Data Screening - Information with Category - Assessment Answer

Data Screening
Assignment
6 March 2019
876300282575lab3Genderlab3Gender male lt- Male lab3Genderlab3Gender female lt- Female
lab3Informationlab3Information info poor lt- Poor Information lab3Gender lt- as.factor(lab3Gender)
lab3Information lt- as.factor(lab3Information) summary(lab3)
00lab3Genderlab3Gender male lt- Male lab3Genderlab3Gender female lt- Female
lab3Informationlab3Information info poor lt- Poor Information lab3Gender lt- as.factor(lab3Gender)
lab3Information lt- as.factor(lab3Information) summary(lab3)
Part 1
GenderInformationlikeabilityexternalatt Female18Poor Information18Min.0.00Min.5.000 Male17Rich Information201st Qu. 76.001st Qu.7.000 NAs 3Median 83.00Median 7.000
Mean 80.21 Mean 7.289 3rd Qu. 91.50 3rd Qu.8.000 Max. 100.00 Max. 9.000
interalattovcomptselfesteemnegmood Min.3.000Min.11.00Min.0.3894Min. 2.000 1st Qu.6.0001st Qu.13.001st Qu.1.83491st Qu. 2.000
Median 7.000Median 14.00Median 3.2733Median 3.000
3rd Qu.8.000 3rd Qu.16.00 3rd Qu.4.8068 3rd Qu. 6.000 Max. 9.000 Max. 18.00 Max. 9.3132 Max. 12.000
NAs1NAs1
Gender each participants gender. There are 18 females and 3 males in the dataset, we dont have gender of 3 data-instances.
Information which category each subject was assigned. Information poor participants were only
given a few pieces of information about a person (short resume), while information rich participants were given more information about a person (long resume). The dataset assign category to all the 38 subjects
Likability each participant rated how likeable a person was given some information and a short time
talking to them (scale is in percentages). This column/feature/attribute is not out of range as it is between 0-100.
Externalatt Explicit rating of a participants attitude assessed by asking the participant how they
felt about the person on a 9 point Likert scale. This column/feature/attribute is not out of range as it is between 0-9.
Internalatt Implicit rating of a participants attitude about a person assessed by the implicit attitudes
test on a 9 point Likert scale. This column/feature/attribute is not out of range as it is between 0-9.
Ovcompt a rating scale about how much a participant thought the participant was overcompensating for a flaw on their resume on a 0-20 scale. This column/feature/attribute is not out of range as it is between 0-20, with a max of 18 and we are missing the rating for one subject.
Selfesteem an average rating of each participants self esteem on a 0-10 scale. This col-
umn/feature/attribute is not out of range as it is between 0-10 with a max of 9.3132.
Negmood a rating of how negative/positive a participant was during the fake interview where lower numbers are more positive on a 0-10 scale. This column/feature/attribute is out of range as it is
876300266065correct.indexes lt- lab3negmood lt 10 correct.indexesis.na(correct.indexes) lt- TRUE lab3 lt- lab3correct.indexes,
summary(lab3)
00correct.indexes lt- lab3negmood lt 10 correct.indexesis.na(correct.indexes) lt- TRUE lab3 lt- lab3correct.indexes,
summary(lab3)
between 2-12, however, as explained the scale is between 0-10.
GenderInformation likeabilityexternalatt Female17 Poor Information16 Min. 0.00 Min. 5.00 Male 16 Rich Information20 1st Qu. 75.50 1st Qu.7.00 NAs 3Median 85.00 Median 7.00
Mean 80.33 Mean 7.25 3rd Qu. 92.00 3rd Qu.8.00 Max. 100.00 Max. 9.00
interalattovcomptselfesteemnegmood Min.3.000Min.11.0Min.0.3894Min.2.000 1st Qu.6.0001st Qu.12.51st Qu.2.06691st Qu.2.000
Median 7.000Median 14.0Median 3.7072Median 3.000
Part 2
table(lab3Gender)
FemaleMale
1716
table(lab3Information)
Poor Information Rich Information
1620
Part 3
Everyone is not skipping the same question, it appears to be random in nature.
