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114 Cards in this Set
- Front
- Back
- 3rd side (hint)
6 Steps for conducting an experimental research study |
1. Develop a hypothesis
2. Choose a research design 3. Select a sample 4. Conduct the study 5. Analyze data 6. Report results |
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Protocol Analysis
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A type of behavioral measurement Askinga subject to "think aloud" while solving a problem in order toidentify underlying cognitive processes or "heeded cognitions
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Interval Recording
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Method for behavior sampling during discrete intervals (i.e., is it occring now). Good for sampling complex beh. with no clear cut beginning or end such as laughing, talking, or playing.
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Event Sampling
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Recording each time the event occurs. Good for beh. that infrequently happen.
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Sequential Analysis
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Coding behavioral sequences rather than isolated beh. events when studying complex social behaviors.
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Situational Analysis
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Observea behavior in a number of settings. Helps increase generalizability of findings
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Nonexperimental Research
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Conducted to collect data on variables.
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Experimental Research
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Conducted to test hypotheses about the relationship between variables. (True exp or Quasi exp)
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Variables
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Characteristics or behaviors that researchers can vary.
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Random Assignment
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"Randomization" helps ensure that any observed diff between groups is due to the IV. Random assignment of S to control or experimental group. Subjects you selected have an equal chance of being assigned to any given group
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Quasi-Exp Research
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Must use intact pre-existing groups or a single treatment group. No random ass b/c your just using one group.
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Random Sampling
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Every member of the pop has an equal chance of being included. Reduces biased sampling.
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Stratified Random Sampling
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Dividing the pop into the "strata" (e.g., SES, ed., gender, age, ract)and then using random sampling.
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Cluster Sampling
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Selecting pre-existing units/clusters/groups of ind. Used when it's not poss to id an entire pop.
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Random Assignment
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Allows an investigator to be more certain that the DV was caused by the IV.
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Random Selection
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Enables the investigator to generalize findings from the pop to the sample.
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Extraneous (confounding) variable
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Source of systematic error that effects the DV, but is irrelevant to the research.
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Techniques to control confounding variables:
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1. Random Assignment
2. Holding the Ext Var Constant 3. Matching S's on the Ext Var 4. Building the Ext Var into the study ("Blocking") 5. Stasticial Control (ANCOVA) |
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Random Error
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Experimental research attempts to minimize fluctuations in S's, conditions, and measuring instruments.
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Internal Validity
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Successfully determining if there is a casual relationship between IV and DV.
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8 Threats to Internal Validity
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1. Maturation (change that occurs within subjects duringcourse of study) 2. History(environmental event) Testing(exposure to test may alter performance on subsequent tests) 3. Instrumentation(changes in accuracy or validity of measuring devices)
4. StatisticalRegression (regression to the mean) 5. 6. Selection (method ofassigning subjects to groups results in systematic differences) 7. Attrition(when subjects who drop-out are different from those who remain. 8. Interactions withSelection (interaction between selection and other factors, such as history) |
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External and Ecological Validity
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Being able to generalize findings to other settings.
Ecological validity= Generalizability to other settings. External validity always limitedby internal validity. |
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4. Threats to External Validity
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1. Interaction betweentesting and treatment-pre-test sensitization
2. Interaction betweenselection and treatment: When subjects in study have characteristics that makethem respond to IV in particular way. 3. Reactivity: Whenparticipants respond to IV in particular way simply because they know they arebeing observed. Demand Characteristics= Cues in the environment that informsubjects of the purpose of the study or what is expected of them. ExperimenterExpectancy: When experimenter unexpectantly provides subjects with cues thatlet them know what is expected. 4. Multiple TreatmentInterference (Order effects, Carryover Effects): controlled by using counterbalanceddesign. The Latin Squaredesign involves administering each level so it appears the same number of timesin each position. |
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Between-Group (S's) Designs
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The effects of diff levels of an IV are assessed by administration each level to a diff group of S's and then comparing the status on the DV.
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Main Effect
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Effect of 1 IV on the DV, disregarding the effects of all other IV. When the marginal means show differences.
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Self-control example
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Interaction
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When the effects of an IV differ at different levels of another IV (crossing lines). Requires 2 IV's.
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Self-control example
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Within-S's Designs (Repeated Measures)
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All levels of the IV are administered sequentially to all S's. Can include only 2 levels of a IV or can be expanded to include 3 or more levels of a single IV or two or more IV's.
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Single-group time series deisgn
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Type of W/in-S's design. Assess one group sequentially before and after treatment. Threatened by history.
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Factorial Design: |
When astudy involves two or more IVs. This allows an investigator to analyze main andinteraction effects. Presence of interaction effect invalidates the conclusionthat was made on the basis of main effects alone. |
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Mixed Designs
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Combines B/t S's and W/in S's methods.
