Choosing the Correct Data AnalysisRayenda Khresna Brahmana

Faculty of Economics and Business

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Rayenda Khresna Brahmana1

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Rayenda

Brahmana

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Research Design

Paradigm

Quantitative, qualitative, mixed-method designs:

What does each methodology entail?

School of ThoughtsQualitative Quantitative

Expand or understand a phenomenon

Design study driven by induction & exploration rather than by theory

Include research questions but no hypotheses

Explain processes for coding/categorizing data

Understand relationship bet. two+

quantifiable variables

Design study driven by theory rather

than by induction or exploration

Include a null and an alternative hyp

othesis for each research

question

Qualitative Research Tools

Quantitative study• 2 Common approach

SECONDARY

DATASURVEY

EconometricsQuestionnaire –

Cross Section

OLS, Panel,

VAR, GARCH

, VECM, ARI

MA

OLS, PLS

Eviews

, Stata

SPSS, S

martPLS

, Amos,

Lisrel

Research Design

• Each research design has different research tool

• 4 Research Design

– Experiment

– Survey

– Secondary Data

– Observation

Research Design

Experimental Manipulation check

Survey Reliability and Validity

Observation Tendency Test

CLRM the 4 Classical Assumption

Panel Regression Breusch and Pagan LM, and Hausman Test

Cointegration and VECM Unit Root Test

Different design, different test

Choosing Data

Analysis

• Refer back to your objective. It is important

• Head or Tail: Experiment?

• Happiness: Survey or Experiment or Secondary

Data?

• Choice: Contingent Valuation Model or Discrete

Choice Experiment?

• Optimization: Heuristic Optimization? GA? ML?

How To CHOOSE?

Survey

• Correlation

• Variance Analysis

• Temporal Study

• Causality

• Be careful: Structural Model? Cross sectional

? Longitudinal?

Experiment

• T-test? Paired? Mean? Independent? Krus

kall Wallis?

• Simulation (Magnitude)

• Factorial Design?

• Correlation?

• Descriptive

• Causality Effect?

Secondary Data

• Cross-Sectional? Multiple Regression Family

• Time Series? OLS, Cointegration, GARCH,

ARIMA, GMM, etc

• Panel? Panel Regression

• Correlation?

• Causality?

• Descriptive?

Observation

• Descriptive

• Mean Difference

• Regression

• Non-Parametric test

• Data modeling and parameter estimation:

maximum likelihood, chi-squared minimiza

tion

Others?

• SIMULATION: Monte Carlo, Bayesian, Ne

ural Network

• Mapping: Trend Algorithm, GIS, Social Net

work Analysis

• Content Analysis: NVIVO, ATLAS.ti

• EFFICIENCY: DEA, SFA

• Logistic Regression

Extra Note

• Moderation Effect: Cohen F2, Hayes and Pallant

(2018)

• Mediation Effect: Preacher and Hayes (2004), S

obel Test, R2 Changed Size, etc

• Endogeneity: TSLS, GMM

• Interaction Model

• Sample Size: G-Power, Sekaran and Bougie (20

10), Rule of 10, Normality, Sample Iteration, etc

Extra Note

• Do Extra Robustness Test! Why?

– Confirmation

– Selling point

• Example:

– Sub-sampling

– T-Test

Important References

Important References

Important References

Important References

Important References

Important References

So, what is our conclusion?

1. Choosing data analysis has to always refer back to your research objective and

research philosophy

2. If you are PhD or MPhil, make sure you know the research philosophy before est

ablishing a new model. Ask first, what is your epistemological assumption? Your

ontological? Your axiological? Etc

3. There is no such thing as the best method. But there is always a room for wrongly

-choose-model

4. Understand first your method before you write it

Thank you