Part 4
(percentage.missing lt- apply(is.na(lab3),2,sum)/nrow(lab3)100)
Gender Information likeability externalattinteralattovcompt 8.3333330.0000000.0000000.0000000.0000002.777778
selfesteemnegmood 0.0000002.777778
876300267970lab3 lt- lab3apply(is.na(lab3),1,sum)gt1,
(percentage.missing lt- apply(is.na(lab3),2,sum)/nrow(lab3)100)
00lab3 lt- lab3apply(is.na(lab3),1,sum)gt1,
(percentage.missing lt- apply(is.na(lab3),2,sum)/nrow(lab3)100)
Part 5
Gender Information likeability externalattinteralattovcompt 8.3333330.0000000.0000000.0000000.0000002.777778
selfesteemnegmood 0.0000002.777778
Part 6
md.pattern(lab3)
Informatiolinkeabiliteyxternalaitnt teralasttelfesteemovcompnt egmoodGender
31 0
3 1
1 1
1 1
0 0 0 0 0 1 1 3 5
Information likeability externalatt interalatt selfesteem ovcompt
31 1 1 1 1 1 1
3 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 0
0 0 0 0 0 1
negmood Gender 31 1 1 0
3 1 0 1
1 0 1 1
1 1 1 1
1 3 5
Lets understand this table. There are 33 observations with no missing values. There are 3 observations with missing values in Gender Similarly, there are 1 missing values with negmood and so on.
876300269875lab3 lt- lab3complete.cases(lab3), nums lt- unlist(lapply(lab3, is.numeric)) numeric.lab3 lt- lab3,nums
(mahalanobis.dist lt- maha_dist(numeric.lab3))
00lab3 lt- lab3complete.cases(lab3), nums lt- unlist(lapply(lab3, is.numeric)) numeric.lab3 lt- lab3,nums
(mahalanobis.dist lt- maha_dist(numeric.lab3))
Part 7
12.33948322.46541314.72975696.85061852.09641516.1884968
129413051758850
000
129413045504105
005
131.49701203.67560004.38387326.9119436 11.08011841.3391054
197.44823596.83089770.97157825.39834994.01214217.5310089
312.8403632
8763003810000hist(mahalanobis.dist, mainMahalanobis distance, xlabdistance)
6584954748530009277354373245009283701322070Frequency
00Frequency
1294130147193010
0010
129413084645515
0015
Mahalanobis distance
183769021844000
05101520
distance
The above function returned a vector, with the same length as the number of rows of the provided data frame, corresponding to the average mahalanobis distances of each row from the whole data set.
Part 8
symnum(cor(numeric.lab3), abbr.colnames FALSE)
likeability externalatt interalatt ovcompt selfesteem negmood likeability 1
externalatt1
interalatt.1
ovcompt1
selfesteem1
negmood1
attr(,legend)
1 0 0.3 . 0.6 , 0.8 0.9 0.95 B 1
As indicated in the legend, the correlation coefficients between 0 and 0.3 are replaced by a space ( ) correlation coefficients between 0.3 and 0.6 are replace by. etc .
Part 9
The variable ovcompt is highly correlated to externalatt and interalatt.
876300267970result lt- mvn(data numeric.lab3,
mvnTest royston, univariatePlot histogram)
00result lt- mvn(data numeric.lab3,
mvnTest royston, univariatePlot histogram)
Part 10
Part 11
As seen from Figure above, negmood has a right-skewed distribution and likeability and interalatt has a left-skewed distribution whereas other variables have approximately normal distributions. Thus, we can conclude that problems with multivariate normality arise from the skewed distributions mentioned above.
876300269875result lt- mvn(data numeric.lab3,
mvnTest royston, univariatePlot qqplot,
00result lt- mvn(data numeric.lab3,
mvnTest royston, univariatePlot qqplot,
Part 12
129413031946858
008
multivariatePlotqq)
Part 13
Except for the likeabililty and self esteem, the other variables do not show multivariate normality.
