Research Management and Data Analysis

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RESEARCH MANAGEMENT

DATA ANALYSIS

Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman, Centre for Real Estate Studies,

Faculty of Geoinformation Engineering & Sciences, Universiti Teknologi Malaysia.

Web: http://ac.utm.my/web/hamidiman

PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

(C) Copyrights of the Author. No part of materials in these slides should be extracted in any electronic or non-electronic method without permission from

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Definition



A

systematic programme

of planning ,

coordinating, implementing, and controlling

knowledge process

through information

development, with a view to obtaining a

strategic fit between an organisation’s goals

and its internal capabilities.



It is basically a

practice-related research

management.



The nature of the research may be

fundamental, developmental, or commercial.

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Purpose of Research



Intelligence purposes



Ad-hoc or planned problem-solving.



Strengthening overall research

programs within a particular

organisation.



Enhancing organisational capabilities,

e.g.

→ medium-term & long-term planning,

strategy,

decision-making ability, etc.



Else?

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Basic Structure of Research Unit

Targets (e.g. groups)

4 PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

The ‘state of affair’ of each component of this structure?

What, how, how much, when, and who to

improve?

Possible

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

Organisational research philosophy.



Strategic research areas.



Proper administrative structure.



Adequate & good facilities.



Qualified staff.



Research training:

* Research programs;

* Research methodology;

* Intelligence gathering & ad-hoc research;

* Information management.



Funding.

Developing Research Skills

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

Go beyond administrative functions.



Producing practice-related research outcomes.



Fulfilling organisational mission.



Directed research:

* Problem-solving research.

* Industry orientation (applied research),

aligning with government’s policies & within

the ambit of organisational policies.

Organisational Research Philosophy

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♦ A research that is pivoted on the riority areas of an organisation in which it has the expertise, resources, and institutional set-up readily available.

♦ To help an organisation focus on some strategic

research areas, reflecting its research niches and

strength and thus giving it competitive advantages in those areas.

♦ These focus areas are the “shooting targets” at which

Key Performance Indicators (KPI) are used to gauge institutional achievement.

♦ Can be implemented in collaboration with universities

through Intensification of Research in Priority Areas (IRPA), E-Science, Technofund, and the National

Property Research Coordinator (NAPREC), etc.

7 PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

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Strategic Research Areas

 _{Need for strategic research planning.}

 _{Purpose: to identify research niches, strengths,}

and thus, comparative advantages.

 _{Should be established at departmental level.}

 _Example:

* Set research mission, goal & objectives, portfolio &

functional strategies;

* Establish two-tier research programs: (1) priority

research; (2) fundamental research;

* Documentation of departmental strategy.

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Strategic Research

Planning

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Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman

Centre for Real Estate Studies Faculty of Engineering and Geoinformation

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Objectives

 _{Overall: Reinforce your understanding from the}

main lecture

 _Specific:

* Concepts of data analysis

* Some data analysis techniques * Some tips for data analysis

 _{What I will not do:}

* To teach every bit and pieces of statistical analysis

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Data analysis – “The Concept”



Approach to de-synthesizing data,

informational, and/or factual elements to

answer research questions



Method of putting together facts and figures

to solve research problem



Systematic process of utilizing data to

address research questions



Breaking down research issues through

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Categories of data analysis

 _{Narrative (e.g. laws, arts)}

 _{Descriptive (e.g. social sciences)}

 _{Statistical/mathematical (pure/applied sciences)}

 _{Audio-Optical (e.g. telecommunication)}

 _Others

Most research analyses, arguably, adopt the first three.

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Statistical Methods

 _{Something to do with “statistics”}

 _{Statistics: “meaningful” quantities about a}_sample

of objects, things, persons, events, phenomena, etc.

 _{Widely used in social sciences.}

 _{Simple to complex issues. E.g.}

* correlation * anova

* manova

* regression

* econometric modelling

 _{Two main categories:}

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Descriptive Statistics

 _{Use sample information to explain/make}

abstraction of population “phenomena”.