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Single Subject Designs:
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Includes at least one baselinephase and one treatment phase. Subject acts as his/her own no-treatmentcontrol. DV is measured repeatedly at regular intervals throughout baseline andtreatment phases. di-lang⒰��
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AB Design
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Baseline and Treatment
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Reversal Designs (ABA, ABAB, Etc.)
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withdrawal)Design: More than one baseline and treatment phase. Involves the withdrawal oftreatment during the second and subsequent treatment baseline phases. Provideadditional control over threats to internal validity. ase � Provides more data to support inferences if treatment works twice. Can be unethical.
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Multiple Baseline Design
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Sequentially applying a treatment condition to diff beh in diff settings to see if it changes DV. Really an AB in diff settings. Used when withdrawing treatmet is unethical.
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Descriptive Statistics
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Describe and summarize the date collected on a variable or the relationship between variables.
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Inferential Statistics
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Answer the question: can the data be generalized to the gen. pop.?
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Continuous Variable
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Infinite # of values. Ex: Time
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Discrete Variable
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Finite # of values.
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Nominal Scale
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Divides variables into unordered categories. Ex: Male of Female, Eye color, DSM diagnosis, Religion, Political affiliation. Weakness: Only frequencies can be obtained.
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Ordinal Scale
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Places information into "order." Ex: Ranks and Likert-scales. Weakness: Does not tell how much difference b/t scores.
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Interval Scale
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Order and equal intervals b/t successive data pts. Ex: Standard scores on IQ and Temp. No absolute 0.
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Ratio Scale
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Order, equal intervals, and an absolute 0. 0 is the complete absence of the characteristic. Ex: # of calories, # of correct items on a test, & reaction time in sec.
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Kurtosis
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Height or flatness of a distribution.
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Leptokurtic
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"peaked" distribution.
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Platykurtic
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Flat distribution
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Mesokurtic
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A normal curve
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Skewed distribution
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More than half of the observations fall on one side.
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Positively Skewed
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Most scores are low (negateve end) and the positive tail is extended.
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Negatively Skewed
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Most scores are high and the negative tail is extended.
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Mode
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Most frequent score in a set of data.
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Median
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The score that divides the data.
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Mean
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Average
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Scales of measurement
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Nominal-Mode
Ordinal-Mode or Median Interval-Mode, Median, or Mean Ratio-Mode, Median, or Mean |
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Median is used when...
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the distribution is skewed, b/c the mean is sensitive to all scores (i.e., pull from outliers).
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Normal distribution of a curve (SD)
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68% = 1 SD
95% = 2 SD 99% = 3 SD |
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Range
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Simplest measure of variability which is calculated by sub the lowest score from the highest score in the distribution.
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SD
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Square root of the variance.
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When constants are added or subtracted... (central tendency?)
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the measures of central tendency stay the same.
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When scores are multiplied or divided...(central tendency?
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the measures of central tendency all change.
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Inferential Statistics
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Tells if the obtained sample values can be generalized to the pop w/ confidence.
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Population Paramaters
mu sigma sigma squared |
Sample Statistics
M or X S or SD S^2 or V |
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Sampling Distribution
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Allows a researcher to determine the probability that a sample having a particular mean or other value could have been drawn from a pop with a known parameter.
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Sampling Distribution of the Mean
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Taking several means and finding a normal curve.
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Central Limit Theorem (CLT)
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1. Regardless of the shape of the distribution, as the sample size increases, the sampling distribution approaches a normal distribution.
2. The M of the sampling dist is equal to the pop M. 3. The SD of the sampling dist is = sigma/sq root of N (SEM). |
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The larger the pop SD and the ________ the sample size, the ______ the SEM.
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smaller, larger
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The smaller the pop SD and the ______ the sample size, the ________ the SE M.
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larger, smaller
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Rejection Region
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Region of unlikely values or your H1 was right rather than the null.
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Retention Region
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Region of likely values or your H1 was wrong...keep the Ho.
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Type I Error (experiment-wise error rate)
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False positive. When you reject a true null. Directly related to the size of alpha. As alpha increases, your probability of making a Type I error increases.
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Type II Error
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False negative. When you retain a false null. The probability of making a Type II error is = Beta. When Beta is low, the sample is small, and when the IV is not sufficient, then a Type II error is more likely.
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Statistical Power
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When a statistical test enables an experimenter to reject a false null.
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Ways to Increase Power
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1. Increase Alpha
2. Increase Sample 3. Max IV 4. Min Error 5. One tailed-test 6. Parametric test |
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Parametric Test assumptions
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Are used to evaluate hyp about pop means, variances, or other parameters.
Interval or Ratio scale. Assumptions: 1)Normal dist. & 2)Homoscedasticity (normal variances). Ex: T-test, ANOVA, ANCOVA, MANOVA. |
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Nonparametric Test
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Used to analyze data collected on variables on a nominal or ordinal scale or when the assumptions of a Parametric test are not met.
