RESEARCH DESIGN TOPICS l l Non experimental research

RESEARCH DESIGN

TOPICS l l Non experimental research design Experimental vs. quasi-experimental research designs Research designs with limited power to assess cause and effect Research design with more power to assess cause and effect

DEVELOPMENT & INCOME DISTRIBUTION l The economic development theory suggests that as countries get richer income distribution worsens but eventually income distribution improves. HIGH GAP DIST. LOW LEVEL OF DEVELOPMENT HIGH

Non experimental CROSS SECTIONAL TREND LONGITUDINAL COHORT PANEL Source: Sampieri et al. 1998: 192

NON EXPERIMENTAL RESEARCH l l CROSS SECTIONAL: The researcher would take a sample of countries with different levels of development (low, medium, high) and analyze the match between theory and facts. This approach is the weakest to determine causality, but strong in generalizing.

GINI CONCENTRATION RATIOS LOW INCOME Bangladesh Sri Lanka MIDDLE INCOME Mexico Brazil Costa Rica HIGH INCOME USA Sweden Canada Japan GINI. 375. 485. 523. 569. 485. 369. 288. 338. 285 GINI=0 TOTAL EQUALITY GINI =1 TOTAL INEQUALITY

NON EXPERIMENTAL RESEARCH l l LONGITUDINAL: The researcher would select a country or countries that have move from lower to higher levels of development (eg. USA, Canada, etc. ) and determine whether income distribution over time followed the trend suggested by theory Causality can be infer but lacks detail about the true causes as well as weak on generalization.

LONGITUDINAL l l l TENDENCY: EVOLUTION OF INCOME DISTRIBUTION OVER TIME IN MEXICO. COHORT: EVOLUTION OF INCOME DISTRIBUTION OVER TIME OF MEXICAN WOMEN BORN AFTER 1960. PANEL: EVOLUTION OF INCOME DISTRIBUTION OF THE SAME GROUP OF WOMEN OVER TIME (MARIA, SUSAN, BRENDA, JOAN, GLORIA & ISABEL)

CASE (S) STUDY l l l The researcher will take a case (s) study and analyze in depth what factors (policies, institutions, political systems, culture, etc. ) are associated with improving income distribution in a given country. Why is income distribution better in Chile than in Mexico and Brazil despite having the same level of development? Improves details and understanding of causes and effect but lacks generalization.

Experimental vs. Quasiexperimental Research Designs l l Experimental research design: The researcher has control over the experiment in terms of sample selection, treatment, environment, etc. Experimental designs are typical in psychology, medicine, education, etc. Quasi-experiments: The researcher does not have control over the experiment, rather the experiment occurs in a “natural” setting. Quasi-experimental design are typical in economics, sociology, public administration, urban planning, political sciences, etc.

Things to consider as rival or alternative hypothesis A rival hypothesis is an alternative explanation that may invalidate what we think as the cause. l History: Events that happen in the outside world during the experiment. l Maturation: Acquisition and processing of information by subjects is not constant through time.

Instrumentation: How the instrument (e. g. survey) is conducted. l Selection: Who is and who isn’t included in the experiment. l Rivalry between treatment and control group: How aware the subjects of the experiment. l A combination (interaction) between the above threats to validity. (e. g. instrumentation-history) l

RESEARCH DESIGN 0 t = Observation in time t of experimental group X = Treatment 0 c = Control group

Research design with limited power l POST-TEST ONLY X l O 1 POST-TEST WITH CONTROL GROUP X O 1 Oc

l PRE-TEST POST-TEST O 1 l X O 2 PRE-TEST POST-TEST WITH CONTROL GROUP O 1 Oc X O 2 Oc

Research designs with more causal power l CONTROL WITH MORE OBSERVATION IN THE PRETEST O 1 Oc O 2 Oc O 3 Oc X O 4 Oc

l PRE-TEST POST-TEST REMOVING THE TREATMENT O 1 X O 2 X O 3 X 04

CHANGES TO LOOK FOR l 1. CONVERGENCE-DIVERGENCE Positive change in the treatment group without change in the control group Treatment Control Treatment Pre-test Post-test

Divergence l Positive increments at a different rate Treatment Control Pre-test Post-test

Convergence l The treatment group catches up with the control group Treatment Control Pre-test Postest

Cross pattern l The treatment group overpass the control group Treatment Control Pre-test Post-test

Research design with more power (time series) l Pre-test post-test O 1 l O 2 O 3 X O 4 O 5 O 6 Pre-test post-test with control group O 1 O 2 O 3 Oc Oc Oc X O 4 O 5 O 6 Oc Oc Oc

Changes to look for in time series No effect Change on the rate or slope Change on the intercept PRE-TEST X POST-TEST TIME

Several possible problems arise that are related to model misspecification and spurious relationships. • • Thus, we need to control for confounding factors and alternative explanations!!!

Model Misspecification and Spuriousness l Antecedent variable: A variable that indirectly affecting the relationship between two other variables. l For example, Ivy league education increases income. l However, parental wealth and legacy admissions affect Ivy league education. Thus, income of graduates from Ivy League schools may not be random.

Model Misspecification and Spuriousness l Intervening Variable: These may be spuriously related to another relationship. l Drinking coffee causes cancer. l Drinking coffee may not be the cause of cancer, but rather the fact that smokers are also coffee drinkers.

Model Misspecification and Spuriousness l Alternative Variables: We also want to control for variables that would bias our results if omitted. l In this case, the X variables in a model would produce biased estimates, undermining their validity and producing error that leads to inaccurate inferences. To forecast correctly the number of medals we need to know something about institutions and sports culture of a country besides level of development. l