persila workshop seriesusmpersila.weebly.com/uploads/1/7/6/5/17653075/persila_workshop... ·...
TRANSCRIPT
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PERSILA WORKSHOP SERIES
“Assessing the Normality, Outliers
and Extreme Cases
AHMAD ZAMRI BIN KHAIRANI
CONFERENCE ROOM SES
7th SEPTEMBER 2013
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OBJECTIVES
What? Why? How?
normality
outliers
Extreme scores
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Normality of data
What?
Normality is described as a symmetrical bell-shaped curve where the greatest frequency of
the scores in the middle and with the smaller frequencies
toward the extremes
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Normality of data
Why?
Many statistical analysis techniques hold the assumption that the
distribution of scores is normal
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Normality of data
How?
Skewness & Kurtosis
Kolmogorov-Smirnov Test
Shapiro-Wilk Test
Explore procedure
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Normality of data
Analyze Descriptive
Statistics Explore
Dependent variable
Plot Histogram
Normality plots with tests
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Normality of data
If the value of skewness is close to 0, then the distribution is
considered normal…. But how close is ‘close’ [ -1 to +1…???]
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Normality of data
If the value of Sig. is < .05, then the distribution is considered
normal. Here, it is not…
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exercise
1. Refer to your Data1 (SPSS) file.
2. Compute composite score for the following
variables:
Instructional Strategies (IS) :
H7, H10, H11, H17, H18, H20, H23, H24
Student Engagement (SE) :
H1, H2, H4, H6, H9, H12, H14, H22
3. Check whether the distribution of each variable
is normal or not.
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Outliers & extreme scores
What?
Score that lies apart from most of the rest of the distribution. Affects normality of distribution
Why?
The data is big
Error in key-in
?
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Outliers & extreme scores
How?
Look at the bloxplot in the
Explore procedure
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Outliers & extreme scores
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Outliers & extreme scores
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Outliers & extreme scores
Outliers
Extreme scores
Study them Check the data provide separate analysis with
and without them
Delete them If you are lazy
researcher
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exercise
1. Investigate the presence of outliers and/or
extreme scores in the IS and SE variables.
2. If the outliers and/or extreme scores are
present, conduct separate analysis and decide
whether they influence normality of the
distribution or not.
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You
Thank
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PERSILA WORKSHOP SERIES
“Correlation & Regression”
AHMAD ZAMRI BIN KHAIRANI
CONFERENCE ROOM SES
7th SEPTEMBER 2013
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OBJECTIVES
What? Why? How?
correlation regression
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Correlation & regression
similarity
Both examine relationships
among
difference
In interpretation
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Correlation
Analyze Correlate Bivariate
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Strength of relationship
Value of the Correlation
Coefficient Strength of Correlation
1 Perfect
0.7 - 0.99 Strong
0.4 - 0.69 Moderate
0.1 - 0.39 Weak
0 Zero
Dancey and Reidy's (2004)
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Correlation : important notes
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Correlation : important notes
Pearson
Interval vs interval
CGPA, summated
scores (???..)
Spearman
Ordinal vs ordinal
Grades,
Kendall’s tau
Non parametric
for Spearman
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exercise
1. Refer to your Data1 (SPSS) file.
2. Find the correlation between CM and
BURNOUT. Interprete your findings
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REGRESSION
• To see the influence of your IV towards DV
When
• More powerful than correlation
So? • Regression equation
• Variance explained, R square
What
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(SIMPLE) regression
Analyze Regression
Linear Dependent
variable: BURNOUT
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REGRESSION
0.7% of the variance in BURNOUT is explained
by CM
CM is not a good predictor of BURNOUT (the
higher percentage the better)
Find other variable as predictor……
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REGRESSION
Y = 30.56 - .086X1 + 0.32X2 + …..
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exercise
1. Refer to your Data1 (SPSS) file.
2. Compute a new variable, TSE = CM + IS + SE
3. Explain the influence of SE towards BURNOUT
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WARNING!!
WARNING!!
WARNING!!
Do not correlate or
find the influence of
DIMENSIONS of a
variables towards the
dependent variable as
your main hypothesis
testing, i.e.
Finding
relationship/influence
between CM, IS and SE
towards BURNOUT….
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(MULTIPLE) regression
Analyze Regression Linear
Independent variable: TSE
+ A + B Method: Enter
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You
Thank