In addition to the univariate plots, one can also perform univariate normality tests using the univariateTest argument in the mvn function. It provides several widely used univariate normality tests, including SW (do not apply Shapiro-Wilks test, if dataset includes more than 5000 cases or less than 3 cases.) for Shapiro-Wilk test, CVM for Cramer-von Mises test, textttLillie for Lilliefors test, SF for Shapiro-Francia test and AD Anderson-Darling test.
876300271145par(mfrowc(3,2))
plot(lm(likeability., datanumeric.lab3)residuals, type l,
mainresiduals, ylablikeability)
plot(lm(externalatt., datanumeric.lab3)residuals, type l,
mainresiduals, ylabexternalatt)
plot(lm(interalatt., datanumeric.lab3)residuals, type l,
mainresiduals, ylabinteralatt)
plot(lm(ovcompt., datanumeric.lab3)residuals, type l,
mainresiduals,
00par(mfrowc(3,2))
plot(lm(likeability., datanumeric.lab3)residuals, type l,
mainresiduals, ylablikeability)
plot(lm(externalatt., datanumeric.lab3)residuals, type l,
mainresiduals, ylabexternalatt)
plot(lm(interalatt., datanumeric.lab3)residuals, type l,
mainresiduals, ylabinteralatt)
plot(lm(ovcompt., datanumeric.lab3)residuals, type l,
mainresiduals,
Part 14
ylabovcompt)
plot(lm(selfesteem., datanumeric.lab3)residuals, type l,
mainresiduals, ylabselfesteem)
plot(lm(negmood., datanumeric.lab3)residuals, type l,
mainresiduals, ylabnegmood)
ylabovcompt)
plot(lm(selfesteem., datanumeric.lab3)residuals, type l,
mainresiduals, ylabselfesteem)
plot(lm(negmood., datanumeric.lab3)residuals, type l,
mainresiduals, ylabnegmood)

918210293370likeability
00likeability
115951032893020
0020
3890010261620externalatt
00externalatt
residualsresiduals
1348740-327660004320540-327660001159510-16446530
0030
4131310-2819401e14
001e14
051015202530051015202530
IndexIndex
918210244475interalatt
00interalatt
3890010247650ovcompt
00ovcompt
41313103568700e00
000e00
residualsresiduals
134874017018000432054017018000
051015202530051015202530
1159510-5168901e14
001e14
IndexIndex
11595103022604
004
3890010226695negmood
00negmood
residualsresiduals
1348740-327660004320540-32766000918210-448945selfesteem
00selfesteem
1159510-1841502
002
4131310-3378202 4
002 4
051015202530051015202530
IndexIndex
Part 15
All the graph indicates homogeneity.
Part 16
None of the graph indicates homoscedasticity.
Part 17
The study is all about an imaginary situation of interviewing a person for a job by the selected participants of the study. Participants were given a resume of the job candidate and allowed to talk to that candidate. Finally they were given a questionnaire with few variables stated below. Gender is a participants gender. The variable Information states the category each subject was assigned with two categories of poor and rich. Information poor participants were only given a few pieces of information about a person, while information rich participants were given more information about a person.
Part 18
The variable Likability defines participants rating of likeability of a person given some information and a short time talking to them (scale is in percentages). The variable Externalatt defines explicit rating of a participants attitude assessed by asking the participant how they felt about the person on a 9 point Likert scale. The Internalatt defines implicit rating of a participants attitude about a person assessed by the implicit attitudes test on a 9 point Likert scale. Ovcompt defines rating scale about how much a participant thought the participant was overcompensating for a flaw on their rsum on a 0-20 scale. Selfesteem defines an average rating of each participants self esteem on a 0-10 scale. Negmood defines a rating of how negative/positive a participant was during the fake interview where lower numbers are more positive on a 0-10 scale.

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