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PERSILA WORKSHOP SERIES

“Assessing the Normality, Outliers

and Extreme Cases

AHMAD ZAMRI BIN KHAIRANI

CONFERENCE ROOM SES

7th SEPTEMBER 2013

OBJECTIVES

What? Why? How?

normality

outliers

Extreme scores

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

Normality of data

Why?

Many statistical analysis techniques hold the assumption that the

distribution of scores is normal

Normality of data

How?

Skewness & Kurtosis

Kolmogorov-Smirnov Test

Shapiro-Wilk Test

Explore procedure

Normality of data

Analyze Descriptive

Statistics Explore

Dependent variable

Plot Histogram

Normality plots with tests

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…???]

Normality of data

If the value of Sig. is < .05, then the distribution is considered

normal. Here, it is not…

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.

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

?

Outliers & extreme scores

How?

Look at the bloxplot in the

Explore procedure

Outliers & extreme scores

Outliers & extreme scores

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

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.

You

Thank

PERSILA WORKSHOP SERIES

“Correlation & Regression”

AHMAD ZAMRI BIN KHAIRANI

CONFERENCE ROOM SES

7th SEPTEMBER 2013

OBJECTIVES

What? Why? How?

correlation regression

Correlation & regression

similarity

Both examine relationships

among

difference

In interpretation

Correlation

Analyze Correlate Bivariate

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)

Correlation : important notes

Correlation : important notes

Pearson

Interval vs interval

CGPA, summated

scores (???..)

Spearman

Ordinal vs ordinal

Grades,

Kendall’s tau

Non parametric

for Spearman

exercise

1. Refer to your Data1 (SPSS) file.

2. Find the correlation between CM and

BURNOUT. Interprete your findings

REGRESSION

• To see the influence of your IV towards DV

When

• More powerful than correlation

So? • Regression equation

• Variance explained, R square

What

(SIMPLE) regression

Analyze Regression

Linear Dependent

variable: BURNOUT

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……

REGRESSION

Y = 30.56 - .086X1 + 0.32X2 + …..

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

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….

(MULTIPLE) regression

Analyze Regression Linear

Independent variable: TSE

+ A + B Method: Enter

You

Thank