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Degrees of Freedom
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N-1
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Chi-Square Test (Singel or 1 var & Multiple or 2+ var)
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Used to analyize the frequency of observations in each category of a NOMINAL VARIABLE. Frequency cannot be less than 5 and obs must be independent.
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Single-Sample x^2 Test (Goodess-of-fit)
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1 var (NOM)
df = c-1 |
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Multiple-Sample x^2 Test
(chi-square test for contingency tables) |
2+ var (NOM)
df = (c-1)(r-1) |
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Mann-Whitney U Test
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One IV: 2 Ind groups
One DV: Rank order data (ORD) Stasitc: U ALT: T-test for ind samples |
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Wilcoxon Matched-Pairs Signed-Ranks Test
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One IV: 2 corr groups
One DV: Rank order data (ORD) Stistic: T ALT: T-test for corr samples |
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Kruskal-Wallis Test
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One IV: 2 or more ind groups
One DV: Rank order data (ORD) Stistic: H ALT: one-way ANOVA |
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T-test for a Single Sample
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One IV: Single group
One DV: Int or ratio Statistic: T df = n-1 |
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T-test for Ind Samples
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One IV: 2 Ind groups
One DV: Int or ratio Statistic: T df = n-2 |
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T-test for Correlated Samples
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One IV: 2 corr groups
One DV: Int or ratio Stistic: T df = n-1 |
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ANOVA
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Uses to compare 2 or more means and helps control for Type I error.
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One-Way ANOVA
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One IV: 2 or more ind groups
One DV: Int or Ratio Stastic: F df = (c-1)(n-c), where C=levels in IV and N=# of sub |
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F-ratio
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mean sq between(explained variance/Mean sq within (unexplained variance)
(treatment+error)/error When null is true, MSB & MSW are similar. When null is false, MSB is larger than MSW. |
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Factorial (Two-Way when 2 IV's) ANOVA
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2+ IV's: Indep groups
One DV: Int or ratio Stistic: F |
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Randomized Block Factorial ANOVA
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When "blocking" is used to control extraneous variables. Treats the ext var as a IV which reduces w/in group variability and increases power.
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ANCOVA
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Combines ANOVA with regression and seperates ext var in the DV.
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Repeated Measures ANOVA
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When using W/in subj designs when diff levels of the IV are admin sequentially.
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Mixed (Split-Plot) ANOVA
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For mixed designs and one IV is B/t groups and one IV is W/in groups.
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Trend Analysis
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Type of analysis of variance used to assess linear and nonlinear trends when the IV is quantitative.
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Multivariate ANOVA (MANOVA)
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1+ IV's and 2+ DV's.
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Bivariate Correlation
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Used to summarize the degree of association b/t two variables. Ex: Scatterplot or Correlation coefficient
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Point Biserial Corr
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True dicotomy such as sex (m/f) and Int or Ratio variable.
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Biserial Corr
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Artificial dicotomy such as climate comfort (fav/unfav) and Int or Ratior variable.
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Eta (Used to assess nonlinear relationships)
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Int/Rat and Int/Rat
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Correlation assumptions
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1. Linearity
2. Unrestricted Range 3. Homoscendasticity (range of x scores is similar to the range of y scores) |
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Shared variability
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Squared corr coef which represents degree of association.
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Regression Analysis
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Prediction of x and y.
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Multiple Regression
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Multivariate technique for 2+ continuous or discrete predictors.
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Multicollinearity
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High corr b/t predictors
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Types of multiple regression
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1. Simultaneous (simple)
2. Stepwise (step-up and step-down) |
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Cross Validation
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Trying out a multiple correlation and multiple regression equation on another sample causing the corr coef to "shrink" and decrease the predictive value of the regression equation.
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Canonical Correlation
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Extension of multiple regression that is used when 2+ continous predictors are to be ued to predict status on 2+ continuous criteria.
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Discriminant Function Analysis
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Appropriate technique when 2+ continuous predictors will be used to predict or estimate a person's status on a single discrete (nominal) criterion.
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Logistic Regression
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Same as the discriminant analysis, but assumes a non linear relationship.
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Path analysis
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Extension of multiple regression and translates theory about casual relationships into a path diagram. It only goes in one direction
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LISREL (linear structural relations analysis)
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Used when a casual model includes recursive (one-way) and non-recursive (two-way paths).
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Reliability Coefficient
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In most cases, you would square the correlation coefficient to obtain the answer to this question. However, the reliability coefficient is an exception to this rule: it is never squared. Instead, it is interpreted directly. This means that the value of the reliability coefficient itself indicates the proportion of variance in a test that reflects true variance.
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The coefficient of determination (denoted by R2) |
is a key output of regression analysis. It is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable. |
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