RM2 Exam Defs

Main Effect

effect of one IV averaged across second IV

Interactions / difference of the differences

the differences between levels of one IV differ across levels of another IV

Sample distribution of mean

mean of distribution of sample means gathered on all sample selections from a population

Central limit theorem

for a population with a µ and sd, the sample mean distrubution will have a mean = µ and sd equal to SE as samples approach infinity

expected value

the mean of a sample mean distribution

SE of mean

amount of error wee can expect between population / sample means due to chance when Ho is true

Types of One Way/One Factor Designs

Randomized Groups (between subject) - randomized between groups

Matched Subject - subjects matched by important variable/characterisitc

Repeated Measures (within subject) - experience all condition of IV

pre/post test

measuring dv before and after intervention

advan: less participants , higher statistical power

disavan: extraneous variables (time, order effects)

Factorial design

2 or more IV’s

Mixed factorial factorial

combine one ways such as between and within group combination

combined strategy factorial

combine experimental and non experimental/quasi

counterbalancing

presenting levels of IV in different orders to different people

Partial/incomplete= Latin Square

Probability

attempt to predict future likelihood of events, based on previous information

Gamblers Falacy

mistaken belief that past random outcomes predict future random events

binomial data

occurs when only 2 possibilities on measurement scale

goal of statistical analysis

1) is variance due to IV

2) identify error variance

3) find statistical solution

inferential statistics

determine observed differences between means of experimental conditions are due to error variance

hypothesis testing

decision made due to value of parameters

sample error

difference between a sample statistic and true population parameter