[SOLVED] Chi-Square Test Assignment
VerifiedAdded on 2020/10/05
|20
|3482
|200
AI Summary
This assignment involves conducting a chi-square test to analyze the relationship between gender and different responses (rse2, rse4, rse6, and rse7) in a dataset. The crosstab results show the count of each category for male and female respondents, while the chi-square tests provide the value, degrees of freedom, asymptotic significance, and likelihood ratio values for each combination. The assignment requires careful analysis of the results to understand the relationship between gender and responses.
Contribute Materials
Your contribution can guide someone’s learning journey. Share your
documents today.
SPSS RESEARCH REPORT
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
TABLE OF CONTENTS
SECTION 1.....................................................................................................................................3
Introduction..................................................................................................................................3
Literature review..........................................................................................................................3
Hypotheses...................................................................................................................................4
Participants and procedure...........................................................................................................4
Measures......................................................................................................................................5
SECTION 2.....................................................................................................................................5
Results..........................................................................................................................................5
Discussion....................................................................................................................................6
Limitations and future directions.................................................................................................6
Conclusion...................................................................................................................................6
REFERENCES................................................................................................................................8
APPENDIX....................................................................................................................................10
1. Factor analysis.......................................................................................................................10
2. Reliability analysis.................................................................................................................13
3. Reliability test of Big Five Index...........................................................................................14
4. Reliability test for Dark Triad Dirty Dozen...........................................................................15
5. Chi-square test.......................................................................................................................16
SECTION 1.....................................................................................................................................3
Introduction..................................................................................................................................3
Literature review..........................................................................................................................3
Hypotheses...................................................................................................................................4
Participants and procedure...........................................................................................................4
Measures......................................................................................................................................5
SECTION 2.....................................................................................................................................5
Results..........................................................................................................................................5
Discussion....................................................................................................................................6
Limitations and future directions.................................................................................................6
Conclusion...................................................................................................................................6
REFERENCES................................................................................................................................8
APPENDIX....................................................................................................................................10
1. Factor analysis.......................................................................................................................10
2. Reliability analysis.................................................................................................................13
3. Reliability test of Big Five Index...........................................................................................14
4. Reliability test for Dark Triad Dirty Dozen...........................................................................15
5. Chi-square test.......................................................................................................................16
SECTION 1
Introduction
Self-esteem is considered as essential part in the psychology field. There are several
methods through which personality of the person can be measured such as, Rosenberg Self-
Esteem Scale, five scale index, dark traits etc (Reise and et.al, 2016). Rosenberg self esteem
scale (RSES) is the self esteem measuring tool that is used in scientific reports. This scale
represents feeling of the person about themselves. That supports in measuring self personalities,
there are several statements on which individual can analysis their own attribute. The present
study will describe psychometric evaluation of personality scale, it will evaluate properties of
RSES. Report will verify psychometric and convergent validity of RSES.
Literature review
As per the view of Kim and et.al, (2015) Rosenberg self esteem scale is considered as self
esteem measuring tool. In this method, individual has to answer on the bases of four point scale.
The four main points are strongly agree, agree, disagree, strongly disagree. It clearly describe
self acceptance and overall feeling of self worth. This is the method that uses 0-30 scale. If
individual has got score less than 15 that means person consists low self esteem. Each person
have better understanding about own self esteem. This scale just show better picture of current
esteem of the person in relation to others. Results of scale have strong relationship with self
esteem and outcomes in the life. Donnellan, Ackerman and Brecheen, (2016) argued that RSES
can not answer on specific personality of the person. For that use of Dark triad is the suitable
method in psychology. This method pays attention of four major personality traits: narcissism,
psychopathy, machiavellianism and sadism. This method shows that if the person is scoring high
that means there is high chances to commit crimes and create severe problems. According to
Ozsoy and et.al, (2017) RSES consists of 10 statements and through these statements' personality
or self esteem can be score properly. Whereas there are 27 statements in dark personality traits
method.
Carter and et.al, (2015) argued that in order to test personality, Big five model is
considered as most suitable method. This model includes five main elements: openness,
Introduction
Self-esteem is considered as essential part in the psychology field. There are several
methods through which personality of the person can be measured such as, Rosenberg Self-
Esteem Scale, five scale index, dark traits etc (Reise and et.al, 2016). Rosenberg self esteem
scale (RSES) is the self esteem measuring tool that is used in scientific reports. This scale
represents feeling of the person about themselves. That supports in measuring self personalities,
there are several statements on which individual can analysis their own attribute. The present
study will describe psychometric evaluation of personality scale, it will evaluate properties of
RSES. Report will verify psychometric and convergent validity of RSES.