 _{Common “phenomena”:}

 * Association (e.g. σ_1,2.3 = 0.75)

 _{* Tendency (left-skew, right-skew)}

 _{* Causal relationship (e.g. if X, then, Y)}

 _{* Trend, pattern, dispersion, range}

 _{Used in non-parametric analysis (e.g.}

chi-square, t-test, 2-way anova)

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Examples of “abstraction” of phenomena

Trends in property loan, shop house demand & supply

0 50000 100000 150000 200000

Year (1990 - 1997)

Loan to property sector (RM million)

32635.8 38100.6 42468.1 47684.7 48408.2 61433.6 77255.7 97810.1 Demand for shop shouses (units) 71719 73892 85843 95916 101107 117857 134864 86323 Supply of shop houses (units) 85534 85821 90366 101508 111952 125334 143530 154179 1 2 3 4 5 6 7 8

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 Batu Pah at Joho

r Bah ru Klua ng Kota Tin ggi Mer

sing _Muar Pont ian Sega mat District N o . o f h o u se s 1991 2000 0 2 4 6 8 10 12 14 0-4 10-1 4 20-2 4 30-3 4 40-4 4 50-5 4 60-6 4 70-7 4

Age Category (Years Old)

P ro p o rt io n ( % )

Demand (% sales success)

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Examples of “abstraction” of phenomena

Demand (% sales success)

120 100 80 60 40 20 P ri c e ( R M /s q .f t. b u ilt a re a ) 200 180 160 140 120 100 80

1 0 . 0 0 2 0 . 0 0 3 0 . 0 0 4 0 . 0 0 5 0 . 0 0 6 0 . 0 0 1 0 . 0 0

2 0 . 0 0 3 0 . 0 0 4 0 . 0 0 5 0 . 0 0

- 1 0 0 . 0 0 - 8 0 . 0 0 - 6 0 . 0 0 - 4 0 . 0 0 - 2 0 . 0 0 0 . 0 0 2 0 . 0 0 4 0 . 0 0 6 0 . 0 0 8 0 . 0 0 1 0 0 . 0 0

D is_t a n c e f_r o m R a k a_i a ( k m )

D i s t a n c e f r o m A s h u r t o n ( k m )

% prediction

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Inferential statistics

 _{Using sample statistics to infer some}

“phenomena” of population parameters

 _{Common “phenomena”: cause-and-effect}

* One-way r/ship

* Multi-directional r/ship * Recursive

 _{Use parametric analysis}

Y1 = f(Y2, X, e1) Y2 = f(Y1, Z, e2)

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Examples of relationship

Coefficientsa

1993.108 239.632 8.317 .000

-4.472 1.199 -.190 -3.728 .000

6.938 .619 .705 11.209 .000

4.393 1.807 .139 2.431 .017

-27.893 6.108 -.241 -4.567 .000

34.895 89.440 .020 .390 .697

(Constant) Tanah Bangunan Ansilari Umur Flo_go Model

1

B Std. Error

Unstandardized Coefficients

Beta Standardized

Coefficients

t Sig.

Dependent Variable: Nilaism a.

Dep=9t – 215.8

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Which one to use?

 _{Nature of research}

* Descriptive in nature?

* Attempts to “infer”, “predict”, find “cause-and-effect”, “influence”, “relationship”?

* Is it both?

 _{Research design (incl. variables involved). E.g.}

 _{Outputs/results expected}

* research issue

* research questions * research hypotheses

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Common mistakes in data analysis

 _{Wrong techniques. E.g.}

 _{Infeasible techniques. E.g.}

How to design ex-ante effects of KLIA? Development occurs “before” and “after”! What is the control

treatment?

Further explanation!

 _{Abuse of statistics. E.g.}

 _{Simply exclude a technique}

Note: No way can Likert scaling show “cause-and-effect” phenomena!

Issue Data analysis techniques

Wrong technique Correct technique To study factors that “influence” visitors to

come to a recreation site

“Effects” of KLIA on the development of Sepang

Likert scaling based on interviews

Data tabulation based on open-ended questionnaire survey

Descriptive analysis based on ex-ante post-ante

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Common mistakes (contd.) – “Abuse of statistics”

Issue Data analysis techniques

Example of abuse Correct technique

Measure the “influence” of a variable on another

Using partial correlation

(e.g. Spearman coeff.)

Using a regression parameter

Finding the “relationship” between one variable with another

Multi-dimensional scaling, Likert scaling

Simple regression coefficient

To evaluate whether a model fits data better than the other

Using R2 _{Many – a.o.t. Box-Cox}

2_{test for model}

equivalence To evaluate accuracy of “prediction” Using R2_{and/or F-value}

of a model

Hold-out sample’s MAPE

“Compare” whether a group is different from another

Many – a.o.t. two-way anova, 2_{, Z test}

To determine whether a group of factors “significantly influence” the observed phenomenon

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How to avoid mistakes - Useful tips

 _{Crystalize the research problem → operability of}

it!

