research methodology chapter 12 data analysis & …
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Research Methodology
Professor Li-Hua LIChaoyang University of Technology (CYUT)
朝陽科技大學李麗華
Text Book: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
Chapter 12
Data Analysis & Interpretation
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Types of Experimental Design
• Editing Data• Handling Blank Responses
• Coding
• Categorizing
• Entering Data
• Data analysis• Basic Objectives in Data Analysis
• Feel for the Data
• Testing Goodness of Data
• Hypothesis Testing
• Data Analysis and Interpretation• Use of several Data-Analytic Technique
• Descriptive Statistics
• Inferential Statistics
Refrence: https://www.slideshare.net/mehtabmr/lecture-07-2498485
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33Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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• Blind men and an elephant. - Chinese & Indian fable
• There is a world of difference between truth and facts. Fact can obscure the truth. --Maya Angelou
• Things aren’t always what we think!
The picture is from: https://www.slideshare.net/azamghaffar/probability-sampling-techniques
Data Analysis & Interpretation
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• Objective of Data Analysis
1. Getting a feel for the data such as getting the mean, range, std. deviation,…
2. Testing the goodness of data
such as reliability test, validity test
3. Testing the hypotheses
The picture is from: https://www.slideshare.net/azamghaffar/probability-sampling-techniques
Data Analysis & Interpretation
The purpose of data analysis is to answer the
research questions and to help determine the trends
and relationships among the variables.
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Data Analysis & Interpretation
Common descriptive statistics• Count (frequencies)
• Percentage
• Mean
• Mode
• Median
• Range
• Standard deviation
• Variance
• Ranking
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Data Analysis & Interpretation
Think about analysis early
• Start with a plan
• Code, enter, clean
• Analyze
• Interpret
• Reflect
−What did we learn?
−What conclusions can we draw?
−What are our recommendations?
−What are the limitations of our analysis?
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Data Analysis & Interpretation
Why do I need an analysis plan?
– To make sure the questions and your data collection instrument will get the information you want
– Think about your “report” when you are designing your data collection instruments
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Data Analysis & Interpretation
Do you want to report…• the number of people who answered each question?
• how many people answered a, b, c, d?
• the percentage of respondents who answered a, b, c, d?
• the average number or score?
• the mid-point among a range of answers?
• a change in score between two points in time?
• how people compared?
• quotes and people’s own words
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Data Analysis & Interpretation
Key components of a data analysis plan• Purpose of the evaluation
• Questions
• What you hope to learn from the question
• Analysis technique
• How data will be presented
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Data Analysis & Interpretation
Getting your data ready• Assign a unique identifier
• Organize and keep all forms
(questionnaires, interviews, testimonials)
• Check for completeness and accuracy
• Remove those that are incomplete or do not make sense
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Data Analysis & Interpretation
Data entry
• You can enter your data
−By hand
−By computer
--By cloud service
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Data Analysis & Interpretation
Hand coding
Question 1 : Do you smoke? (circle 1)
YES NO No answer
// ///// /
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Data Analysis & Interpretation
Data entry by computer
• Excel (spreadsheet)
• Microsoft Access (database mngt)
• Quantitative analysis: SPSS (statistical software)
• Qualitative analysis: Epi info (CDC data management and analysis program: www.cdc.gov/epiinfo); In ViVo, etc.
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Data Analysis & Interpretation
Data entry computer screen
Smoking 1(yes) 2(no)
Survey ID Q1 Do you smoke Q2 Age
001 1 24
002 1 18
003 2 36
004 2 48
005 1 26
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Data Analysis & Interpretation
Dig deeper• Did different groups show different
results?
• Were there findings that surprised you?
• Are there things you don’t understand very well – further study needed?
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Data Analysis & Interpretation
Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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Data Analysis & Interpretation
Common descriptive statistics• Count (frequencies)
• Percentage
• Mean
• Mode
• Median
• Range
• Standard deviation
• Variance
• Ranking
The conclusion must describe sample characteristics and only refer to the sample
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Data Analysis & Interpretation
Descriptive Analysis1. Frequency Distribution
A systematic arrangement of numeric values from the lowest to the highest or highest to lowest.
Formula : Ef = N
E : sum of
f : frequency
N: sample size
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Data Analysis & Interpretation
Descriptive Analysis
2. Measure of Central Tendency
A statistical index that describe the average of the set values.
Kinds of averages: Mean, Median, Mode
3. Measure of Variability
Statistics that concern the degree to which the scores in a distribution are different from or similar to each other.
EX: Range, Standard Deviation
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2121https://www.statalist.org/forums/forum/general-stata-discussion/general/1395253-descriptive-
statistics-table-generation
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2222Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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Data Analysis & Interpretation
Descriptive Analysis
4. Bivariate Descriptive Statistics
Derived from the simultaneous analysis of two variables to examine the relationships between the variables.
(1)Contingency Tables: is essentially a 2-D frequency distribution in which the frequencies of two variables are cross-tabulated.
(2)Correlation: the most common method of describing the relationship between two measures.
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2424https://sciencestruck.com/descriptive-vs-inferential-statistics
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Data Analysis & Interpretation
Inferential AnalysisThe use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses.
Inferential Statistics : are numerical values that enable the researcher to draw conclusion about a population based on the characteristics of a population sample.
This is based on the laws of probability.
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Data Analysis & Interpretation
Use of Inferential Analysis1. t-test : is used to examine the difference
between the means of two independent groups.
2. Analysis of Variance (ANOVA) : is used to test the significance of differences between means of two or more groups.
3. Chi-square: is used to test hypotheses about the proportion of elements that fall into various cells of a contingency table.
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2727Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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2828https://towardsdatascience.com/inferential-statistics-hypothesis-testing-using-normal-deviate-z-test-c3f5f7647581
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Data Analysis & Interpretation
Steps in testing hypothesis• Determine the test statistics to be used
• Establish the level of significance
• Select a one-tailed or two-tailed test
• Compute a test statistic
• Calculate the degree of freedom
• Obtain a tabled value for statistical test
• Compare the test statistics to the tabled value
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Data Analysis & InterpretationDiscussing limitationsWritten reports:• Be explicit about your limitations
Oral reports:• Be prepared to discuss limitations• Be honest about limitations• Know the claims you cannot make
−Do not claim causation without a true experimental design
−Do not generalize to the population without random sample and quality administration (e.g., <60% response rate on a survey)
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Data Analysis & Interpretation
Analyzing qualitative data
“Content analysis” steps:
1. Transcribe data (if audio taped)
2. Read transcripts
3. Highlight quotes and note why important
4. Code quotes according to margin notes
5. Sort quotes into coded groups (themes)
6. Interpret patterns in quotes
7. Describe these patterns
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• Hand coding
• qualitative data
Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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Data Analysis & Interpretation
Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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Data Analysis & Interpretation
Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.
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3535https://stackoverflow.com/questions/34469597/what-is-the-difference-between-
inferential-analysis-and-predictive-analysis
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Review of this classAfter learning of chapter 12, you should be able to:
1. Edit questionnaire and interview responses
2. Handle blank responses
3. Set up the coding key for the data set and code the data
4. Categorize data
5. Create a data file
6. Data Analysis
7. Get a “feel” for the data
8. Test the goodness of data
9. Interpret the computer results of tests of various hypotheses.