliability.
Validity
Construct validity of the Money Data scale was assessed through exploratory factor
analysis (see above for more details). Three factors were retained – the Risk-Taking factor, the
Dependency factor, and the Lifestyle factor. Together they account for 44.59% of the variance in
the total variance. These three dimensions correspond to the three subscales of the test. The
subscales were significantly correlated to one another as expected, but not to the level where they
would need to be merged.
Concurrent validity was tested by examining the Pearson correlation coefficients between
the three factors retained from the factor analysis and the Financial Comfort and Relationship
Happiness subscales. This was accomplished by summing the raw scores corresponding to all
items loading on a factor and then testing the correlation coefficient for statistical significance.
Predictive validity was tested by examining the correlations between each scale and income. The
correlation matrix is shown in Table 4A.
I hypothesized that the Risk-Taking factor was negatively correlated with Relationship
Happiness. The Risk-Taking factor was tested for statistical significance and found to be
negatively correlated with Financial Happiness (r = -.091, p < .01). The level of correlation is
very weak.
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I hypothesized that the Risk-Taking factor was positively correlated with the participant’s
income. A test of statistical significance showed that there was a weak positive correlation (r
= .131, p < .01).
I hypothesized that Dependency would be negatively correlated with Financial Comfort.
A test of statistical significance showed that there was a weak positive correlation (r =- .173, p
< .01).
I anticipated that the Dependency factor would have a complicated relationship to
income. The Dependency should decrease as a participant’s income increases. On the other hand,
the magnitude of the difference between the partner’s income and a participant’s income would
seem to be the crucial factor. Because increases in the utility of income are nonlinear, measuring
income disparities between partners can be quite complicated. For example, a difference of
$20,000 in a relationship when the combined income is $30,000 is much more severe than a
disparity of $20,000 when the combined income is $150,000. To avoid these complications, I
simply examined the correlations between Dependency and the participant’s income separately
from Dependency and the partner’s income.
As stated earlier, as a participant’s income increases, Dependency should decrease. This
is born out statistically as the two variables are weakly negatively correlated (r = -.192, p < .01).
I hypothesized that the relationship between a partner’s income and Dependency should have a
positive linear relationship. This relationship was verified (r = .120, p < .01). Although this
method is somewhat simplistic, the result is what we would expect.
I hypothesized that the Lifestyle factor was positively correlated with Financial Comfort.
Naturally, the more comfortable one is financially, the greater the desire for Lifestyle. The
relationship between the Lifestyle factor and Financial Comfort was not statistically significant,
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though (r = .053, p =.073). This might be an anomaly because Lifestyle and a participant’s
income are significantly correlated (r = -0.297, p < .01).
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References
American Educational Research Association, American Psychological Association, & National
Council on Measurement in Education. (2014). Standards for educational and
psychological testing. Washington, DC: American Educational Research Association.
American National Election Studies (www.electionstudies.org). 2010. Time Series Cumulative
Data File [dataset]. Stanford University and the University of Michigan [producers and
distributors].
Block, R. (2011). Backing Barack because he's Black: Racially motivated voting in the 2008
election. Social Science Quarterly, 92(2), 423-446.
Charity, J. (2016, November 10). Why Donald Trump Terrifies Many Black Americans – The
Ringer. Retrieved July 02, 2017, from https://theringer.com/why-donald-trump-terrifiesmany-black-americans-c3cf008c78e6
Chen, M. (2017, March 24). Donald Trump's Rise Has Coincided With an Explosion of Hate
Groups. Retrieved August 03, 2017, from https://www.thenation.com/article/donaldtrumps-rise-has-coincided-with-an-explosion-of-hate-groups/
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and
applications. Journal of applied psychology, 78(1), 98.
Darlington, R. B. (2004). Factor Analysis. Retrieved June 19, 2017, from
http://node101.psych.cornell.edu/Darlington/factor.htm
Eisler, P. (2016, November 23). Trump won with lowest minority vote in decades, fueling
divisions. Reuters. Retrieved from http://reuters.com
Flanigan, W. H., Zingale, N. H., Theiss-Morse, E. A., & Wagner, M. W. (2014). Political
behavior of the American electorate. Cq Press.
