Presentation is loading. Please wait.

Presentation is loading. Please wait.

Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation."— Presentation transcript:

1 Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation comparing a correlation across populations comparing correlated correlations Statistical control questions partial correlation multiple partial correlation semi-partial (part) correlation multiple semi-partial correlation Multiple regression questions multiple correlation comparing nested multiple regression models comparing non-nested multiple regression models comparing multiple regression models across populations comparing multiple regression models across criterion variables

2 Picking the right statistical model… Started out simple … What kinds of variables ??? 2 quant  correlation 2 qual  X 2 1 @  ANOVA Got more interesting with larger ANOVA designs… What kinds of variables ? What kind of design – How many IVs? BG, MG or WG ? What kind of RH: -- main, simple or interaction effect? Now.. Different models, different “keys,” but same importance: the analysis must give the most direct test of RH: possible… if you pick the wrong model the analyses are worthless!!!

3 Survey of Major Correlational Models/Questions simple correlation questions obtaining and comparing bivariate correlations statistical control questions what “would be” the bivariate correlation is all participants had the same score on some “control variable”? multiple correlation questions obtaining and comparing models with multiple predictors

4 Simple Correlation questions (old friends) r y,x1 simple correlation of y and x1 r y,x1 vs. r y,x2 comparing “correlated” correlations within a population/group (uses Hotelling’s t-test or Steiger’s Z-test) r y,x1 vs. r y,x1 comparing the same bivariate correlation in 2 populations/grps (uses Fisher’s Z-test) Examples… Is there a relationship between # therapy sessions and symptomatic improvement? Is # therapy sessions a better predictor of symptomatic improvement than initial level of depression? Is # therapy sessions a better predictor of symptomatic improvement for adults than for adolescents? r imp,#ses r imp,#ses vs. r imp,init r imp,#ses for adults vs r imp,#ses for adolescents.

5 Does amount of practice predict performance better for novices that for experiences individuals? Does amount of practice predict level of performance? Does amount of practice predict performance better then prior experience? Same r, dif populations r perf,pract for novices vs. r perf,pract for experienced Use Fisher’s Z-test Simple r -- r perf,pract Compare r’s in the same pop r perf,pract vs. r perf,exp Hotelling’s t-test & Steiger’s Z-test Which type is each of the following? Use the notation & tell test used for each type of comparison

6 Statistical control questions are always about the “causal relationships” that “produce” or “modify” bivariate correlations - called “3rd variable problems”. Here’s a well-known example... Ice cream sales Violent crimes When plotted by week or by month -- there is a +r between ice cream sales & amount of violent crime. Huh? Does eating ice cream make you violent ? Does being violent make you crave ice cream ? Is there some “3 rd variable” variable that is “producing” the bivariate correlation? What might it be ??? Violent crimes Ice cream sales Here’s the same scatterplot, but with the size of each data point representing the average temperature of that period. We can see that there is no relationship between violent crimes and ice cream sales after controlling both for temperature. Temperature might be that “3 rd variable”.

7 We found a -r between # therapy sessions and amount of symptomatic improvement! Huh?!? Let’s think through this... The sample is heterogeneous with respect to initial level of depression Initial level of depression is likely to be related to # sessions they attend Initial level of depression is likely to be related to symptomatic improvement So, is the relationship each of these variables has with initial level of depression “producing” the bivariate correlation we found? # sessions symptomatic improvement # sessions dotsize indicates initial depression… Those who are more depressed come to more sessions and show less improvement. Those who are less depressed come to fewer sessions and show more improvement. Another example...

8 Statistical Control questions partial correlation questions -- is the relationship BOTH variables have with some 3rd variable(s) “producing” the bivariate relationship between them? r y,x1.x2 partial correlation of y & x1 controlling both for x2 (control var listed to right of “.” ) r y,x1.x2,x3 multiple partial correlation of y & x1 controlling both for x2 and x3 semi-partial (part) questions -- is the relationship JUST ONE of the variables have with some 3rd variable(s) “producing” the bivariate relationship between them? r y,(x1.x2) semi-partial correlation of y & x1, controlling the latter for x2 “( )” around variable being controlled and control variable r y,(x1.x2,x3) multiple semi-partial correlation of y & x1, controlling latterfor x2 & x3 “( )” around variable being controlled and control variables

9 Examples … Are # sessions and symptomatic improvement correlated after controlling symptomatic improvement for initial depression. Are # sessions and symptomatic improvement correlated after controlling both for initial depression? Are # sessions and symptomatic improvement correlated after controlling both for initial depression and age? Are # sessions and symptomatic improvement correlated after controlling symptomatic improvement for initial depression and age. Partial correlation r #ses,imp.init Multiple partial r #ses,imp.init,age Semi-partial r #ses (imp.init) Multiple semi-partial r #ses,(imp.init,age)

10 Does practice predict performance after controlling both for prior experience? Does practice predict performance after controlling performance for prior experience and age? Does practice predict performance after controlling performance for age? Does practice predict performance after controlling both for prior experience and age? partial r perf,prac.exp multiple semi-partial r prac(perf.exp,age) semi-partial r prac(perf.age) multiple partial r perf,prac.exp,age Which type is each of the following?