Literature review
As per the view of Kim and et.al, (2015) Rosenberg self esteem scale is considered as self
esteem measuring tool. In this method, individual has to answer on the bases of four point scale.
The four main points are strongly agree, agree, disagree, strongly disagree. It clearly describe
self acceptance and overall feeling of self worth. This is the method that uses 0-30 scale. If
individual has got score less than 15 that means person consists low self esteem. Each person
have better understanding about own self esteem. This scale just show better picture of current
esteem of the person in relation to others. Results of scale have strong relationship with self
esteem and outcomes in the life. Donnellan, Ackerman and Brecheen, (2016) argued that RSES
can not answer on specific personality of the person. For that use of Dark triad is the suitable
method in psychology. This method pays attention of four major personality traits: narcissism,
psychopathy, machiavellianism and sadism. This method shows that if the person is scoring high
that means there is high chances to commit crimes and create severe problems. According to
Ozsoy and et.al, (2017) RSES consists of 10 statements and through these statements' personality
or self esteem can be score properly. Whereas there are 27 statements in dark personality traits
method.
Carter and et.al, (2015) argued that in order to test personality, Big five model is
considered as most suitable method. This model includes five main elements: openness,
Paraphrase This Document
Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
conscientiousness, agreeableness, extraversion and neuroticism. On the bases of all these five
elements personality of the person is being measured. As per the view of Reise and et.al, (2016)
big five personality traits is not appropriate to explain attributes in other cultures. In this respect
use of RSES model is considered as very suitable. Because by including ten statements
individual can identify score of their own personality and can measure their nature.
According to Hamby and et.al., (2016) systematically many models have been applied in
order to test personality. RESE has been found the stable tool that determines attribute of the
person. This models takes support of correlated uniqueness and latent method factors in order to
measure effects. Reise and et.al, (2016) stated that RESE is global self esteem test instrument
that is used for checking reliability and validity of score. There are number of studies have been
conducted by using this model but it is found that there is no significant gender difference.
Hypotheses
Hypothesis 1:
Null hypothesis (H0): There is no significant association between gender and RSES measures
undertaken.
Alternative hypothesis (H1): There is a significant association between gender and RSES
measures undertaken.
Hypothesis 2:
Null hypothesis (H0): There is no reliability in the measures undertaken as per RSES, Big five
index and Dark Triad Dirty Dozen.
Alternative hypothesis (H1): There is a significant reliability in the measures undertaken as per
RSES, Big five index and Dark Triad Dirty Dozen.
Participants and procedure
In the current research, the main motive is to assess or evaluate psychometric properties
of the Rosenberg Self-Esteem Scale. For meeting research objectives, survey has been conducted
on 200+ Americans via mobile. Hence, online survey methodology has been used for completing
study within the suitable time frame. Further, for evaluating the concerned issue factor analysis
elements personality of the person is being measured. As per the view of Reise and et.al, (2016)
big five personality traits is not appropriate to explain attributes in other cultures. In this respect
use of RSES model is considered as very suitable. Because by including ten statements
individual can identify score of their own personality and can measure their nature.
According to Hamby and et.al., (2016) systematically many models have been applied in
order to test personality. RESE has been found the stable tool that determines attribute of the
person. This models takes support of correlated uniqueness and latent method factors in order to
measure effects. Reise and et.al, (2016) stated that RESE is global self esteem test instrument
that is used for checking reliability and validity of score. There are number of studies have been
conducted by using this model but it is found that there is no significant gender difference.
Hypotheses
Hypothesis 1:
Null hypothesis (H0): There is no significant association between gender and RSES measures
undertaken.
Alternative hypothesis (H1): There is a significant association between gender and RSES
measures undertaken.
Hypothesis 2:
Null hypothesis (H0): There is no reliability in the measures undertaken as per RSES, Big five
index and Dark Triad Dirty Dozen.
Alternative hypothesis (H1): There is a significant reliability in the measures undertaken as per
RSES, Big five index and Dark Triad Dirty Dozen.
Participants and procedure
In the current research, the main motive is to assess or evaluate psychometric properties
of the Rosenberg Self-Esteem Scale. For meeting research objectives, survey has been conducted
on 200+ Americans via mobile. Hence, online survey methodology has been used for completing
study within the suitable time frame. Further, for evaluating the concerned issue factor analysis
has been done which provides high level of assistance in assessing latent variables or constructs.
Such procedure has been used to simplify data or reducing the number of variables in regression
analysis model. Further, for evaluating association between the concerned scale and chi-square
measure has been undertaken. This in turn helps in presenting suitable results in categorical
variables.