 _{Read literature on data analysis techniques.}

 _{Evaluate various techniques that can do similar}

things w.r.t. to research problem

 _{Know what a technique does and what it}

doesn’t

 _{Consult people, esp. supervisor}

 _{Pilot-run the data and evaluate results}

 _{Don’t do research??}

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Principles of analysis

 _{Goal of an analysis:}

* To explain cause-and-effect phenomena * To relate research with real-world event * To predict/forecast the real-world

phenomena based on research

* Finding answers to a particular problem

* Making conclusions about real-world event based on the problem

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

Data can’t “talk”



An analysis contains some aspects of scientific

reasoning/argument:

* Define

* Interpret

* Evaluate

* Illustrate

* Discuss

* Explain

* Clarify

* Compare

* Contrast

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Principles of analysis (contd.)

 _{An analysis must have four elements:}

* Data/information (what)

* Scientific reasoning/argument (what? who? where? how? what happens?) * Finding (what results?)

* Lesson/conclusion (so what? so how? therefore,…)

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Principles of data analysis



Basic guide to data analysis:

* “Analyse” NOT “narrate”

* Go back to research flowchart

* Break down into research objectives and

research questions

* Identify phenomena to be investigated

* Visualise the “expected” answers

* Validate the answers with data

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Principles of data analysis (contd.)

Shoppers

Number

Male

Old

Young

6

4

Female

Old

Young

10

15

More female shoppers than male shoppers

More young female shoppers than young male shoppers

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Data analysis (contd.)

 _{When analysing:}

* Be objective * Accurate

* True

 _{Separate facts and opinion}

 _{Avoid “wrong” reasoning/argument. E.g.}

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What is Statistics

 _{“Meaningful” quantities about a}_sample_of

objects, things, persons, events, phenomena, etc.

 _{Something to do with “data”}

 _{Widely used in various discipline of sciences.}

 _{Used to solve simple to complex issues.}

 _{Three main categories:}

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Forms of “Statistical” Relationship

 _{Relationship can be non-parametric or}

parametric

 _{E.g. of non-parametric r/ships:}

* Correlation * Contingency

 _{E.g. of parametric}_{→ c}_{ause-and-effect}

* Causal

* Feedback

* Multi-directional * Recursive

 _{The “parametric” categories are normally dealt}

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Non-Parametric Data Analysis Methods – A Summary

Scale of measurement One-sample Two independent Sample K independent Sample Measures of Association Independent Sample Single treatment repeat Measures Multiple treatment repeat Measures Nominal Binomial test; one-way contingency Table McNemar test Cochrane Q Test Two-way contingency Table Contingency Table Contingenc y Coefficients

Ordinal Runs test Wilcoxon

signed rank test Friedman test Mann-Whitney Test Kruskal-Wallis Test Spearman rank Correlation

Interval/ratio Z- or t-test of variance

Paired t-test Repeat

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Parametric Analysis - Regression

Coefficients(a)

Unstandardized Coefficients StandardizedCoefficients

Model B Std. Error Beta t Sig.

(Constant) _29680.695 _2885.532 _10.286 _.000

AGE _-705.817 _38.491 _-.212 _-18.337 _.000

NB211001 _12374.064 _2176.815 _.061 _5.684 _.000

NB211002 _-1094.891 _1527.977 _-.008 _-.717 _.474

NB211003 _-938.838 _1136.671 _-.010 _-.826 _.409

NB211005 _12639.946 _2139.489 _.066 _5.908 _.000

NB211006 _852.109 _2535.266 _.004 _.336 _.737

SQFT1 _31.388 _7.815 _.039 _4.016 _.000

SQFT2 _44.166 _1.365 _.595 _32.349 _.000

SQFT3 _52.939 _1.265 _.808 _41.857 _.000

SQFT4 _60.447 _3.561 _.164 _16.974 _.000

SQFT5 _94.723 _2.943 _.312 _32.186 _.000

LAND75 _11.788 _.433 _.303 _27.240 _.000

BATHS _7714.093 _1338.204 _.076 _5.765 _.000

POOL _13359.275 _1184.469 _.105 _11.279 _.000

1

GARAGE _10.750 _3.137 _.038 _3.427 _.001

a Dependent Variable: SALEPRIC

Rule of Thumb: “t” scores Should be 2.0 or greater. Nilai “t” seharusnya lebih Besar atau sama dengan

2,0

The significance of each variable to the model can be determined by looking at the “t” values.