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Goforth, C. (2015, November 16). Using and Interpreting Cronbach’s Alpha. Retrieved July 22,
2017, from http://data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/
Hale, C. D., & Astolfi, D. (2011). Measuring learning and performance: A primer.
Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for intermediate statistics: Use and
interpretation. Psychology Press.
Lester, P. E., & Bishop, L. K. (2000). Handbook of tests and measurement in education and the
social sciences. Lanham, Md: Scarecrow Press.
Madhani, A. (2016, November 10). Trump's victory leaves black community reeling. Retrieved
July 25, 2017, from https://www.usatoday.com/
Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2012). IBM SPSS for
introductory statistics: Use and interpretation. Routledge.
Murphy, K. R., & Davidshofer, C. O. (1988). Psychological testing. Principles, and
Applications, Englewood Cliffs.
Parker, K., Horowitz, J., & Mahl, B. (2016). On Views of Race and Inequality, Blacks and
Whites Are Worlds Apart Washington, DC: Author. Retrieved from
http://assets.pewresearch.org/wp-content/uploads/sites/5/2017/04/17171737/04-17-17Political-release.pdf
Pew Research Center. (2017). In the first months, views of Trump are strongly felt, deeply
polarized Washington, DC: Author. Retrieved from http://assets.pewresearch.org/wpcontent/uploads/sites/5/2017/02/17094915/02-16-17-Political-release.pdf
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Pew Research Center. (2017). Public has criticism of both parties, but Democrats lead on
empathy for middle class.Washington, DC: Author. Retrieved from
http://assets.pewresearch.org/wp-content/uploads/sites/5/2017/06/20110029/06-20-17Political-release-1.pdf
Public Dissatisfaction With Washington Weighs on the GOP. (n.d.). Retrieved August 06, 2017,
from http://latinosreadytovote.com/public-dissatisfaction-washington-weighs-gop/
Thanasegaran, G. (2009). Reliability and Validity Issues in Research. Integration &
Dissemination, 4.
Wilson, J. M. (2012). How are we doing? Group-based economic assessments and African
American political behavior. Electoral Studies, 31(3), 550-561.
Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory
factor analysis. Tutorials in quantitative methods for psychology, 9(2), 79-94.
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Appendix
Descriptive Statistics
Table 1
Std.
Minimum
Maximum
Mean
Deviation
Participant's income
$0
$500,000 $44,247.41 $52,992.329
Partner's income
$0
$400,000 $45,486.10 $45,414.748
Financial Comfort
1.00
2.00
1.5419
.49846
Participant's age
18
68
32.90
11.250
Partner's age
18
69
34.11
11.402
Participant's gender
1.00
2.00
1.6894
.46296
Partner's gender
1.00
2.00
1.3298
.47036
Participant's
1.00
7.00
3.8525
1.33939
Partner's education
1.00
7.00
3.5151
1.43283
I need luxury to feel
1.00
5.00
2.4433
1.04016
1.00
5.00
2.4625
1.08136
1.00
5.00
2.4930
1.14474
1.00
5.00
2.7818
1.16329
1.00
5.00
2.6073
1.16847
1.00
5.00
4.0436
1.12138
1.00
5.00
1.9564
1.12138
education
comfortable.
I enjoy having people
wait on me.
I tend to buy more
expensive, higher
quality products.
I like to give other
people the impression
that I am financially
well-off.
To tell the truth, I like
people to envy my
financial status.
I'd rather have a
modest lifestyle
because it's less
stressful.
L6R
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My partner tends to
19
1.00
5.00
2.7688
1.33535
1.00
5.00
2.3019
1.25991
1.00
5.00
2.1396
1.22052
1.00
5.00
2.3185
1.21732
1.00
5.00
1.8621
1.03907
1.00
5.00
2.8578
1.25584
1.00
5.00
3.1632
1.35508
1.00
5.00
2.4538
1.17843
1.00
5.00
2.7827
1.19858
1.00
5.00
2.4258
1.14235
be more responsible
with money than I
am.
I've never been very
good at handling
financial
responsibility
I expect my partner to
take care of me
financially.
I know that my
partner has more
financial
responsibility than I
do, so I try not to
"rock the boat."