Measures
The present research is based on psychometric evaluation of personality scale where
assessment has been done by the researcher with respect to psychometric properties of
Rosenberg Self Esteem Scale (RSES). Hence, various measures have been adopted so as to
achieve overall outcome of the same (Cronk, 2012). These are use of Nomological network, big
five index and Dark Traid Dirty Dozen method. The process will help in making the overall
process easy so that the researcher can reach to the ultimate outcome of assessment and
objectives being framed for the research.
SECTION 2
Results
KMO test provides assistance in evaluating or identifying relationship which takes place
among the variables. Referring the tabular presentation of KMO test it can be stated that
alternative hypothesis is true because p<0.05. Thus, by taking into account such outcome or
result it can be mentioned that correlation matrix is not an identity matrix.
By doing statistical evaluation, reliability assessment, it has assessed that value of
Cronbach's Alpha account for .53 respectively. By taking into account such Cronbach's Alpha
value, it can be presented that measures pertaining to rse 2, rse 4, rse 6 and rse 7 are reliable in
nature. Hence, in the context of present study, all such measures provide suitable view of issue.
Further, it has assessed from the evaluation that big five index and Dark Traid Dirty Dozen
measure are not reliable because assessed value falls below the figure of Cronbach alpha.
Chi-square results show that significance value is higher than 0.05. On the basis of this,
no statistically significant association takes place between gender and REES measures
Such procedure has been used to simplify data or reducing the number of variables in regression
analysis model. Further, for evaluating association between the concerned scale and chi-square
measure has been undertaken. This in turn helps in presenting suitable results in categorical
variables.
Measures
The present research is based on psychometric evaluation of personality scale where
assessment has been done by the researcher with respect to psychometric properties of
Rosenberg Self Esteem Scale (RSES). Hence, various measures have been adopted so as to
achieve overall outcome of the same (Cronk, 2012). These are use of Nomological network, big
five index and Dark Traid Dirty Dozen method. The process will help in making the overall
process easy so that the researcher can reach to the ultimate outcome of assessment and
objectives being framed for the research.
SECTION 2
Results
KMO test provides assistance in evaluating or identifying relationship which takes place
among the variables. Referring the tabular presentation of KMO test it can be stated that
alternative hypothesis is true because p<0.05. Thus, by taking into account such outcome or
result it can be mentioned that correlation matrix is not an identity matrix.
By doing statistical evaluation, reliability assessment, it has assessed that value of
Cronbach's Alpha account for .53 respectively. By taking into account such Cronbach's Alpha
value, it can be presented that measures pertaining to rse 2, rse 4, rse 6 and rse 7 are reliable in
nature. Hence, in the context of present study, all such measures provide suitable view of issue.
Further, it has assessed from the evaluation that big five index and Dark Traid Dirty Dozen
measure are not reliable because assessed value falls below the figure of Cronbach alpha.
Chi-square results show that significance value is higher than 0.05. On the basis of this,
no statistically significant association takes place between gender and REES measures
undertaken. In other words, it can be depicted that self esteem level of individuals are not
affected on the basis of gender.
Discussion
There are various psychometric tests that have been available. However, in order to assess
the reliability of RSES, the results of big five matrix and Dark Traid Dirty Dozen method are
taken into consideration. It was being assessed from the results of RSES that rse 2, rse 4, rse 6
and rse 7 are reliable in nature. However, the results of other tests used in it are not reliable in
nature. Hence, it can be stated that these measures are suitable enough to evaluating the
psychometric properties of the Rosenberg Self-Esteem Scale (RSES), where research were
actually being conducted on 200 Americans (Basto and Pereira, 2012). The results also helped in
making the evaluation that the evaluation and results may differ based on age and sex of the
individual. It was able to make an evaluation that there is no difference that can be found on
personality traits of individual based on to what gender one tends to carry.
Limitations and future directions
Since, only certain measures are being used to ascertain the psychometric properties of the
Rosenberg Self-Esteem Scale (RSES). In this scenario, the ultimate results of the research may
deviate to a certain extent. Moreover, there are certain limitation of adoption of big five index
and Dark Traid Dirty Dozen method, which increases the probability that overall results obtained
may not be effective enough. The researcher can check the psychometric properties of the
Rosenberg Self-Esteem Scale (RSES) through other psychometric tests as well so as to generate
effective as well as appropriate results (Valeri and VanderWeele, 2013).
Conclusion
It can be concluded from the overall research being conducted above, that, the main
objective of investigation is to evaluate the psychometric properties of the Rosenberg Self-
Esteem Scale (RSES) (Meyers, Gamst and Guarino, 2016). The sample of 200 Americans have
been taken into consideration so as to reach out to the results based on psychometric test carried
on them. These tests are, RSES, Big Five Index and Dark Traid Dirty Dozen. The results stated
that only rse 2, rse 4, rse 6 and rse 7 are found reliable when assessed based on RSES test.
