Non-Experimental designs: Developmental designs & Small-N designs

Psych 231: Research Methods in Psychology

Announcements

Journal Summary 2 due this week– No names, SSN only

Stages of survey research cont.

Stage 7) Pre-testing the survey instrument– Fix what doesn’t seem to be working

Stage 8) Selecting and training interviewers– For telephone and in-person surveys– Need to avoid interviewer bias

Stage 9) Implementing the survey Stage 10) Coding and entering the data Stage 11) Analyzing the data and preparing a

final report

Error in survey research Sampling error

– Are there differences in your sample (compared to the population as a whole)?

– Response rate • What proportion of the sample actually responded to the

survey?– Hidden costs here - what can you do to increase response

rates

• Non-response error (bias)– Is there something special about the data that you’re

missing? From the people who didn’t respond

Measurement error (validity and reliability)– Are your questions really measuring what you want

them to?

Quasi-experiments What are they?

– Almost “true” experiments, but with an inherent confounding variable

• Design includes a quasi-independent variable– General types

• An event occurs that the experimenter doesn’t manipulate• Interested in subject variables• Time is used as a variable

Quasi-experiments Advantages

– Allows applied research when experiments not possible

– Threats to internal validity can (sometimes) be assessed

Disadvantages– Threats to internal validity may exist (which can

not be addressed)• Be careful when making causal claims

– Statistical analysis can be difficult• Most statistical analyses assume randomness

Quasi-experiments Common types

– Program evaluation– Nonequivalent control group designs– Interrupted time series designs

Quasi-experiments Program evaluation

– Research on programs that is implemented to achieve some positive effect on a group of individuals.

– e.g., does abstinence from sex program work in schools

– Steps in program evaluation– Needs assessment - is there a problem?– Program theory assessment - does program address the

needs?– Process evaluation - does it reach the target population? Is it

being run correctly?– Outcome evaluation - are the intended outcomes being

realized?– Efficiency assessment- was it “worth” it? The the benefits

worth the costs?

Quasi-experiments Nonequivalent control group designs

– with pretest and posttest (most common)

participants

Experimentalgroup

Controlgroup

Measure

Measure

Non-Random Assignment

Independent Variable

Dependent Variable

Measure

Measure

Dependent Variable

– But remember that the results may be compromised because of the nonequivalent control group

Quasi-experiments

Treatment

Time series designs– Observe a single group multiple times prior to and after a

treatment

– The pretest observations allow the researcher to look for pre-existing trends

– The posttest observations allow the researcher to look for changes in the trends

• Is it a temporary change, does it last, etc.?

Obs Obs Obs Obs Obs Obs

Quasi-experiments Time series designs

– Variations of basic time series design• Addition of a nonequivalent no-treatment control group

time seriesO O O T O O O & O O O _ O O O

• Interrupted time series – When the treatment is not created or manipulated by the

researcher

Developmental designs

Used to study changes in behavior that occur as a function of age changes

Non-experimental or quasi-experimental– Age typically serves as a quasi-independent

variable Three major types

– Cross-sectional – Longitudinal– Cohort-sequential

Developmental designs Cross-sectional design

– Groups are pre-defined on the basis of a pre-existing variable

• Study groups of individuals of different ages at the same time

– Use age to assign participants to group

– Age is subject variable treated as a between-subjects variable

Developmental designs

– Advantages:• Can gather data about different groups (i.e., ages) at

the same time

• Participants are not required to commit for an extended

period of time

Cross-sectional design

Developmental designs

– Disadvantages:• Individuals are not followed over time

– Cohort (or generation) effect: individuals of different ages may be inherently different due to factors in the environment» Example: are 5 year old different from 13 year olds just

because of age, or can factors present in their environment contribute to the differences?

• Cannot infer causality due to lack of control

Cross-sectional design

Developmental designs

– Follow the same individual or group over time• Age is treated as a within-subjects variable

– Rather than comparing groups, the same individuals are compared to themselves at different times

– Repeated measurements over extended period of time

• Changes in dependent variable reflect changes due to aging process– Changes in performance are compared on an

individual basis and overall

Longitudinal design

Developmental designs

– Advantages:• Can see developmental changes clearly• Avoid some cohort effects (participants are all from

same generation, so changes are more likely to be due to aging)

• Can measure differences within individuals

Longitudinal design

Developmental designs

– Disadvantages• Can be very time-consuming• Can have cross-generational effects:

– Conclusions based on members of one generation may not apply to other generations

• Numerous threats to internal validity:– Attrition/mortality

– History

– Practice effects

» Improved performance over multiple tests may be due to practice taking the test

• Cannot determine causality

Longitudinal design

Developmental designs

– Measure groups of participants as they age• Example: measure a group of 5 year olds, then the

same group 5 years later, as well as another group of 5 year olds

– Age is both between and within subjects variable

• Combines elements of cross-sectional and longitudinal designs

• Addresses some of the concerns raised by other designs

– For example, allows to evaluate the contribution of generation effects

Cohort-sequential design

Developmental designs

– Advantages:• Can measure generation effect• Less time-consuming than longitudinal

– Disadvantages:• Still time-consuming• Still cannot make causal claims

Cohort-sequential design

Small N designs

What are they?– Historically, these were the typical kind of design

used until 1920’s when there was a shift to using larger sample sizes

– Even today, in some sub-areas, using small N designs is common place

• (e.g., psychophysics, clinical settings, expertise, etc.)

Small N designs

One or a few participants Data are not analyzed statistically; rather rely

on visual interpretation of the data Observations begin in the absence of

treatment (BASELINE) Then treatment is implemented and changes

in frequency, magnitude, or intensity of behavior are recorded

Small N designs

Baseline experiments – the basic idea is to show:

1. when the IV occurs, you get the effect

2. when the IV doesn’t occur, you don’t get the effect (reversibility)

Before introducing treatment (IV), baseline needs to be stable

Measure level and trend

Small N designs

Level – how frequent (how intense) is behavior?– Are all the data points high or low?

Trend – does behavior seem to increase (or decrease)– Are data points “flat” or on a slope?

ABA design

ABA design (baseline, treatment, baseline)

A B A

Steady state (baseline) | Transition steady state | Reversibility

– The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.)

Small N designs Advantages

– Focus on individual performance, not fooled by group averaging effects

– Focus is on big effects (small effects typically can’t be seen without using large groups)

– Avoid some ethical problems – e.g., with non-treatments

– Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices)

– Often used to supplement large N studies, with more observations on fewer subjects

Small N designs

Disadvantages– Effects may be small relative to variability of

situation so NEED more observation– Some effects are by definition between subjects

• Treatment leads to a lasting change, so you don’t get reversals

– Difficult to determine how generalizable the effects are

Small N designs

Some researchers have argued that Small N designs are the best way to go.

The goal of psychology is to describe behavior of an individual

Looking at data collapsed over groups “looks” in the wrong place

Need to look at the data at the level of the individual

Next time

Statistics (Chapter 14)