I expect others to
help me out when I
have a financial
problem.
I'm more of a nurturer
than a provider.
I'd rather run my own
business than to work
for someone else.
I don't mind risking
large amounts of
money if there is a
good chance I can
come out ahead.
I'm willing to take
real chances to get
ahead financially.
I get bored unless I'm
taking some risks
with my career.
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Being too
20
1.00
5.00
2.8307
1.04533
1.00
5.00
3.4284
1.28295
Marital Status
.00
1.00
.5462
.49807
Relationship
1.00
5.00
3.9101
1.05968
LIFE
6.00
30.00
14.7443
4.39671
RISKTAKE
6.00
30.00
17.0846
5.02299
DEPENDENT
6.00
30.00
14.2487
4.55707
conservative with
your investments can
cause financial
problems.
Running a business is
something that I think
of as interesting and
exciting.
Happiness
Factor Analysis
Table 2A
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of
.750
Sampling Adequacy.
Bartlett's Test of
Approx. Chi-
Sphericity
Square
5335.789
df
153
Sig.
.000
Table 2B
Total Variance Explained
Rotation Sums of
Squared Loadingsa
Initial Eigenvalues
Factor
Total
% of Variance Cumulative %
Total
1
3.588
19.935
19.935
2.671
2
2.452
13.620
33.555
1.711
3
1.985
11.030
44.585
2.265
4
1.263
7.016
51.601
5
1.124
6.246
57.847
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6
1.049
5.827
63.674
7
.864
4.800
68.475
8
.779
4.328
72.803
9
.716
3.977
76.780
10
.633
3.517
80.297
11
.601
3.337
83.635
12
.550
3.053
86.687
13
.546
3.031
89.719
14
.480
2.667
92.386
15
.402
2.232
94.618
16
.348
1.932
96.550
17
.331
1.837
98.388
18
.290
1.612
100.000
Table 2C
Final Project: Test Report
22
Table 2D
Pattern Matrixa
Factor
1
I need luxury to
2
3
-.016
.082
-.648
.028
-.097
-.648
-.048
.080
-.342
.009
-.117
-.615
.047
.025
-.581
.064
.030
-.524
.060
.425
-.016
.080
.449
.023
-.115
.590
-.062
feel comfortable.
I enjoy having
people wait on me.
I tend to buy more
expensive, higher
quality products.
I like to give other
people the
impression that I
am financially
well-off.
To tell the truth, I
like people to envy
my financial status.
I'd rather have a
modest lifestyle
because it's less
stressful (recoded)
My partner tends to
be more
responsible with
money than I am.
I've never been
very good at
handling financial
responsibility
I expect my partner
to take care of me
financially.
Final Project: Test Report
I know that my
23
.024
.641
.083
.039
.367
-.104
-.138
.592
.024
.648
.069
.050
.750
-.046
-.056
.763
-.002
-.033
.585
.037
-.042
.315
-.102
-.058
.620
.075
.068
partner has more
financial
responsibility than
I do, so I try not to
"rock the boat."
I expect others to
help me out when I
have a financial
problem.
I'm more of a
nurturer than a
provider.
I'd rather run my
own business than
to work for
someone else.
I don't mind risking
large amounts of
money if there is a
good chance I can
come out ahead.
I'm willing to take
real chances to get
ahead financially.
I get bored unless
I'm taking some
risks with my
career.
Being too
conservative with
your investments
can cause financial
problems.
Running a business
is something that I
think of as
interesting and
exciting.
Final Project: Test Report
24
Extraction Method: Principal Axis Factoring.
Rotation Method: Oblimin with Kaiser
Normalization.a
a. Rotation converged in 5 iterations.
Table 2E
Factor Correlation Matrix
Factor
Risk-Taking Dependence Lifestyle
Risk-Taking
1.000
-.016
-.293
Dependence
-.016
1.000
-.122
Lifestyle
-.293
-.122
1.000
Reliability
Table 3A
Item-Total Statistics
I'd rather run my
Scale Mean if Item
Scale Variance if
Corrected Item-Total
Squared Multiple
Cronbach's Alpha if
Deleted
Item Deleted
Correlation
Correlation
Item Deleted
13.9215
17.065
.565
.473
.747
14.6309
17.410
.654
.515
.725
14.3019
17.196
.664
.535
.722
14.6588
18.768
.521
.301
.757
own business than
to work for
someone else.