However, there is no components that has been found reliable when Big Five Index and Dark
Traid Dirty Dozen were conducted on sample. In the end, it can be stated that there is no
affected on the basis of gender.
Discussion
There are various psychometric tests that have been available. However, in order to assess
the reliability of RSES, the results of big five matrix and Dark Traid Dirty Dozen method are
taken into consideration. It was being assessed from the results of RSES that rse 2, rse 4, rse 6
and rse 7 are reliable in nature. However, the results of other tests used in it are not reliable in
nature. Hence, it can be stated that these measures are suitable enough to evaluating the
psychometric properties of the Rosenberg Self-Esteem Scale (RSES), where research were
actually being conducted on 200 Americans (Basto and Pereira, 2012). The results also helped in
making the evaluation that the evaluation and results may differ based on age and sex of the
individual. It was able to make an evaluation that there is no difference that can be found on
personality traits of individual based on to what gender one tends to carry.
Limitations and future directions
Since, only certain measures are being used to ascertain the psychometric properties of the
Rosenberg Self-Esteem Scale (RSES). In this scenario, the ultimate results of the research may
deviate to a certain extent. Moreover, there are certain limitation of adoption of big five index
and Dark Traid Dirty Dozen method, which increases the probability that overall results obtained
may not be effective enough. The researcher can check the psychometric properties of the
Rosenberg Self-Esteem Scale (RSES) through other psychometric tests as well so as to generate
effective as well as appropriate results (Valeri and VanderWeele, 2013).
Conclusion
It can be concluded from the overall research being conducted above, that, the main
objective of investigation is to evaluate the psychometric properties of the Rosenberg Self-
Esteem Scale (RSES) (Meyers, Gamst and Guarino, 2016). The sample of 200 Americans have
been taken into consideration so as to reach out to the results based on psychometric test carried
on them. These tests are, RSES, Big Five Index and Dark Traid Dirty Dozen. The results stated
that only rse 2, rse 4, rse 6 and rse 7 are found reliable when assessed based on RSES test.
However, there is no components that has been found reliable when Big Five Index and Dark
Traid Dirty Dozen were conducted on sample. In the end, it can be stated that there is no
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
significant difference has been found based on personality traits carried by the individual and
gender possessed by each one of them.
gender possessed by each one of them.
REFERENCES
Books and Journals
Basto, M. and Pereira, J.M., 2012. An SPSS R-menu for ordinal factor analysis. Journal of
statistical software. 46(4). pp.1-29.
Carter, G. L. & et.al., (2015). A Mokken analysis of the Dark Triad ‘Dirty Dozen’: Sex and age
differences in scale structures, and issues with individual items. Personality and Individual
Differences. 83. pp.185-191.
Cronk, B.C., 2012. How to use SPSS statistics: A step-by-step guide to analysis and
interpretation. Pyrczak Pub.
Donnellan, M. B., Ackerman, R. A. & Brecheen, C., (2016). Extending structural analyses of the
Rosenberg self-esteem scale to consider criterion-related validity: can composite self-esteem
scores be good enough?. Journal of personality assessment. 98(2). pp.169-177.
Hamby, T. & et.al., (2016). A meta-analysis of the reliability of free and for-pay Big Five scales.
The Journal of psychology. 150(4). pp.422-430.
Kim, C. & et.al., (2015). An Item Response Theory Analysis of Rosenberg’s Self Esteem Scale.
In Proceedings of the 2000 Academy of Marketing Science (AMS) Annual Conference (pp.
469-469). Springer, Cham.
Meyers, L.S., Gamst, G. and Guarino, A.J., 2016. Applied multivariate research: Design and
interpretation. Sage publications.
Ozsoy, E. & et.al., (2017). Reliability and validity of the Turkish versions of Dark Triad Dirty
Dozen (DTDD-T), Short Dark Triad (SD3-T), and Single Item Narcissism Scale (SINS-T).
Personality and Individual Differences. 117. pp.11-14.
Reise, S.P. & et.al., (2016). Is the bifactor model a better model or is it just better at modeling
implausible responses? Application of iteratively reweighted least squares to the Rosenberg
Self-Esteem Scale. Multivariate behavioral research, 51(6), pp.818-838.
Books and Journals
Basto, M. and Pereira, J.M., 2012. An SPSS R-menu for ordinal factor analysis. Journal of
statistical software. 46(4). pp.1-29.
Carter, G. L. & et.al., (2015). A Mokken analysis of the Dark Triad ‘Dirty Dozen’: Sex and age
differences in scale structures, and issues with individual items. Personality and Individual
Differences. 83. pp.185-191.
Cronk, B.C., 2012. How to use SPSS statistics: A step-by-step guide to analysis and
interpretation. Pyrczak Pub.
