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

https://www.slideshare.net/mehtabmr/lecture-07-2498485

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


https://www.slideshare.net/azamghaffar/probability-sampling-techniques

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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


The purpose of data analysis is to answer the

research questions and to help determine the trends

and relationships among the variables.

https://www.slideshare.net/azamghaffar/probability-sampling-techniques

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Common descriptive statistics• Count (frequencies)

• Percentage

• Mean

• Mode

• Median

• Range

• Standard deviation

• Variance

• Ranking

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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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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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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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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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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 entry

• You can enter your data

−By hand

−By computer

--By cloud service

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Hand coding

Question 1 : Do you smoke? (circle 1)

YES NO No answer

// ///// /

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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 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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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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Source: Uma Sekaran, ”Research Methods for Business : A Kill Building Approach,” John Wiely & Sons, 2016.

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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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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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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

2121https://www.statalist.org/forums/forum/general-stata-discussion/general/1395253-descriptive-

statistics-table-generation

https://www.statalist.org/forums/forum/general-stata-discussion/general/1395253-descriptive-statistics-table-generation

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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.

2424https://sciencestruck.com/descriptive-vs-inferential-statistics

https://sciencestruck.com/descriptive-vs-inferential-statistics

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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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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.

2828https://towardsdatascience.com/inferential-statistics-hypothesis-testing-using-normal-deviate-z-test-c3f5f7647581

https://towardsdatascience.com/inferential-statistics-hypothesis-testing-using-normal-deviate-z-test-c3f5f7647581

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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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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


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3535https://stackoverflow.com/questions/34469597/what-is-the-difference-between-

inferential-analysis-and-predictive-analysis

https://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.

Research Methodology Chapter 12 Data Analysis & …

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