I don't mind
risking large
amounts of money
if there is a good
chance I can come
out ahead.
I'm willing to take
real chances to get
ahead financially.
I get bored unless
I'm taking some
risks with my
career.
Final Project: Test Report
Being too
25
14.2539
21.383
.285
.129
.805
13.6562
17.805
.534
.450
.754
conservative with
your investments
can cause financial
problems.
Running a business
is something that I
think of as
interesting and
exciting.
Table 3B
Item-Total Statistics – Lifestyle Scale
I need luxury to
Scale Mean if Item
Scale Variance if
Corrected Item-Total
Squared Multiple
Cronbach's Alpha if
Deleted
Item Deleted
Correlation
Correlation
Item Deleted
12.3010
14.083
.534
.299
.677
12.2818
13.946
.522
.449
.679
12.2513
15.428
.288
.091
.745
11.9625
13.849
.477
.422
.692
12.1370
13.474
.524
.401
.677
12.7880
14.082
.474
.379
.693
feel comfortable.
I enjoy having
people wait on me.
I tend to buy more
expensive, higher
quality products.
I like to give other
people the
impression that I
am financially
well-off.
To tell the truth, I
like people to envy
my financial
status.
I'd rather have a
modest lifestyle
because it's less
stressful (recoded)
Table 3C
Final Project: Test Report
26
Item-Total Statistics
Scale Mean if Item
Scale Variance if
Corrected Item-Total
Squared Multiple
Cronbach's Alpha if
Deleted
Item Deleted
Correlation
Correlation
Item Deleted
I'd rather run my
13.9215
17.065
.565
.473
.747
14.6309
17.410
.654
.515
.725
14.3019
17.196
.664
.535
.722
14.6588
18.768
.521
.301
.757
14.2539
21.383
.285
.129
.805
13.6562
17.805
.534
.450
.754
own business than
to work for
someone else.
I don't mind
risking large
amounts of money
if there is a good
chance I can come
out ahead.
I'm willing to take
real chances to get
ahead financially.
I get bored unless
I'm taking some
risks with my
career.
Being too
conservative with
your investments
can cause financial
problems.
Running a business
is something that I
think of as
interesting and
exciting.
Table 4A
Correlations
Participant's income Pearson
Participant's
Partner's
income
income
1
.344
Financial
Relationship
Comfort
**
.164
happiness
**
risk
depend lifestyle
**
-.192**
.138**
.000
.000
.000
.043 .131
Correlation
Sig. (2-tailed)
.000
.000
.148
Final Project: Test Report
N
Partner's income
Pearson
27
1146
1146
1146
1146
1146
1146
1146
.344**
1
.162**
.016
.031
.120**
.029
.000
.595
.302
.000
.326
1146
1146
1146
.009 -.173**
.053
.000
.764
.000
.073
1146
1146
1146
-
-.009
.002
.002
.753
.941
Correlation
Financial Comfort
Sig. (2-tailed)
.000
N
1146
1146
1146
1146
.164**
.162**
1
.216**
Sig. (2-tailed)
.000
.000
N
1146
1146
1146
1146
.016
**
1
Pearson
Correlation
Relationship
happiness
risk
Pearson
.043
.216
Correlation
.091
**
Sig. (2-tailed)
.148
.595
.000
N
1146
1146
1146
1146
1146
1146
1146
**
1
-.011
.254**
.700
.000
**
.031
.009
Sig. (2-tailed)
.000
.302
.764
.002
N
1146
1146
1146
1146
1146
1146
1146
-.192**
.120**
-.173**
-.009
-.011
1
.087**
Sig. (2-tailed)
.000
.000
.000
.753
.700
N
1146
1146
1146
1146
1146
1146
1146
.138**
.029
.053
.002 .254**
.087**
1
Sig. (2-tailed)
.000
.326
.073
.941
.000
.003
N
1146
1146
1146
1146
1146
1146
Pearson
.131
-.091
Correlation
depend
Pearson
Correlation
lifestyle
Pearson
.003
Correlation
1146