Donnellan, M. B., Ackerman, R. A. & Brecheen, C., (2016). Extending structural analyses of the
Rosenberg self-esteem scale to consider criterion-related validity: can composite self-esteem
scores be good enough?. Journal of personality assessment. 98(2). pp.169-177.
Hamby, T. & et.al., (2016). A meta-analysis of the reliability of free and for-pay Big Five scales.
The Journal of psychology. 150(4). pp.422-430.
Kim, C. & et.al., (2015). An Item Response Theory Analysis of Rosenberg’s Self Esteem Scale.
In Proceedings of the 2000 Academy of Marketing Science (AMS) Annual Conference (pp.
469-469). Springer, Cham.
Meyers, L.S., Gamst, G. and Guarino, A.J., 2016. Applied multivariate research: Design and
interpretation. Sage publications.
Ozsoy, E. & et.al., (2017). Reliability and validity of the Turkish versions of Dark Triad Dirty
Dozen (DTDD-T), Short Dark Triad (SD3-T), and Single Item Narcissism Scale (SINS-T).
Personality and Individual Differences. 117. pp.11-14.
Reise, S.P. & et.al., (2016). Is the bifactor model a better model or is it just better at modeling
implausible responses? Application of iteratively reweighted least squares to the Rosenberg
Self-Esteem Scale. Multivariate behavioral research, 51(6), pp.818-838.
Valeri, L. and VanderWeele, T.J., 2013. Mediation analysis allowing for exposure–mediator
interactions and causal interpretation: Theoretical assumptions and implementation with SAS
and SPSS macros. Psychological methods. 18(2). p.137.
interactions and causal interpretation: Theoretical assumptions and implementation with SAS
and SPSS macros. Psychological methods. 18(2). p.137.
Paraphrase This Document
Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
APPENDIX
1. Factor analysis
Correlation Matrix
rse1 rse2 rse3 rse4 rse5 rse6 rse7 rse8 rse9 rse10
Correlation
rse1 1.000 .193 -.007 .329 .036 .266 .296 .235 .193 .187
rse2 .193 1.000 -.257 -.290 -.228 -.314 -.284 -.186 -.208 -.179
rse3 -.007 -.257 1.000 -.345 .729 -.292 -.380 .520 .581 .677
rse4 .329 -.290 -.345 1.000 -.209 .704 .731 -.005 -.076 -.160
rse5 .036 -.228 .729 -.209 1.000 -.238 -.313 .475 .505 .612
rse6 .266 -.314 -.292 .704 -.238 1.000 .780 -.139 -.240 -.263
rse7 .296 -.284 -.380 .731 -.313 .780 1.000 -.131 -.183 -.286
rse8 .235 -.186 .520 -.005 .475 -.139 -.131 1.000 .621 .626
rse9 .193 -.208 .581 -.076 .505 -.240 -.183 .621 1.000 .755
rse10 .187 -.179 .677 -.160 .612 -.263 -.286 .626 .755 1.000
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .801
Bartlett's Test of Sphericity
Approx. Chi-Square 1196.257
df 45
Sig. .000
Anti-image Matrices
rse1 rse2 rse3 rse4 rse5 rse6 rse7 rse8 rse9 rse10
Anti-image
Covariance
rse1 .610 -.276 -.011 -.074 -.016 -.063 -.075 -.079 -.044 -.062
rse2 -.276 .558 .092 .080 .039 .074 .075 .027 .047 .015
rse3 -.011 .092 .308 .102 -.171 -.038 .029 -.049 -.040 -.067
rse4 -.074 .080 .102 .357 -.030 -.105 -.096 -.050 -.031 -.009
rse5 -.016 .039 -.171 -.030 .432 .007 .036 -.027 .009 -.054
rse6 -.063 .074 -.038 -.105 .007 .315 -.144 .024 .074 .003
rse7 -.075 .075 .029 -.096 .036 -.144 .293 .008 -.031 .034
1. Factor analysis
Correlation Matrix
rse1 rse2 rse3 rse4 rse5 rse6 rse7 rse8 rse9 rse10
Correlation
rse1 1.000 .193 -.007 .329 .036 .266 .296 .235 .193 .187
rse2 .193 1.000 -.257 -.290 -.228 -.314 -.284 -.186 -.208 -.179
rse3 -.007 -.257 1.000 -.345 .729 -.292 -.380 .520 .581 .677
