
Other names for "tests"
 assessment
 test
 measure
 instrument
 scale

reliability
 a test is reliable if the data is consistent over time when the test is
 repeated


Testretest
when someone is administered the same test over time

Splittest
Taking parts of a test and comparing those parts to one another

Validity
Is the test supposed to test what it is supposed to test?

Tests of validity
 content validity
 construct validity
 predictive validity

content validity
does the content make sense according to the topic?

predictive validity
does it predict a difference in behavior?

Theory
A general statement about the relationship between constructs or events.

Hypothesis
 Formal prediction about the relationship between two or more variables
 that is logically derived from the theory
 derived from a theory

correlational research
 When: used to investigate relationships between variables
 How:
 measure both or all groups involved in study
 limit: does not give a
 cause
 keywords: relationship, related, linked to, associated

Correlation coefficient
 between 1 to 1
 the closer to one the correlation is, the stronger
 the relationship is

positive correlation
 Both variables travel in the same direction
 01 (0 to .2= no
 correlation, .2 to .4= weak correlation, .4 to .6 =moderate correlation,
 .6 to .8 =strong correlation
 Example: The more time one spends
 studying, the better grades that person will recieve on an exam

Negative Correlation
 Both variables that travel in opposite directions
 0 to 1 (0 to
 .2= no correlation,
 .2 to .4= weak correlation, .4 to .6
 =moderate correlation, .6 to .8
 =strong correlation
 Example:
 The more time someone spends partying the person is most likely to
 recive a lower score on an exam

Experimental Research
 When: when you want to draw cause and effect
 How: manipulate one of
 the variables under controlled conditions
 limit: hard to make
 entirely random and hard to control variables

Independent variable
 The variable in a experiment that is being manipulated
 causes direct
 variable
 Example: Different temperatures may cause subjects to
 score higher on test ( temperature is independant variable)

Dependent variable
The outcome that is affected by the independent variable

Why you may want more than 1 Independent variable
There may be interactions between variables

Why you may want more than 1 Dependent variable
The Independant variable may have influenced another dependent variable

A true experiment
 A manipulated independent variable while everything else is controlled
 randomly
 assigned groups
 There will be an infinite amount of independent
 variables if groups aren't randomly assigned

Experimental Groups
The group that recieves treatment(independent variable)

Control Group
The group that doesn't get treatment

Sampling Bias
 subjects tested are not chosen by random
 corrected by random
 assignment

Confounding bias
 other variables may have affected dependent variable
 corrected by
 randomly assigning groups

Placebo effects
 People belive that treatment actually works
 corrected by blind study

Experimentor effect
 The experimenter's expectations influence results
 corrected by a
 double blind study

Social desirability effect
 subject is not comfortable with test or answers on test
 anonymity or
 nonself report

Significance Test
 Tests that determine if group averages were found from real effects or
 just by chance
 Example: ANOVA, Chisquare, or ttest

Pvalue
The number that determines the significance between dependent variables

Interpreting Pvalue
 n = number of subjects in group
 N = total number of subjects in group
 P
 < .05 means that there is a significant difference
 P > .10
 means that there is not a significant difference
 P between .05 and
 .10 means that there is a marginally significant. This usually means
 that you need to get more data and withold judgement

Replication
 When an experiment is done by another researcher. If similar results are
 found in future experiments then the experiment is seen as significant
 in terms of a genuine relationship

