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Never Ever Give Up !
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Data Analysis Basic with SPSS (PART 1)
Prof. Dr Chua Yan Piaw Institute of Educational Leadership (IEL,UM)
Unit for the Enhancement of Academic Performance (ULPA,UM)
University of Malaya
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Targets: 1. Concept of Data analysis
2. Prepare Data for Analysis
3. Checking the normality of a data
4. Establishing reliability of a questionnaire
5. Descriptive analysis
6. Inferential analysis
7. Analyse data and report the data
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Research Methods and Statistics Reference Books
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Volume 1: Research Methods
Chapter 1: Introduction to Research
Chapter 2: Research Ethics
Chapter 3: Literature Review
Chapter 4: Research Design
Chapter 5: Experimental Study
Chapter 6: Quasi-experimental Study
Chapter 7: Survey Study
Chapter 8: Field Study
Chapter 9: Case Study
Chapter 10: Action Study
Chapter 11: Historical Study
Chapter 12: Probability Sampling Procedures
Chapter 13: Non-probability Sampling Procedures
Chapter 14: Measurement in Research
Chapter 15: Index, Scales and Specific Measurement
Procedures
Chapter 16: Pilot Study
Chapter 17: Research Instrumentation
Chapter 18: Format of Writing Research Report
2006 2011
2015
Volume 2: Fundamental Research Statistics
Chapter 1: Descriptive Statistics
Chapter 2: Inferential Statistics and
Significance Test
Chapter 3: Qualitative Data Analysis
Chapter 4: Data Preparation for SPSS
Program
Chapter 5: Reliability of Research
Instrument
Chapter 6: Chi-Square Tests
Chapter 7: T Tests
Chapter 8: ANOVA Tests
Chapter 9: Correlation Tests
Chapter 10: Multiple Regressions
Chapter 11: Reporting the Results of Data
Analysis Based on the APA Format
2012
2006
2015
Volume 3 (2nd edition):
Fundamental Research Statistics: Data Analysis for Likert Scale
Chapter 1: Measurement Scales and
Statistical Test
Chapter 2: Data Preparation for SPSS
Program
Chapter 3: Data Transformation
Chapter 4: Mann-Whitney U Test
Chapter 5: Wilcoxon T Test
Chapter 6: Kruskal-Wallis H Test
Chapter 7: Friedman Test
Chapter 8: Spearman Correlation Test
Chapter 9: Contingency Table Data Analysis
Chapter 10: Cramer V Correlation Test Chapter 11: Reporting the Results of Data Analysis
Based on the APA Format
2013
2008
Volume 4: Advanced Research Statistics:
Univariate and Multivariate Tests
Chapter 1: Research Statistics Concept and
Data Preparation for SPSS Program
Chapter 2: One-Way ANOVA Test
Chapter 3: Two-Way ANOVA Test
Chapter 4: SPANOVA Test
Chapter 5: ANCOVA Test
Chapter 6: Independent Samples
MANOVA Test
Chapter 7: Repeated Measures
MANOVA Test
Chapter 8: MANCOVA Test
Chapter 9: Trend Analysis
Chapter 10: Method of Writing
High Impact
Journal Paper
2009
2014
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Volume 5: Advanced Research Statistics:
Regression Test, Factor Analysis and
Structural Equation Modeling Analysis
Chapter 1: Data Preparation for SPSS
Program
Chapter 2: Partial Correlation Test
Chapter 3: Hierarchical Multiple
Regressions Analysis
Chapter 4: Hierarchical Binary
Logistics Analysis
Chapter 5: Log-Linear Analysis
Chapter 6: Factor Analysis
Chapter 7: Discriminant Analysis
Chapter 8: Cluster Analysis and Chapter 9: Structural Equation Modeling
Analysis Using AMOS
2014
2009
Mastering Research Methods
Chapter 1: Introduction to Research
Chapter 2: Research Ethics
Chapter 3: Literature Review
Chapter 4: Research Design
Chapter 5: Experimental Study
Chapter 6: Quasi-experimental Study
Chapter 7: Survey Study
Chapter 8: Field Study
Chapter 9: Case Study
Chapter 10: Action Study
Chapter 11: Historical Study
Chapter 12: Probability Sampling Procedures
Chapter 13: Non-probability Sampling Procedures
Chapter 14: Measurement in Research
Chapter 15: Index, Scales and Specific
Measurement Procedures
Chapter 16: Pilot Study
Chapter 17: Research Instrumentation
Chapter 18: Format of Writing Research Report
2011
Mastering Research Statistics
Chapter 1: Descriptive Statistics
Chapter 2: Inferential Statistics and
Significance Test
Chapter 3: Qualitative Data Analysis
Chapter 4: Data Preparation for SPSS
Program
Chapter 5: Reliability of Research
Instrument
Chapter 6: Chi-Square Tests
Chapter 7: T Tests
Chapter 8: ANOVA Tests
Chapter 9: Correlation Tests
Chapter 10: Multiple Regressions
Chapter 11: Multiple Responses Analysis
Chapter 12: Reporting the Results of Data
Analysis Based on the
APA Format
Amazon.com or MPH Online
Concept of Data analysis
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Research design and statistics
A quantitative researcher should understand statisics before planning his research design.
Planning a design without the
knowledge of statistics, the
researcher will find difficult to
analyse the data after collecting it. R
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Research Statistics
Determined by several factors:
Factor 1. Type of study
Factor 2. Type of measurement
scales in an instrument
Factor 3. Sample size
Factor 3. Type of Statistical tests
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Factor 1
1. Types of study:
a. Descriptive study
b. Inferential study
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How to recognize whether the study is a
descriptive study or an inferential study?
In a descriptive study,
No sample is drawn from the
population.