rse4 .329 -.290 -.345 1.000 -.209 .704 .731 -.005 -.076 -.160
rse5 .036 -.228 .729 -.209 1.000 -.238 -.313 .475 .505 .612
rse6 .266 -.314 -.292 .704 -.238 1.000 .780 -.139 -.240 -.263
rse7 .296 -.284 -.380 .731 -.313 .780 1.000 -.131 -.183 -.286
rse8 .235 -.186 .520 -.005 .475 -.139 -.131 1.000 .621 .626
rse9 .193 -.208 .581 -.076 .505 -.240 -.183 .621 1.000 .755
rse10 .187 -.179 .677 -.160 .612 -.263 -.286 .626 .755 1.000
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .801
Bartlett's Test of Sphericity
Approx. Chi-Square 1196.257
df 45
Sig. .000
Anti-image Matrices
rse1 rse2 rse3 rse4 rse5 rse6 rse7 rse8 rse9 rse10
Anti-image
Covariance
rse1 .610 -.276 -.011 -.074 -.016 -.063 -.075 -.079 -.044 -.062
rse2 -.276 .558 .092 .080 .039 .074 .075 .027 .047 .015
rse3 -.011 .092 .308 .102 -.171 -.038 .029 -.049 -.040 -.067
rse4 -.074 .080 .102 .357 -.030 -.105 -.096 -.050 -.031 -.009
rse5 -.016 .039 -.171 -.030 .432 .007 .036 -.027 .009 -.054
rse6 -.063 .074 -.038 -.105 .007 .315 -.144 .024 .074 .003
rse7 -.075 .075 .029 -.096 .036 -.144 .293 .008 -.031 .034
rse8 -.079 .027 -.049 -.050 -.027 .024 .008 .518 -.099 -.073
rse9 -.044 .047 -.040 -.031 .009 .074 -.031 -.099 .367 -.158
rse10 -.062 .015 -.067 -.009 -.054 .003 .034 -.073 -.158 .307
Anti-image
Correlation
rse1 .552a -.473 -.025 -.159 -.032 -.143 -.176 -.140 -.093 -.143
rse2 -.473 .574a .221 .180 .080 .177 .185 .049 .103 .036
rse3 -.025 .221 .813a .307 -.468 -.121 .095 -.124 -.119 -.219
rse4 -.159 .180 .307 .795a -.076 -.313 -.296 -.117 -.086 -.026
rse5 -.032 .080 -.468 -.076 .859a .018 .101 -.058 .022 -.148
rse6 -.143 .177 -.121 -.313 .018 .779a -.475 .059 .218 .010
rse7 -.176 .185 .095 -.296 .101 -.475 .800a .022 -.095 .112
rse8 -.140 .049 -.124 -.117 -.058 .059 .022 .907a -.226 -.183
rse9 -.093 .103 -.119 -.086 .022 .218 -.095 -.226 .823a -.471
rse10 -.143 .036 -.219 -.026 -.148 .010 .112 -.183 -.471 .850a
a. Measures of Sampling Adequacy(MSA)
Communalities
Initial Extraction
rse1 1.000 .839
rse2 1.000 .857
rse3 1.000 .769
rse4 1.000 .794
rse5 1.000 .655
rse6 1.000 .806
rse7 1.000 .838
rse8 1.000 .651
rse9 1.000 .720
rse10 1.000 .798
Extraction Method: Principal
Component Analysis.
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.961 39.614 39.614 3.961 39.614 39.614
2 2.514 25.137 64.751 2.514 25.137 64.751
rse9 -.044 .047 -.040 -.031 .009 .074 -.031 -.099 .367 -.158
rse10 -.062 .015 -.067 -.009 -.054 .003 .034 -.073 -.158 .307
Anti-image
Correlation
rse1 .552a -.473 -.025 -.159 -.032 -.143 -.176 -.140 -.093 -.143
rse2 -.473 .574a .221 .180 .080 .177 .185 .049 .103 .036
rse3 -.025 .221 .813a .307 -.468 -.121 .095 -.124 -.119 -.219
rse4 -.159 .180 .307 .795a -.076 -.313 -.296 -.117 -.086 -.026
rse5 -.032 .080 -.468 -.076 .859a .018 .101 -.058 .022 -.148
rse6 -.143 .177 -.121 -.313 .018 .779a -.475 .059 .218 .010
rse7 -.176 .185 .095 -.296 .101 -.475 .800a .022 -.095 .112
rse8 -.140 .049 -.124 -.117 -.058 .059 .022 .907a -.226 -.183
rse9 -.093 .103 -.119 -.086 .022 .218 -.095 -.226 .823a -.471
rse10 -.143 .036 -.219 -.026 -.148 .010 .112 -.183 -.471 .850a
a. Measures of Sampling Adequacy(MSA)
Communalities
Initial Extraction
rse1 1.000 .839
rse2 1.000 .857
rse3 1.000 .769
rse4 1.000 .794
rse5 1.000 .655
rse6 1.000 .806
rse7 1.000 .838
rse8 1.000 .651
rse9 1.000 .720
rse10 1.000 .798
Extraction Method: Principal
Component Analysis.
