Types of research design – experiments Chapter 8 in Babbie & Mouton (2001) Chapter 8 in Babbie & Mouton (2001) Introduction to all research designs Introduction

Types of research design – experiments

Chapter 8 in Babbie & Mouton (2001)

Introduction to all research designsAll research designs have specific

objectives they strive forHave different strengths and

limitationsHave validity considerations

SUMBER: web.uct.ac.za/.../Types%20of%20research%20d...

Validity considerations

When we say that a knowledge claim (or proposition) is valid, we make a JUDGEMENT about the extent to which relevant evidence supports that claim to be true

Is the interpretation of the evidence given the only possible one, or are there other plausible ones?

"Plausible rival hypotheses" = potential alternative explanations/claims

e.g. New York City's "zero tolerance" crime fighting strategy in the 1980s and 1990s - the reverse of the "broken windows" effect


The logic of causal social research in the controlled experiment

Explanatory rather than descriptive Different from correlational research - one

variable is manipulated (IV) and the effect of that manipulation observed on a second variable (DV)

If … then ….

E.g. "Animals respond aggressively to crowding" (causal) "People with premarital sexual experience have more

stable marriages" (noncausal)


Three pairs of components:

Independent and dependent variables

Pre-testing and post-testingExperimental and control groups


Components

VariablesDependent (DV)Independent (IV)

Pre-testing and post-testingO X O

Experimental and control groupsTo off-set the effects of the experiment

itself; to detect effects of the experiment itself


The generic experimental design:

R O1 X O2

R O3 O4

The IV is an active variable; it is manipulated

The participants who receive one level of the IV are equivalent in all ways to those who receive other levels of the IV


Sampling

1. Selecting subjects to participate in the researchCareful sampling to ensure that results

can be generalized from sample to population

The relationship found might only exist in the sample; need to ensure that it exists in the population

Probability sampling techniques


Sampling

2. How the sample is divided into two or more groups is importantto make the groups similar when they

start offrandomization - equal chancematching - similar to quota sampling

proceduresmatch the groups in terms of the most

relevant variables; e.g. age, sex, and race


Variations on the standard experimental design

One-shot case study

X O

No real comparison


A famous one-group posttest-only design

Milgram's study on obedienceObedience to authorityThe willingness of subjects to follow E's orders

to give painful electrical shocks to another subject

A real, important issue here: how could "ordinary" citizens, like many Germans during the Nazi period, do these incredibly cruel and brutal things?

If a person is under allegiance to a legitimate authority, under what conditions will the person defy the authority if s/he is asked to carry out actions clearly incompatible with basic moral standards?


One-group pre-test post-test design

O1 X O2


Example

We want to find out whether a family literacy programme enhances the cognitive development of preschool-age children.

Find 20 families with a 4-year old child, enrol the family in a high-quality family literacy programme

Administer a pretest to the 20 children - they score a mean of say 50 on the cognitive test

The family participates in the programme for twelve months

Administer a post-test to the 20 children; now they score 75 on the test - a gain of 25


Two claims/conclusions:

1 The children gained 25 points on average in terms of their cognitive performance

2 the family literacy programme caused the gain in scores

VALIDITY - rival explanations


Static-group comparison

X O O


Evaluating research (experiments)

We know the structure of researchWe understand designsWe know the requirements of "good"

researchThen we can evaluate a studyIs it good? Can we believe its

conclusions?Back to plausible rival hypotheses


Validity in designs

If the design is not valid, then the conclusions drawn are not supported; it is like not doing research at all

Validity of designs come in two parts:Internal validity

can the design sustain the conclusions?External validity

can the conclusions be generalized to the population?


