Statistics

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Pearson r is used to:

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Assess the relationship between two variables that are measured with continuous data(age, exam score)

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Partial correlation describes the:

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Index of the relationship with the influence of a third variable removed.

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ANOVA is used to:

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Test hypotheses based on population values.

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A T-test is used to:

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Analyze data from a study that has one independent variable (with only two levels) and one dependent variable.

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In a negatively skewed distribution:

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Most scores are very high, the mean is lower than the median, and the median is lower than the mode.

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Counterbalancing is used in studies to:

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Control for order effects by presenting multiple treatments to subjects in varying orders for different subgroups.

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Maturation refers to:

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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

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Instrumentation:

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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)

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Path analysis is useful for:

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As a causal modeling technique, it can be used for examining unidirectional causal relationships among a set of measured traits

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To use trend analysis, you need:

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a quantitative independent variable

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Autocorrelation refers to:

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Observations obtained close together in time from the same subjects tend to be highly correlated

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Best control for practice effects in an experiment:

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Counterbalancing (

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Difference between external & internal validity:

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External: concerned with generalizing study to real world
Internal: plausability regarding causal relationships between study variables

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Eigenvalue

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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)

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Standard score

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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)

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Multiple baseline design

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Single-subject design in which a treatment is applied sequentailly across settings, behaviors, or subjects (baseline data is first collected in each setting)

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Advantage of MANOVA over multiple ANOVA's is:

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reduces the probability that a Type I error will be made

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The standard errors of the mean, measurment, & estimate express error in terms of:

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Standard deviation

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Construct Validity

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How weel does the test measure the theoretical construc (e.g. intelligence) that is attempting to measure

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Range of the standard error of measurement:

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0 to the standard deviation of the test

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Range of the validity coefficient

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-1 to +1

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Range of reliability coefficient

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0 to +1

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In terms of reliability & validity, increasing test length will:

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affect reliability more than validity

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Rejecting a false null hypothesis means:

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Your study has power

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If you go from a one-tailed test to a two-tailed test, you are..

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Lees likely to correctly reject the null (as you are losing power)

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History refers to:

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A threat to internal validity when any EXTERNAL event that affects scores on theDV (besides the experimental treatment)

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Statistical Regression:

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Tendency for extreme scores to revert to the average upon re-testing (batting average)
Can be a threat to internal validity

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Differential Mortality

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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

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Controlling threats to internal validity:

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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

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THREATS to external validity

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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

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Ways to increase External Validity

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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

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True vs. Quasi Experimental Research

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True = investigator randomly assigns subjects to different groups
Quasi = random assignment is not possible

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Developmental Research

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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

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Single-Subject Designs

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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

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Z-Score

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A measure of how many SD's a given score is from the mean

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T-Score

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based on 10 point intervals, where T=50, as the mean, and every 10 points above or below is one SD

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Stanine Scores

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distribution of 9 equal intervals 1-9, mean = 5 and SD = 2

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Standard Error of the Mean

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expected error of a given sample mean. It is obtained by dividing the SD by the square root of N

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Type I Error:

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conclude there is a difference, when in fact there is not

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Type II Error

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concluding there is no difference, when in fact there is

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Factors that affect Power

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Sample size = increases
Alpha - increases
One-tailed - increases
Magnitude of Population Difference - increases

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T-Test

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(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

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Post Hoc Tests

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1)Schaffe = most conservative (least Type 1 error)
2)ukey is best for pairwise comparisons

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ANOVA

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One-way = 1 independed variable & more than 2 independent groups
Factorial (2-way) = used when there are more than one independent variable

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MANOVA

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used when a study has 2 or more dpendent variables and one or more independent variable

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Mann-Whitney

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non-parametric test used forrank-ordered data when there are more than 2 independent groups

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Factors Affecting the Pearson r

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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

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Misc. Correlation Coefficients

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Point-Biserial =relates 1 continous variable & 1 dichotomous
Contingency = correlation btw. 2 nominal variables, that each have at least 3 categories

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Spearman's Rho

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Used to correlate 2 variables that are ordinally ranked

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Eta

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used to measure continous data when the relationship is non-linear

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Canonical Correlation

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used to relate 2 or more predictors to 2 or more criterion variables

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Discriminant Function Analysis

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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)

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Suppressor Variable

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an extraneos variabel that results in a spuriously LOWER correlation (it suppresses the relationship)

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Trend Analysis

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statitical technique for looking at the pattern of change between two quantitative variables (include linear, quadratic, cubic, & quartic)

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Rosenthal Effect

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when the experimenter's expectancies influence subject's responses on a dependent variable in the direction predicted

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Solomon 4 group design is used to

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Evaluate the effects of testing

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ANCOVA is used to

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reduce error variance due to an extraneous variable

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In regards to SD & Validity, the Standard Error of Estimate has:

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a direct relationship with SD of the criterion & an indirect relationship with validity

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Item Response Theory is based on the premis that:

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when test content is of varying difficulty, uniform scales of measurement can be applied to persons of different ability level

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In weighting variables in a multiple regression equation, we use:

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line of best fit

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Cluster analysis is used for the purpose of:

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deriving several subgroups from a cluster of dependent variables

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Advantage of ANOVA over multiple T-Tests is:

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Reduces Type I error