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.961 39.614 39.614 3.961 39.614 39.614
2 2.514 25.137 64.751 2.514 25.137 64.751
3 1.251 12.506 77.257 1.251 12.506 77.257
4 .590 5.904 83.161
5 .410 4.104 87.264
6 .342 3.417 90.681
7 .302 3.017 93.698
8 .242 2.423 96.122
9 .204 2.043 98.164
10 .184 1.836 100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
4 .590 5.904 83.161
5 .410 4.104 87.264
6 .342 3.417 90.681
7 .302 3.017 93.698
8 .242 2.423 96.122
9 .204 2.043 98.164
10 .184 1.836 100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
1 2 3
rse1 .501 .766
rse2 -.109 -.546 .740
rse3 .845 .156 -.174
rse4 -.497 .739
rse5 .768 .209 -.146
rse6 -.580 .678 -.104
rse7 -.611 .681
rse8 .664 .428 .164
rse9 .751 .377 .118
rse10 .827 .321 .107
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
2. Reliability analysis
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.531 10
Item Statistics
Mean Std. Deviation N
rse1 2.9330 .78771 209
rse2 2.5311 .93038 209
rse1 .501 .766
rse2 -.109 -.546 .740
rse3 .845 .156 -.174
rse4 -.497 .739
rse5 .768 .209 -.146
rse6 -.580 .678 -.104
rse7 -.611 .681
rse8 .664 .428 .164
rse9 .751 .377 .118
rse10 .827 .321 .107
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
2. Reliability analysis
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.531 10
Item Statistics
Mean Std. Deviation N
rse1 2.9330 .78771 209
rse2 2.5311 .93038 209
rse3 3.5072 .80932 209
rse4 2.3732 1.00692 209
rse5 3.4306 .85834 209
rse6 2.4450 1.05979 209
rse7 2.3876 1.01812 209
rse8 3.0766 1.01616 209
rse9 3.1148 .93861 209
rse10 3.2153 .94896 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
rse1 26.0813 13.642 .465 .445
rse2 26.4833 19.299 -.391 .659
rse3 25.5072 14.607 .275 .493
rse4 26.6411 14.356 .209 .510
rse5 25.5837 14.235 .308 .483
rse6 26.5694 15.073 .094 .547
rse7 26.6268 15.158 .097 .544
rse8 25.9378 12.414 .491 .415
rse9 25.8995 12.908 .471 .429
rse10 25.7990 12.902 .464 .430
3. Reliability test of Big Five Index
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
rse4 2.3732 1.00692 209
rse5 3.4306 .85834 209
rse6 2.4450 1.05979 209
rse7 2.3876 1.01812 209
rse8 3.0766 1.01616 209
rse9 3.1148 .93861 209
rse10 3.2153 .94896 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
rse1 26.0813 13.642 .465 .445
rse2 26.4833 19.299 -.391 .659
rse3 25.5072 14.607 .275 .493
rse4 26.6411 14.356 .209 .510
rse5 25.5837 14.235 .308 .483
rse6 26.5694 15.073 .094 .547
rse7 26.6268 15.158 .097 .544
rse8 25.9378 12.414 .491 .415
rse9 25.8995 12.908 .471 .429
rse10 25.7990 12.902 .464 .430
3. Reliability test of Big Five Index
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.144 5
Item Statistics
Mean Std. Deviation N
bfie 3.2631 .70036 209
bfia 3.6700 .64369 209
bfic 3.4256 .51864 209
bfin 2.9289 .74152 209
bfio 3.3258 .55902 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
bfie 13.3502 1.661 .084 .092
bfia 12.9434 1.427 .300 -.182a
bfic 13.1878 1.665 .275 -.081a
bfin 13.6845 2.665 -.377 .590
bfio 13.2875 1.525 .336 -.173a
a. The value is negative due to a negative average covariance among items. This
violates reliability model assumptions. You may want to check item codings.
4. Reliability test for Dark Triad Dirty Dozen
Scale: ALL VARIABLES
Case Processing Summary
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.144 5
Item Statistics
Mean Std. Deviation N
bfie 3.2631 .70036 209
bfia 3.6700 .64369 209
bfic 3.4256 .51864 209
bfin 2.9289 .74152 209
bfio 3.3258 .55902 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
bfie 13.3502 1.661 .084 .092
bfia 12.9434 1.427 .300 -.182a
bfic 13.1878 1.665 .275 -.081a
bfin 13.6845 2.665 -.377 .590
bfio 13.2875 1.525 .336 -.173a
a. The value is negative due to a negative average covariance among items. This
violates reliability model assumptions. You may want to check item codings.