Internal validity

Each design is only capable of supporting certain types of conclusions e.g. only experiments can support conclusions about

causality

Says nothing about if the results can be applied to the real world (generalization)

Generally, the more controlled the situation, the higher the internal validity

The conclusions drawn from experimental results may not accurately reflect hat has gone on in the experiment itself


Sources of internal invalidity

These sources often discussed as part of experiments, but can be applied to all designs (e.g. see reactivity)

HistoryHistorical events may occur that will be

confounded with the IVEspecially in field research (compare the

control in a laboratory, e.g. nonsense syllables in memory studies


Maturation

Changes over time can be caused by a natural learning process

People naturally grow older, tired, bored, over time


Testing (reactivity)

People realize they are being studied, and respond the way they think is appropriate

The very act of studying something may change it

In qualitative research, the "on stage" effects


The Hawthorne studies

Improved performance because of the researcher's presence - people became aware that they were in an experiment, or that they were given special treatment

Especially for people who lack social contacts, e.g. residents of nursing homes, chronic mental patients


Placebo effect

When a person expects a treatment or experience to change her/him, the person changes, even when the "treatment" is know to be inert or ineffective

Medical research"The bedside manner", or the power

of suggestion


Experimenter expectancy

Pygmalion effect - self-fulfilling prophecies of e.g. teachers' expectancies about student achievement

Experimenters may prejudge their results - experimenter bias

Double blind experiments: Both the researcher and the research

participant are "blind" to the purpose of the study.

They don't know what treatment the participant is getting


Instrumentation

Instruments with low reliability lead to inaccurate findings/missing phenomena

e.g. human observers become more skilled over time (from pretest to posttest) and so report more accurate scores at later time points


Statistical regression to the mean

Studying extreme scores can lead to inflated differences, which would not occur in moderate scorers


Selection biases

Selection subjects for the study, and assigning them to E-group and C-group

Look out for studies using volunteers


Attrition

Sometimes called experimental (or subject) mortality

If subjects drop out, it creates a bias to those who did not e.g. comparing the effectiveness of family therapy with

discussion groups for treatment of drug addiction addicts with the worst prognosis more likely to drop out

of the discussion group will make it look like family therapy does less well than

discussion groups, because the "worst cases" were still in the family therapy group


Diffusion or imitation of treatments

When subject can communicate to each other, pass on some information about the treatment (IV)


Compensation

In real life, people may feel sorry for C-group who does not get "the treatment" - try to give them something extrae.g. compare usual day care for street

children with an enhanced day treatment condition

service providers may very well complain about inequity, and provide some enhanced service to the children receiving usual careSUMBER: web.uct.ac.za/.../Types%20of%20research%20d...

Compensatory rivalryC-group may "work harder" to

compete better with the E-group


Demoralization

Opposite to compensatory rivalryMay feel deprived, and give up

e.g. giving unemployed high school dropouts a second chance at completing matric via a special education programme

if we assign some of them to a control group, who receive "no treatment", they may very well become profoundly demoralized


External validity

Can the findings of the study be generalized?

Do they speak only of our sample, or of a wider group?

To what populations, settings, treatment variables (IV's), and measurement variables can the finding be generalized?


External validity

Mainly questions about three aspects: Research participants Independent variables, or manipulations Dependent variables, or outcomes

Says nothing about the truth of the result that we are generalizing

External validity only has meaning once the internal validity of a study has been established

Internal validity is the basic minimum without which an experiment is uninterpretableSUMBER: web.uct.ac.za/.../Types%20of%20research%20d...

External validity

Our interest in answering research questions is rarely restricted to the specific situation studied - our interest is in the variables, not the specific details of a piece of research

But studies differ in many ways, even if they study the same variables: operational definitions of the variables subject population studied procedural details observers settings

Generally bigger samples with valid measures lead to better external validity


Sources of external invalidity

Subject selection - Selecting a sample which does not represent the population well, will prevent generalization

Interaction between the testing situation and the experimental stimulus

When people have been sensitized to the issues by the pre-test

Respond differently to the questionnaires the second time (post-test)

Operationalization


Operationalization

We take a variable with wide scope and operationalize it in a narrow fashion

Will we find the same results with a different operationalization of the same variable?


Field experiments

"natural" - e.g. disaster researchStatic-group comparison type Non-equivalent experimental and

control groups


Strengths and weaknesses

StrengthsControlManipulating the IVSorting out extraneous variables

Weaknesses Articifiality - a generalization problemExpenseLimited range of questions


Documents

Types of research design – experiments Chapter 8 in Babbie & Mouton (2001) Chapter 8 in Babbie & Mouton (2001) Introduction to all research designs Introduction