Zainal Abidin Mohamed

Professor at

Faculty of Economics & Muamalat,

laniaz@usim.edu.my

1. Analysis and decision making is an important discipline

which integrates the tools of quantitative and qualitative

analysis with decision making process. An emphasis is

placed on real-life examples to develop the skills

necessary to apply the relevant tools in both

quantitative and qualitative analyses and decision

sciences in deriving optimum business and

organizational decision makings.

2. Both quali and quanti tools will be covered as listed in

the course contents.

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3. Among the three major topics covered in this data

analysis course are:

i. “feel for the data”, ii. “goodness of data”, and

iii. “analysis of data”.

4. The integration of statistical software decrease the

need for manual computation and students can

devote more time on the interpretation of results.

Students are however encouraged to master manual

calculations in order to appreciate the statistical

concepts.

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5. Students are expected to be able to recognize the

various statistical approaches to adopt when presented

with data. Therefore they are expected to be able to

recognize the business problems, formulate hypothesis

and determine test methods and statistics involved and

provide sound business interpretations in order to be

able to impart advice based on the analysed data.

• To familiarize students with concepts involved in various

data analysis technique and its application in research.

• To guide students to compute, perform data analysis and

provide inferences for descriptive measures of data.

• To encourage students to participate in decision making

by applying various qualitative and quantitative

techniques.

• After completing this course, the students should be able:

• to evaluate the relevancy and suitability of various data analysis technique and its application in research.(LO1, C6)

• to construct various data analysis techniques by using qualitative and quantitative tools (software). (LO2, P4)

• to produce a preliminary data analysis results based on the proposed research area (LO7- LL3, P6).

MAJOR TOPICS HRS

1 Introduction to data analysis 3

2 Feel of data 3

3 Goodness of data 3

4 Qualitative: Quantitative: Analytical tools

4a i Typology 4b.Regression Model (RM) linear patterns 3

ii Taxonomy

5a i Grounded theory 5b. RM: curved patterns 3

ii Analytic induction

6a i Logical analysis 6b.RM: simple regression 3

ii quasi statistics

7a i event analysis 7b. RM: regression diagnostics 6

ii metaphorical analysis

8a i domain analysis 8b.RM: multiple regression 6

ii hermeneutical analysis

9a i discourse analysis 9b. RM: building regression models 3

ii semiotics

10a i content analysis 10b. RM: categorical explanatory variables 3

ii Phenomenology/heuristic

11a i narrative analysis 11b: ANOVA Test/Assessment/Presentation 3

FINAL 3

TOTAL 42

DA

TA D

EFIN

ED

data is the plural of datum, a single piece of

information

Representation of facts, concepts, for communication,

interpretation and processed further

Value derived from research or scientific experiments

Collection of numbers, symbols

• Data base Data search ` Data warehouse

• Data organising Data mining Data generating

• Data collection Data properties Data tables

• Data entering Data editing Data recoding

• Data aggregation

• Data types

– Primary and secondary data

– Time series/ cross sectional data

– Categorical and Numerical data

DATA

ANALYSIS

quantitative

discrete

continuous

qualitative Various

methodologies

QUANTITATIVE QUALITATIVE

OBJECTIVE THE CHIP SPEED OF MY

COMPUTER IS 1 GIG

YES ALMOST ALL IN THE CLASS

HAS A COMPUTER

SUBJECTIVE

ON A SCALE OF 1-10, MY

COMPUTER WOULD GET A

SCORE OF 8 IN TERMS OF

USER FRIENDLY

THE NEW GENERATION

COMPUTER S CONTINUE TO

HAVE ADDITIONAL

APPLICATIONS

1. Systematically applying statistical and/or logical techniques

to describe and illustrate, condense and recap, and

evaluate data.

2. “provide a way of drawing inductive inferences from data

and distinguishing the signal From the noise Present in the

data”. (Shamoo and Resnik, 2003).

3. In qualitative research analysis becomes an ongoing

iterative process where data is continuously collected and

analyzed almost simultaneously. Researchers generally

analyze for patterns in observations (Savenye, Robinson,

2004).

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4. The form of the analysis is determined by the specific

qualitative approach taken (various methodology).

5. Data integrity is ensured through the accurate and

appropriate analysis of research findings. Improper

statistical analyses distort scientific findings, mislead casual

readers (Shepard, 2002).

6. Integrity issues are just as relevant to analysis of non-

statistical data as well.

For quantitative references:

Main reference:

• Stine, R. and Foster, D. 2010. Statistics for Business: Decision

making and Analysis, Pearson Education, Inc., New Jersey.

Additional references:

• Lind, Douglas A., William G. Marchal and Samuel A. Wathen.

2000. Basic Statistics for Business & Economics, Sixth Ed.

McGraw Hill. Boston U.S.A.

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• Wooldridge, J.M. 2000. Introductory Econometrics: A Modern

Approach, South- Western College Publishing, U S A.

• Groebner, David F., Patrick W. Shannon, Phillip C. Fry and Kent

D. Smith. 2008 Business Statistics: A Decision-Making

Approach. Seventh Ed. Pearson Education, Inc., New Jersey.

• Brenson Mark, L., David M. Levine and Timothy C. Krehbiel

2009. Basic Business Statistics: Concepts and Applications

11th edition, Pearson Education, Inc., New Jersey

Continue… for qualitative references:

Main reference:

• Willis J.W., 2007. Foundations of Qualitative Research, Sage

Publications, California.

Additional references:

• Merriam S.B. and Associates. 2002. Qualitative Research in

Practice, Jossey Bass, San Francisco.

• Boeije H. 2010. Analysis in Qualitative Research, Los Angeles.