4. Reliability test for Dark Triad Dirty Dozen
Scale: ALL VARIABLES
Case Processing Summary
Paraphrase This Document
Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
N %
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.781 3
Item Statistics
Mean Std. Deviation N
ddi_mach 8.8182 3.32465 209
ddi_psyc 8.7943 3.21367 209
ddi_narc 10.8565 3.08664 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
ddi_mach 19.6507 28.998 .682 .631
ddi_psyc 19.6746 31.711 .620 .702
ddi_narc 17.6124 34.662 .558 .766
5. Chi-square test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Cases
Valid 209 100.0
Excludeda 0 .0
Total 209 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.781 3
Item Statistics
Mean Std. Deviation N
ddi_mach 8.8182 3.32465 209
ddi_psyc 8.7943 3.21367 209
ddi_narc 10.8565 3.08664 209
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
ddi_mach 19.6507 28.998 .682 .631
ddi_psyc 19.6746 31.711 .620 .702
ddi_narc 17.6124 34.662 .558 .766
5. Chi-square test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
gender * rse2 209 100.0% 0 0.0% 209 100.0%
gender * rse4 209 100.0% 0 0.0% 209 100.0%
gender * rse6 209 100.0% 0 0.0% 209 100.0%
gender * rse7 209 100.0% 0 0.0% 209 100.0%
gender * rse2
Crosstab
Count
rse2 Total
strongly agree agree disagree strongly disagree
gender male 16 16 32 9 73
female 22 32 65 17 136
Total 38 48 97 26 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 1.076a 3 .783
Likelihood Ratio 1.055 3 .788
Linear-by-Linear Association .553 1 .457
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 9.08.
gender * rse4
Crosstab
gender * rse4 209 100.0% 0 0.0% 209 100.0%
gender * rse6 209 100.0% 0 0.0% 209 100.0%
gender * rse7 209 100.0% 0 0.0% 209 100.0%
gender * rse2
Crosstab
Count
rse2 Total
strongly agree agree disagree strongly disagree
gender male 16 16 32 9 73
female 22 32 65 17 136
Total 38 48 97 26 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 1.076a 3 .783
Likelihood Ratio 1.055 3 .788
Linear-by-Linear Association .553 1 .457
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 9.08.
gender * rse4
Crosstab
Count
rse4 Total
strongly agree agree disagree strongly disagree
gender male 23 17 22 11 73
female 28 42 47 19 136
Total 51 59 69 30 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 3.613a 3 .306
Likelihood Ratio 3.564 3 .313
Linear-by-Linear Association .810 1 .368
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 10.48.
gender * rse6
Crosstab
Count
rse6 Total
strongly agree agree disagree strongly disagree
gender male 23 17 20 13 73
female 28 37 44 27 136
Total 51 54 64 40 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
rse4 Total
strongly agree agree disagree strongly disagree
gender male 23 17 22 11 73
female 28 42 47 19 136
Total 51 59 69 30 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 3.613a 3 .306
Likelihood Ratio 3.564 3 .313
Linear-by-Linear Association .810 1 .368
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 10.48.
gender * rse6
Crosstab
Count
rse6 Total
strongly agree agree disagree strongly disagree
gender male 23 17 20 13 73
female 28 37 44 27 136
Total 51 54 64 40 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
Pearson Chi-Square 3.088a 3 .378
Likelihood Ratio 3.018 3 .389
Linear-by-Linear Association 1.686 1 .194
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 13.97.
gender * rse7
Crosstab
Count
rse7 Total
strongly agree agree disagree strongly disagree
gender male 20 18 24 11 73
female 33 34 50 19 136
Total 53 52 74 30 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square .429a 3 .934
Likelihood Ratio .429 3 .934
Linear-by-Linear Association .107 1 .744
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 10.48.
Likelihood Ratio 3.018 3 .389
Linear-by-Linear Association 1.686 1 .194
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 13.97.
gender * rse7
Crosstab
Count
rse7 Total
strongly agree agree disagree strongly disagree
gender male 20 18 24 11 73
female 33 34 50 19 136
Total 53 52 74 30 209
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square .429a 3 .934
Likelihood Ratio .429 3 .934
Linear-by-Linear Association .107 1 .744
N of Valid Cases 209
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 10.48.
1 out of 20
![[object Object]](/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Flogo.6d15ce61.png&w=640&q=75){kind=small}
Your All-in-One AI-Powered Toolkit for Academic Success.
+13062052269
info@desklib.com
Available 24*7 on WhatsApp / Email
![[object Object]](/_next/static/media/star-bottom.7253800d.svg)
Unlock your academic potential
© 2024 | Zucol Services PVT LTD | All rights reserved.