Respondents are the whole
population of the study.
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A descriptive study
•No sample is drawn from the population, so the
results is not generalised. So no inferential
statistical test is needed.
X X
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In a descriptive study,
No sample is drawn from the
population.
Respondents are the whole
population of the study.
The results/ findings are not
generalised to any subject outside the
population .
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Frequency
Percentage
Mean
Median
Mode
Descriptive statistics describes the
characteristics (variables) in the
population.
Standard deviation
Distribution of Scores
The results are nearly 100% correct for
that population.
For example:
Motivation level of the students.
The result is nearly 100% correct (if the measurement is reliable and is correctly done).
1. Collect data (motivation score) from
the students.
2. Calculate the mean score.
3. It represents the motivation level of
the students
Did I select a sample?
Can I generalize the result to other group? Students from other universities?
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B. For an inferential study:
Respondents are subjects selected randomly from a population, to form a sample.
Statistics tests is used to analyse data collected from the sample.
The results / findings are generalised back to the population from where the sample was selected.
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An inferential study
A sample A population
Study is conducted on the sample.
Results from the sample is
generalised to the population.
Statistical test is
needed to generalised
the results.
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Krejcie &
Morgan
(1970)
N = Population size
S = Sample size
Determine the
sample size
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T tests
Chi-square tests
ANOVA tests
Pearson r
Inferential statistics
Tests of differences
Spearman rho
Kruskal-Wallis test
Mann-Whitney U
Tests of relationship
Cramer V
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Summary:
For a descriptive study, no inferential
test / test of significance is needed.
For an inferential study (a sample is
used), test of significance is needed to
generalise the result to the population. R
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Research Instruments : Questionnaire / other quantitative
data measurement devices/ tests
Factor 2:
Types of measurement in an instrument
Types of analysis is determined by
the Scales of measurement in an
instrument
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SCALES OF MEASUREMENT
1.*Nominal scale
- categories/groups of data
2.*Ordinal scale
- Rank
- distances among scales are different
3.*Interval / Ratio scale
- distances among scales are identical
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Scales of measurement 1.*Nominal scale
- categories/groups of data
Gender: 1= Male
2= Female
Ethnicity: 1= M 2= C 3= I
Climate change phenomenon:
1= Pollution 2= Fires
3= Drought 4= Flood
2.*Ordinal scale
- Rank
- distances among
scales are different
Income: 1= < $1000 2= $1001-3000 3 = >$3000
Attitude towards sport management 1= Negative 2= Neutral 3= Positive
3.*Interval / Ratio scale
- distances among scales are identical Math Score: 1, 7, 13, 26…
Age: 5, 12, 13, 42 yo…
Overweight: 1= Yes 2= No 3= Unsure
Level of agreement 1= Strongly disagree, 2= Disagree, 3= Undecided, 4= Agree, 5= Strongly agree
Gender: 2= Male
1= Female
1= M 2= I 3= C
Score: 0, 1, 2, 3, 4…100
Temperature : 50C, 120C,
130C, 420C… Income: 5,000; 12,000; …
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Identifying the scale of a measurement
Continuous
data?
Yes No
Are the distances among
scales identical?
Can the data be
categoried?
Example:
male/female
Yes No Yes
Interval /
ratio scale
Ordinal
scale
Nominal scale R
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What kind of measurement for this data?
The most appropriate price for an original DVD is:
Less than RM 5 …………………..…1
RM 5 to RM 12 ………….……………2
RM 13 to RM 20 ………….……..……3
RM 21 to RM 30 ……………….….....4
More than RM 30 ……………..……..5
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Statement
1. The main factor of air pollution at your place is:
A. Heavy metals
B. Oil
C. Chemicals
D. Fertilizers
2. Your attitude towards the performance of our sport school is
positive.
Strongly agree 1 2 3 4 5 Strongly disagree
3. Do you agree that the main cause of obesity is too much
intake of oily foods.
1. Yes
2. No
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Yearly Incomes (X$1,000)
24 76 35 58 94 93 77 44 66 88 85 55 55
Company type
1. Big
2. Small
1 = <$20 2 = $20-$40 3=$41-$80 4>$80
Human Activities
1.Urbanisation
2.Tourism
3.Agriculture
4.Industry
5.Fisheries
Sport team
1. Under 16
2. Under 18
3. Under 21
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Scales of measurement
Interval and Ratio data are parametric
data (the data are assumed normal
distributed) Example:
Maths scores of a class
Parametric tests are used: T tests, ANOVA tests Pearson Correlation test, etc
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Scales of measurement
Nominal and Ordinal data are non-
parametric data (the data are assumed
not normal distributed)
Level of agreement Strongly disagree - disagree -------- undecided -------- agree - strongly agree 1 2 3 4 5
Non-parametric tests are used: Chi-square tests (Nominal/ordinal data) Mann-Whitney test (Ordinal data) Kruskal-Wallis test (Ordinal data) Spearman rho Correlation test (Ordinal data) Cramer V correlation test (Nominal data) etc
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Example: Stress level of the residents during air
pollution period
data: Ordinal scale (1=Very low, 2=Low, 3=Average, 4=High, 5=Very high)
The Difference between male and female residents: use the non-parametric test - Mann-Whitney test
data: Ratio scale (stress score, exp: 34, 45,
8, 33, 56, 78, 34, 23, 67, 55)
The Difference between male and female residents: use the parametric test - T test
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Factor 3: Sample size
In inferential statistics, Two
assumption are:
1. The data for continouos data
(interval / ratio) is normal
distributed when n => 30.
Each sub-sample: n =>15
2. Data for categorical data are not-
normally distribued.
Each sub-sample => 5
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Any question?
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Take 5?
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