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62 Cards in this Set

  • Front
  • Back
Pearson r is used to:
Assess the relationship between two variables that are measured with continuous data(age, exam score)
Partial correlation describes the:
Index of the relationship with the influence of a third variable removed.
ANOVA is used to:
Test hypotheses based on population values.
A T-test is used to:
Analyze data from a study that has one independent variable (with only two levels) and one dependent variable.
In a negatively skewed distribution:
Most scores are very high, the mean is lower than the median, and the median is lower than the mode.
Counterbalancing is used in studies to:
Control for order effects by presenting multiple treatments to subjects in varying orders for different subgroups.
Maturation refers to:
any internal change (biological or psychological) that occurs in subjects while an experiment is in progress - and has a systematic effect on the DV - MAY be a threat to internal validity
Can be a threat to internal validity, when the measuring process changes between the pre and post tests (e.g. raters become better at rating with practice)
Path analysis is useful for:
As a causal modeling technique, it can be used for examining unidirectional causal relationships among a set of measured traits
To use trend analysis, you need:
a quantitative independent variable
Autocorrelation refers to:
Observations obtained close together in time from the same subjects tend to be highly correlated
Best control for practice effects in an experiment:
Counterbalancing (
Difference between external & internal validity:
External: concerned with generalizing study to real world
Internal: plausability regarding causal relationships between study variables
Stat that indicates the degree to which a particular factor (in a factor analysis) is accounting for varaiability (so it really tells us the explanatory power of the factor)
Standard score
Type of norm-referenced score that is interpreted in terms of how many SD units a score falls above or below the mean (such as z-scores & T-scores)
Multiple baseline design
Single-subject design in which a treatment is applied sequentailly across settings, behaviors, or subjects (baseline data is first collected in each setting)
Advantage of MANOVA over multiple ANOVA's is:
reduces the probability that a Type I error will be made
The standard errors of the mean, measurment, & estimate express error in terms of:
Standard deviation
Construct Validity
How weel does the test measure the theoretical construc (e.g. intelligence) that is attempting to measure
Range of the standard error of measurement:
0 to the standard deviation of the test
Range of the validity coefficient
-1 to +1
Range of reliability coefficient
0 to +1
In terms of reliability & validity, increasing test length will:
affect reliability more than validity
Rejecting a false null hypothesis means:
Your study has power
If you go from a one-tailed test to a two-tailed test, you are..
Lees likely to correctly reject the null (as you are losing power)
History refers to:
A threat to internal validity when any EXTERNAL event that affects scores on theDV (besides the experimental treatment)
Statistical Regression:
Tendency for extreme scores to revert to the average upon re-testing (batting average)
Can be a threat to internal validity
Differential Mortality
Threat to internal validity, when people who drop out of one of the groups, differ in significant ways from people who remain in the study
Controlling threats to internal validity:
1)Random assignment - most powerful method
2)Matching - identifying subjects who are similar & pairing off (bestwhen sampl size is small)
3) Blocking - analyzing the statistical effects of an extraeous variable
4)Holding constant - only including subjects who are homogenous
5)ANCOVA - statisical strategy for removing the effects of extraneous variables
THREATS to external validity
1)Int. btw Selection & Tx -
2)Int. btw. Hx & Tx -can't generalize to other settings time/period
3)Int. btw. Testing & Tx - can't geberalize unless had a pretest
4)Demand Chracteristics - cues in setting allowed subjects to guess hypothesis
5)Hawthorne Effect
6)Order Effects - problem with repeated measures design
Ways to increase External Validity
1)Random Selection-ensuress equal representativeness
2)Naturalistic Research - controls for Hawthorne & demand characteristics
3)Single/Double Blind Research-subjetcs/researchers don't know which treatment has been assigned
4)Counterbalancing - controls for order effects
True vs. Quasi Experimental Research
True = investigator randomly assigns subjects to different groups
Quasi = random assignment is not possible
Developmental Research
1)Longitudial Design = same people studied over time
2)Cross-Sectional = different groups, divided by age are studied at the same time
3)Cross-Sequential = representative samples of different ages groups are assessed on two or more occasions
Single-Subject Designs
1)AB Design - single baseline foloowed by single tx
2)Reversal (Withdrawal) Design - baseline, treatment, return to baseline, (ABA, or ABAB)
3)Multiple Baseline - IV is sequentially administered across 2 or more subjects, behaviors, or settings
A measure of how many SD's a given score is from the mean
based on 10 point intervals, where T=50, as the mean, and every 10 points above or below is one SD
Stanine Scores
distribution of 9 equal intervals 1-9, mean = 5 and SD = 2
Standard Error of the Mean
expected error of a given sample mean. It is obtained by dividing the SD by the square root of N
Type I Error:
conclude there is a difference, when in fact there is not
Type II Error
concluding there is no difference, when in fact there is
Factors that affect Power
Sample size = increases
Alpha - increases
One-tailed - increases
Magnitude of Population Difference - increases
(Parametric test used to test hypotheses about two means)
One Sample = comparing one sample to a population mean
Independent Samples = comparing 2 means that are unrelated
Correlated Samples = compares means that are matched or related to each other
Post Hoc Tests
1)Schaffe = most conservative (least Type 1 error)
2)ukey is best for pairwise comparisons
One-way = 1 independed variable & more than 2 independent groups
Factorial (2-way) = used when there are more than one independent variable
used when a study has 2 or more dpendent variables and one or more independent variable
non-parametric test used forrank-ordered data when there are more than 2 independent groups
Factors Affecting the Pearson r
1)Linearity - assumes the reationship is linear
2)Homoscedasticity - assumes dispersion of scores is equal
3)Range of Scores - assumes a wide range of scores
Misc. Correlation Coefficients
Point-Biserial =relates 1 continous variable & 1 dichotomous
Contingency = correlation btw. 2 nominal variables, that each have at least 3 categories
Spearman's Rho
Used to correlate 2 variables that are ordinally ranked
used to measure continous data when the relationship is non-linear
Canonical Correlation
used to relate 2 or more predictors to 2 or more criterion variables
Discriminant Function Analysis
scores on 2 or more variables are combined to determine whether they can be used to predict which criterion group a person belongs in (i.e. high or low achievement group)
Suppressor Variable
an extraneos variabel that results in a spuriously LOWER correlation (it suppresses the relationship)
Trend Analysis
statitical technique for looking at the pattern of change between two quantitative variables (include linear, quadratic, cubic, & quartic)
Rosenthal Effect
when the experimenter's expectancies influence subject's responses on a dependent variable in the direction predicted
Solomon 4 group design is used to
Evaluate the effects of testing
ANCOVA is used to
reduce error variance due to an extraneous variable
In regards to SD & Validity, the Standard Error of Estimate has:
a direct relationship with SD of the criterion & an indirect relationship with validity
Item Response Theory is based on the premis that:
when test content is of varying difficulty, uniform scales of measurement can be applied to persons of different ability level
In weighting variables in a multiple regression equation, we use:
line of best fit
Cluster analysis is used for the purpose of:
deriving several subgroups from a cluster of dependent variables
Advantage of ANOVA over multiple T-Tests is:
Reduces Type I error