Experimentation pt. 1

manipulated variable

a variable that is controlled by the researchers, who assigns participants go experience particular levels of the variable

measured variable

records of thoughts, feelings, or behaviors, not directly influenced by the researchers

control variables

any variable the researcher intentionally holds constant across conditions

independent variable

the manipulated variables in an experiment

conditions

the levels or versions of the independent variable

dependent variable

the measured variable in an experiment

what are two ways to check construct validity of manipulated variables

• pilot study

• manipulation check

pilot study

conducted before the actual study to check the construct validity of a manipulation

manipulation check

an extra measure designed to see how well a manipulation worked

• ex. watch a positive thing, manipulation check to see if desired change occurred, then do dependent variable

different types of variables

• manipulated variable

• measured variable

• control variable

• independent variable (and conditions)

• dependent variable

types of conditions

• control group

• treatment group

• placebo group

control group

a condition that is supposed to represent no treatment, or a neutral state

treatment group

the conditions of interest, which are compared to the control group (not all experiments have a true control vs. treatment design)

placebo group

a control group who believes they’re a treatment group, with the goal of ruling out expectancy effects (very few psych experiments have a true placebo group)

well designed experiments have what

maximizes internal validity compared to other designs

confound

anything that differs between your groups other than the levels of the independent variables

design confound

something that inherently varies along with the independent variable 

• creates systematic variability (problem!)

unsystematic variability

created when something differs among participants but does not systematically co-occur with the independent variable

systematic variability

people in one condition vary from people in the other condition in more than just the way you’re manipulating

true experiment essential characteristics

1. manipulation of one or more independent variables

2. random assignment to conditions

random assignment

each participant has an equal chance of being in each condition

• maximizes the likelihood that unintended variability is unsystematic instead of systematic

• avoids selection effects

selection effects

when the kind of person in one condition are systematically different from the ones in other conditions

chance is lumpy

• randomness doesn’t always end up seeing random

• can create a ‘failure of random assignment’

matched groups (or matching)

ensuring that your groups are equivalent in important ways 

• pair (match) people on the characteristic of interest, then split the pair across conditions through random assignment

types of experiments/designs

• between subjects design

  • posttest-only design

  • pretest-posttest design

• within subjects design

  • concurrent measures

  • repeated measures

between subjects design

each participant is only in one experimental condition

posttest-only design

between subjects, participants undergo the manipulation (just one condition) and then complete the measures (once)

pretest-posttest design

between subjects design, participants first complete the measures, then the manipulation, then the measures again

advantages of between subjects

• test and control for selection effects (see if there is a systematic difference, if there was a difference in subjects between assigned groups)

• test and control for failures of random assignment

disadvantages of between subjects design

• might create demand characteristics

• people might think they should be consistent in their responses

demand characteristics

might clue participants in to what you’re doing, so participants might realize what researchers are interested in, and thing might change because of their beliefs

within subjects design

each participant is in all experimental conditions

concurrent measures

within subjects design, participants experience all levels of the independent variable at once

repeated measures design

within subjects design, participants experience levels of the independent variable one after the other, with the measures following each level of the independent variable

why would you want a within subjects design

• guarantees equivalence of groups (no selection effects)

• functionally doubles your sample size (for two conditions)

  • statistical power, p-values = function of effect size + sample size

statistical power

ability of a study to get a statistically significant effect, assuming the effect is real

why would you want a between subjects design

reduces possibility of order effects

order effects

a confound that occurs when experiencing one condition changes how participants react to subsequent conditions in a within-subjects design

types of order effects

• practice effects

• fatigue effects

• carryover effects

• sensitization effects 

practice effects

participants get better at the measures

fatigue effects

participants get worse at the measures

carryover effects

effects of one conditions contaminate subsequent responsess

sensitization effects

participants become suspicious or clued in from earlier discussions

how do you deal with some order effects

counterbalancing (doesn’t FIX order effects, it just allows you to check for them)

counterbalancing

randomly assigning participants to experience the conditions in different orders

types of counterbalancing

• full

• partial

full counterbalancing

all possible orders are represented

partial counterbalancing

only some orders are represented