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Descriptive Methods
707.031: Evaluation Methodology Winter 2015/16
Eduardo Veas
what we do with the data depends on the scales
2
Measurement Scales
3
The complexity of measurements
• Nominal
• Ordinal
• Interval
• Ratio
4
Sophisticated
Crude
Nominal data
• arbitrarily assigning a code to a category or attribute: postal codes, job classifications, military ranks, gender
• mathematical manipulations are meaningless
• mutually exclusive categories
• each category is a level
• use: freq, counts, 5
Ordinal data
• ranking of an attribute
• interval between points in scale not intrinsically equal
• comparisons < or > are possible
6
Interval data
• equal distances between adjacent values, but no absolute zero
• temperature in C or F
• mean can be computed
• Likert scale data ?7
Ratio
• absolute zero
• can be operated mathematically
• time to complete, distance or velocity of cursor,
• count, normalized count (count per something)
8
Frequencies
9
Title Text
Frequency tables
• tab.courses<-as.data.frame(freq(ordered(courses)), plot=FALSE)
• CumFreq= cumsum(tab.courses[-dim(tab.courses)[1],]$Frequency)
• tab.courses$CumFreq=c(CumFreq,NA)• tab.courses
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Interpreting frequency tables
Frequency Percent CumPercent CumFreq1 2 20 20 22 3 30 50 53 4 40 90 94 1 10 100 10Total 10 100 NA NA
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Contingency Tables
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Right-handed Left-handed Total
Males 43 9 52
Females 44 4 48
Totals 87 13 100
sd
Modelling
13
Statistical models
• A model has to accurately represent the real world phenomenon.
• A model can be used to predict things about the real world.
• The degree to which a statistical model represents the data collected is called fit of the model
14
Frequency distributions
• plot observations on the x-axis and a bar showing the count per observation
• ideally observations fall symmetrically around the center
• skew and kurtosis describe abnormalities in the distributions
15
Histogram / Frequency distributions
16
Center of a distribution
• Mode: score that occurs most frequently in the dataset• it may take several values• it may change dramatically with a single added score
• Median: is the middle score (after ranking all scores)• for even nr of scores, add centric values and divide by
2 • good for ordinal, interval and ratios
• Mean: average score• can be influenced by extreme scores17
Dispersion of a distribution
• range: difference between lowest and highest score
• interquartile difference: mode + upper and lower quartiles
18
252 - 22 = 232 121 - 22 = 99
Fit of the mean
• deviance: mean - x
• sum of squared errors (SS)
• variance = SS / N-1
• stddev = sqrt(variance)19
Assumptions
20
Assumptions of parametric data
• normally distributed: sample or error in the model
• homogeneity of variance: • correlational: variance of one variable should be stable at all
levels of the other variable• groups: each sample comes from a population with same
variance
• interval data: at least interval data
• independence: the behaviour of one participant does not influence that of another21
Distributions for DLF
22
0.0
0.2
0.4
0.6
0 1 2 3 4Hygiene score on day1
Density
0.00
0.25
0.50
0.75
0 1 2 3Hygiene score on day 2
Density
0.0
0.3
0.6
0.9
1.2
0 1 2 3Hygiene score on day 3
Density
0
1
2
3
-2 0 2theoretical
sample
0
1
2
3
-3 -2 -1 0 1 2 3theoretical
sample
0
1
2
3
-2 -1 0 1 2theoretical
sample
Quantify normallity
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Different groups
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Exam histogram
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0.000
0.005
0.010
0.015
0.020
0.025
25 50 75 100exam
density
Exam histogram
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0.000
0.005
0.010
0.015
0.020
0.025
25 50 75 100exam
density
0.00
0.01
0.02
0.03
0.04
10 20 30 40 50 60 70exam
density
0.00
0.02
0.04
0.06
60 70 80 90 100exam
density
Shapiro-Wilk test
• # Shapiro-Wilk• shapiro.test(rexam$exam)
• • #if we are comparing groups, what is important
is the normallity within each group• by(rexam$exam, rexam$uni, shapiro.test)
27
Reporting Shapiro-Wilk
• A Shapiro-Wilk test on the R exam, W=0.96, proved a significant deviation from normality (p<0.05).
