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: PTK Course for Local Governments, UTM, Skudai, /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 the Author. 1

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

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

Basic Structure of Research Unit Institutional links Administration, rules & regulations Research training program Supervisory system Resources 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 outcomes & obstacles?

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

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

♦ 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 What is Directed Research

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

Strategic Research Planning 9 PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman Centre for Real Estate Studies Faculty of Engineering and Geoinformation Science Universiti Tekbnologi Malaysia Skudai, Johor

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 techniques

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 utilizing controlled data and factual information

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. The second and third are, arguably, most popular in pure, applied, and social sciences

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: * Descriptive statistics * Inferential statistics

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) Basically non-parametric

Examples of “abstraction” of phenomena

% prediction error

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

Examples of relationship Dep=9t – Dep=7t – 192.6

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.E.g. Outputs/results expected * research issue * research questions * research hypotheses At post-graduate level research, failure to choose the correct data analysis technique is an almost sure ingredient for thesis failure.

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.E.g. Simply exclude a technique Note: No way can Likert scaling show “cause-and-effect” phenomena! IssueData analysis techniques Wrong techniqueCorrect 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 experimental investigation

Common mistakes (contd.) – “Abuse of statistics” IssueData analysis techniques Example of abuseCorrect 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 R 2 Many – a.o.t. Box-Cox  2 test for model equivalence To evaluate accuracy of “prediction”Using R 2 and/or F-value of a model Hold-out sample’s MAPE “Compare” whether a group is different from another Multi-dimensional scaling, Likert scaling Many – a.o.t. two-way anova,  2, Z test To determine whether a group of factors “significantly influence” the observed phenomenon Multi-dimensional scaling, Likert scaling Many – a.o.t. manova, regression

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??

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 * Learning a lesson from the problem

Data can’t “talk” An analysis contains some aspects of scientific reasoning/argument: * Define * Interpret * Evaluate * Illustrate * Discuss * Explain * Clarify * Compare * Contrast 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,…) Example

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 * Don’t tell something not supported by data

Principles of data analysis (contd.) ShoppersNumber Male Old Young 6464 Female Old Young More female shoppers than male shoppers More young female shoppers than young male shoppers Young male shoppers are not interested to shop at the shopping complex

Data analysis (contd.) When analysing: * Be objective * Accurate * True Separate facts and opinion Avoid “wrong” reasoning/argument. E.g. mistakes in interpretation.

Some Principles of Statistical Methods in Data Analysis

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: * Descriptive statistics * Inferential statistics * Probability theory

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 with through regression

Non-Parametric Data Analysis Methods – A Summary Scale of measurement One-sampleTwo independent Sample K independent Sample Measures of Association Independent Sample Single treatment repeat Measures Multiple treatment repeat Measures NominalBinomial test; one-way contingency Table McNemar test Cochrane Q Test Two-way contingency Table Contingenc y Table Contingenc y Coefficients OrdinalRuns testWilcoxon signed rank test Friedman test Mann- Whitney Test Kruskal- Wallis Test Spearman rank Correlation Interval/ratioZ- or t-test of variance Paired t-testRepeat measures ANOVA Unpaired t-test; tests of variance ANOVARegression, Pearson correlation, time series

34 Parametric Analysis - Regression 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. NB NB NB are insignificant