Research Designs

Facilitator:

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

Former Director,

Centre for Real Estate Studies

Faculty of Engineering and Geoinformation Science

Universiti Teknologi Malaysia

Skudai, Johor

Research Design“What it is” Researcher’s overall strategy for answering

research questions. How the study will be performed Different strengths and weakness Classified in a number of ways:

* Descriptive

* Analytic, observational

* Interventional

* Experimental

Quasi-experimental

Research Design - Descriptive Studies”

Describes general characteristics of phenomena. E.g.

* distribution of industrial sites, squatter areas, trend in property values, etc. Further classified into: * correlational studies * case studies * cross-sectional survey

Descriptive studies“Correlational studies” Uses population data to compare between different groups. E.g.

* Mean per house price could be compared to per capita income levels for several geographic areas. Have a number of limitations: * Multi-characteristic nature of population → which correlate to which? - beliefs of the investigators (e.g. income and house price are somehow linked) - researcher’s bias, lack of knowledge (factors other than income!!) * Tend to be bi-directional → underestimate . E.g.

* Correlation does not basically measure cause-and-effect → Misses the opportunity to explain “what causes what, how much, and how”

PriceIncome

Interest rate

Locality

Deposit

Loan marginLoan period

? ?

? ?

Income xx

underlying reality

Price

Descriptive studies “Case Studies”

Describe individual or a group of cases showing certain characteristics, in certain situations, under certain conditions.

Describe a relatively “new”, “unusual”, or “peculiar” issues/things worth researching. E.g.

* Why the shopping complex fails? * What makes the place very popular? * How did SARS, Avien flu affect Johor Bahru property market? Limited to some “localised”, “personalised” problem/issue and

suggesting potential hypotheses to be tested in future studies. May not be generalisable due to some “unusualness”,

“peculiarity” attached to a particular case To a certain extent may suggest potential hypotheses to be

tested in future studies.

Descriptive studies “Cross-Sectional Survey”

Presence or absence of some characteristics are determined at the same time point. E.g.

whether subjects were current smokers (exposure) and whether they had evidence of emphysema (disease). Because both exposure and disease are determined at the same time, it is impossible to determine, in most cases, which came first. Therefore, cross-sectional studies can suggest associations between an exposure and a disease but cannot prove causality.

Elements of Design

Observations or Measures * Symbolized by an 'O' * Can refer to a single measure (e.g., sale price), a single instrument with multiple items (e.g. Likert scale), a complex multi-part instrument (e.g., a survey), or a whole battery of tests or measures given out on one occasion. * To distinguish among specific measures, use subscripts: O1, O2, and so on. Treatments or Programs * Symbolized with an 'X‘ * Can refer to a simple (e.g., a technique) or complex (e.g., an industrial training program) intervention. * No symbol for no-treatment control or comparison group * Can use X+ and X- to indicate treatment and control respectively. * Can use subscripts to distinguish different programs or program variations. Groups * Each group is shown on one line in the design structure. So, if the design notation has three groups, there are three lines in the design. Assignment to Group * Designated by a letter at the beginning of each line (i.e., group) * Describes how the group is assigned. * Major types of assignment: R = random assignment; N = non-equivalent groups; C = assignment by cutoff; Time

Elements of Design“Generic examples”

Type of design General structure Example of issue

Relationship between investor’s risk attitude and choices of type of investment in shares

Effects of crime on housing demand in the Johor Bahru inner city & city-outskirt areas

Effects of KLIA project on land-travel convenience among the residents in the neighbouring areas

R = random assignment; X = treatment; O = observation; N = non-random

Research design“An example” Research question/issue: “Effects of KLIA project on land-travel convenience among the residents

in the neighbouring areas” Design elements: * Convenience - saved travel-time, T - reduction in petrol consumption, P - evasion from traffic congestion, C - increased frequency of public transport service, F * Residents - Group 1: own transport - Group 2: public transport * Hypothesis 1: Average time-travel saved is 20 minutes “after” project 2: Average reduction in petrol consumption is 15%/trip 3: Average time of traffic congestion drops to 15 minutes 4: Frequency of public transport increases by 20% after “project”

Research design - “Pre-test post-test equivalent-group quasi-experimental”

N O1 X O1

T1 T1’

P1 P1’

C1 C1’

F1 F1’

N O2 X O2

T2 T2’

P2 P2’

C2 C2’

F2 F2’

O1 = Own transport O2= Public transport X = KLIA project

Cause-and-effect investigated using instrument – structured q’naire

Research design“An example”

Research question/issue: “Does decision to choose a particular type of shares investment have any

relationship with risk attitude of the investor?” Design elements: * Type of investment (post-test observation, O”) - share corporate, C - property trust, P - bon, B government, G * Risk attitude (post-test observation, O’) - Taker, T - Averser, A - Neutral, N * Hypothesis 1: If one is a risk-taker, then invest in high-risk shares 2: If one is risk-averser, then invest in low-risk shares 3: If one is risk-neutral, then invest in low-risk and/or high-risk shares

Low-risk share

High-risk share

Research design“Post-test non-experimental”

N

T

A

N

High-risk shares

Low-risk shares

C, P

G B

C

P

GB

Risk attitude groupsX

X O

Post-test treatment

Post-test observation

O Shares’ risk categoriesCause-and-effect investigated using various interview instruments

Thank you!