28
Homogeneity of variance
• Levene’s test:• leventTest(rexam$exam, rexam$uni,
center=mean)
• Reporting: for the percentage on the R exam, the variances were similar for KFU and TUG students, F(1,98)=2.09
29
Homogeneity of variance
• Levene in large datasets may give sig for small variations
• Double check Variance ratio (Hartley’s Fmax)
30
Correlations
31
Title Text
Everything is hard to begin with, but the more you practise the easier it gets
32
Relationships
• Everything is hard to begin with, but the more you practise the easier it gets
• increase in practice, increase in skill
• increase in practice, but skill remains unchanged
• increase in practice, decrease in skill33
Correlations
• Bivariate: correlation between two variables
• Partial: correlation between two variables while controlling the effect of one or more additional variables
34
Covariance
• are changes in one variable met with similar changes in the other variable
• cross product deviations= multiply deviations of the two variables
• covariance= CPD / (N-1)
35
Covariance II
• Positive: both variables vary in the same direction
• Negative: variables vary in opposite directions
• Covariance is scale dependent and cannot be generalized
36
Pearson correlation coefficient
• cov/sxsy
• Data must be at least interval
• Value between -1 and 1
• 1 -> variables positively correlated• 0 -> no linear relationship• -1 -> variables negatively correlated
37
Dataset Exams and Anxiety
• effects of exam stress and revision on exam performance
• questionnaire to assess anxiety relating to exams (EAQ)
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Enter data
• examData<-read.delim("ExamAnxiety.dat", header=TRUE)
• examData2<-examData[,c(“Exam”,"Anxiety","Revise")]
• cor(examData2)
39
Pearson correlation
• Exam Anxiety Revise• Exam 1.0000000 -0.4409934 0.3967207• Anxiety -0.4409934 1.0000000 -0.7092493• Revise 0.3967207 -0.7092493 1.0000000
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Confidence values
• rcorr(as.matrix(examData[,c(“Exam","Anxiety","Revise")]))
• Exam Anxiety Revise• Exam 0 0 • Anxiety 0 0 • Revise 0 0
41
Reporting Pearson’s CC
A Pearson correlation coefficient indicated a significant correlation between anxiety performance and time spent revising, r=-.44, p<0.01
42
Spearman’s correlation coefficient
• non parametric test
• first rank the data and then apply Pearson cc
43
Liar Dataset
• contest for storytelling the biggest lie
• 68 participants, ranking, and creativity questionnaire
44
Spearman test
• liarData=read.delim("biggestLiar.dat", header=TRUE)
• rcorr(as.matrix(liarData[,c(“Position","Creativity")]))
• Position Creativity• Position 1.00 -0.31• Creativity -0.31 1.00
45
Reporting spearman
A Spearman non-parametric correlation test indicated a significant correlation between creativity and ranking in the world’s biggest liar contest, r=-.37, p<0.001
46
Kendall’s tau non-parametric
• used for small datasets
• cor.test(liarData$Position, liarData$Creativity, alternative="less", method="kendall")
• z = -3.2252, p-value = 0.0006294• alternative hypothesis: true tau is less than 0• sample estimates:• tau • -0.3002413 47
Reporting Kendall’s test
A Kendall tau correlation coefficient indicated a correlation between creativity and performance in the World’s biggest liar contest, t=-.30, p<0.001
48
Biserial and point-biserial correlations
• one variable is dichotomous (categorical with 2 categories)
• point biserial: for discrete dichotomy (e.g., dead)
• biserial: for continuous dichotomy (e.g., pass exam)
49
Readings
• Discovering statistics using R (Andy Field, Jeremy Miles, Zoe Field)
50
R
51
Title Text
set work directory
• setwd("/new/work/directory")
• getwd()
• ls() # list the objects in the current workspace
52
packages
• install.packages(“package.name") #installing packages
• library(package.name) # loading a package
• package::function() # disambiguating functions
53
Nominal and Ordinal data
• mydata$v1 <- factor(mydata$v1,levels = c(1,2,3),labels = c("red", "blue", “green"))
• mydata$v1 <- ordered(mydata$y,levels = c(1,3, 5),labels = c("Low", "Medium", "High"))
54
Missing data
• is.na(var) #tests for missing valua/ also in rows
• mydata$v1[mydata$v1==99] <- NA # select rows where v1 is 99 and recode column v1
• x <- c(1,2,NA,3)mean(x) # returns NA mean(x, na.rm=TRUE)
• newdata <- na.omit(mydata) # spawn dataset without missing data55
Install and load packages
• install.packages(“car”); install.packages(“ggplot2”); install.packages(“pastecs”); install.packages(“psych”); install.packages(“descr”)
• library(car);library(ggplot2);library(pastecs);library(psych);library(Rcmdr);library(descr)
56
Enter data
• id<-c(1,2,3,4,5,6,7,8,9,10)• sex<-c(1,1,1,1,1,2,2,2,2,2)• courses<-c(2.0,1.0,1.0,2.0,3.0,3.0,3.0,2.0,4.0,3.0)• sex<-factor(sex, levels=c(1:2), labels=c("M", "F"))• example<-
data.frame(ID=id,Gender=sex,Courses=courses)
57
Frequency Distributions
• facebook<-c(22,40,53,57,93,98,103,108,116,121,252)
• library(modeest)• mfv(facebook)
• mean(facebook)
• median(facebook)58
Enter data
• quantile (facebook)
• IQR (facebook)
• var(facebook)
• sd(facebook)
59
describing your data
• #load meaningful data• lecturer<-read.csv(“lecturerData.csv”,
header=TRUE)
• #get statistics• stat.desc(lecturerData[,c("friends", "income")],
basic=FALSE, norm=TRUE)
60
describing your data II
• # print frequency table
• tab.friends<-as.data.frame(freq(ordered(lecturerData$friends)), plot=FALSE)
• tab.friends.cumsum<-cumsum(tab.friends[-dim(tab.friends)[1],]$Frequency)
• tab.friends$CumFreq=c(tab.friends.cumsum,NA)• tab.friends
61
Testing Normally Distributed
• Load DLF data• dlf<-read.delim("DownloadFestival.dat",
header=TRUE)
• Data about hygiene collected during a festival (3days)
62
Enter data
• hist.day1 <- ggplot (dlf, aes(day1)) + theme(legend.position = "none") + geom_histogram(aes(y = ..density..), colour="black", fill="white")+ labs(x="Hygiene score on day1", y="Density")
• hist.day1 + stat_function(fun = dnorm, args=list(mean=mean(dlf$day1,na.rm=TRUE), sd=sd(dlf$day1, na.rm = TRUE)), colour="blue", size=1)
• qqplot.day1 <-qplot(sample=dlf$day1, stat="qq")63
Plot day 1
64
0.0
0.1
0.2
0.3
0.4
0.5
0 5 10 15 20Hygiene score on day1
Density
Offending score
• # print bad score• dlf$day1[dlf$day1>5]
• #correct bad score• dlf$day1[dlf$day1>5]<-2.02
65
0
5
10
15
20
-2 0 2theoretical
sample
Quantify normallity
• describe(cbind(dlf$day1, dlf$day2, dlf$day3))
• stat.desc(dlf[,c("day1","day2","day3")p], basic = FALSE, norm= TRUE)
66
Groups
• rexam<-read.delim("rexam.dat", header=TRUE)
• # obtain statistics for exam, computer, lectures and numeracy• round(stat.desc(rexam[,c("exam","computer","lectures","numer
acy")], basic=FALSE, norm=TRUE), digits=3)
• hist.exam <-ggplot (rexam, aes(exam)) + theme(legend.position = "none") + geom_histogram(aes(y = ..density..), colour="black", fill="white") + labs(x="exam", y="density") + stat_function(fun=dnorm, args=list(mean=mean(rexam$exam,na.rm=TRUE), sd=sd(rexam$exam, na.rm=TRUE)), colour="blue", size=1)67
Add factors
• # set uni to be a factor• rexam$uni <-factor(rexam$uni, levels = c(0:1),
labels = c("KFU", “TUG"))
• by (rexam[, c("exam", "computer", "lectures", "numeracy")], rexam$uni, stat.desc, basic=FALSE, norm = TRUE)
68
Get subsets and individual histograms
• # now we create subsets of the example datasets for each factor
• kfu<-subset(rexam, rexam$uni=="KFU")• tug<- subset(rexam, rexam$uni==“TUG")
• # now we can create histograms for each subset• hist.exam.kfu <-ggplot (kfu, aes(exam)) +
theme(legend.position = "none") + geom_histogram(aes(y = ..density..), colour="black", fill="white") + labs(x="exam", y="density") + stat_function(fun=dnorm, args=list(mean=mean(kfu$exam,na.rm=TRUE), sd=sd(kfu$exam, na.rm=TRUE)), colour="blue", size=1)69