11 Multiple Regression questions R y.x1,x2,x3,x4 ² multiple correlation with y as the criterion and x1, x2, x3 and x4 as predictors predictors to right of “.” R y.x1,x2,x3,x4 ² vs. R y.x1,x2, ² comparing nested models (uses R 2 change F-test) R y.x1,x2 ² vs. R y.x3,x4 ² comparing non-nested models (uses Hotelling’s t-test or Steiger’s Z-test) R y.x1,x2,x3,x4 ² vs. R y.x1,x2,x3,x4 ² comparing the same multiple regression model in two different populations (uses Fisher’s Z-test & H’s t or Steiger’s Z-test) R y.x1,x2,x3,x4 ² vs. R z.x1,x2,x3,x4 ² comparing the same multiple regression model with two different criterion, in the same population ( Hotelling’s t-test & Steiger’s Z-test )

12 Examples… Symptomatic improvement is predicted from a combination of # sessions, initial depression and age. Symptomatic improvement is predicted from a combination of # sessions, initial depression and age and prediction is improved by adding # of prior therapists. Symptomatic improvement is predicted better from a combination of # sessions, initial depression and age than from # sessions & # of prior therapists. Symptomatic improvement is predicted from a combination of # sessions, initial depression and age better for adults than for adolescents. A combination of # sessions, initial depression and age predicts symptomatic improvement better than it predicts treatment satisfaction. R imp.#ses,init,age 2 vs. R imp.#ses,init,age,#ther 2 R imp.#ses,init,age 2 R imp.#ses,init,age 2 vs. R imp.#ses,#ther 2 R imp.#ses,init,age 2 for adults vs. for adolescents R imp.#ses,init,age 2 vs. R tsat.#ses,init,age 2

13 Do practice, prior skill and motivation predict performance? Do practice, prior skill and motivation predict performance on a speeded task as well as they they predict performance on an accuracy task? Do practice, prior skill and motivation predict performance as well as do prior skill and motivation? Do practice, prior skill and motivation predict performance as well as do practice, motivation and age? Do practice, prior skill and motivation predict performance as well for amateurs as for professionals? single model R perf.prac,skill 2 single model for 2 criterion H & S R speed.prac,skill 2 vs. R acc.prac,skill 2 nested model comparisons R 2 -  F-test R perf.prac,skill,mot 2 vs. R perf.prior,mot 2 non-nested models H & S R perf.prac,skill,mot 2 vs. R perf.prac,mot,age 2 R 2 for 2 populations F’s Z-test - S R perf.prac,skill,mot 2 Which type is each of the following? Use the notation & tell the test used for each model comparison

Download ppt "Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation."

Similar presentations

Inferential Statistics and t - tests

Inferential Statistics and t - tests
Transformations & Data Cleaning

Transformations & Data Cleaning
Canonical Correlation simple correlation -- y 1 = x 1 multiple correlation -- y 1 = x 1 x 2 x 3 canonical correlation -- y 1 y 2 y 3 = x 1 x 2 x 3 The.

Canonical Correlation simple correlation -- y 1 = x 1 multiple correlation -- y 1 = x 1 x 2 x 3 canonical correlation -- y 1 y 2 y 3 = x 1 x 2 x 3 The.
Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation.

Survey of Major Correlational Models/Questions Still the first & most important is picking the correct model… Simple correlation questions simple correlation.
Research Hypotheses and Multiple Regression: 2 Comparing model performance across populations Comparing model performance across criteria.

Research Hypotheses and Multiple Regression: 2 Comparing model performance across populations Comparing model performance across criteria.
Survey of Major Correlational Models/Questions simple correlation questions obtaining and comparing bivariate correlations statistical control questions.

Survey of Major Correlational Models/Questions simple correlation questions obtaining and comparing bivariate correlations statistical control questions.
ANCOVA Workings of ANOVA & ANCOVA ANCOVA, Semi-Partial correlations, statistical control Using model plotting to think about ANCOVA & Statistical control.

ANCOVA Workings of ANOVA & ANCOVA ANCOVA, Semi-Partial correlations, statistical control Using model plotting to think about ANCOVA & Statistical control.
Kxk BG Factorial Designs expanding the 2x2 design reasons for larger designs statistical analysis of kxk BG factorial designs using LSD for kxk factorial.

Kxk BG Factorial Designs expanding the 2x2 design reasons for larger designs statistical analysis of kxk BG factorial designs using LSD for kxk factorial.
LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.

LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.

LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.
Research Hypotheses and Multiple Regression Kinds of multiple regression questions Ways of forming reduced models Comparing “nested” models Comparing “non-nested”

Research Hypotheses and Multiple Regression Kinds of multiple regression questions Ways of forming reduced models Comparing “nested” models Comparing “non-nested”
Statistical Control Regression & research designs Statistical Control is about –underlying causation –under-specification –measurement validity –“Correcting”

Statistical Control Regression & research designs Statistical Control is about –underlying causation –under-specification –measurement validity –“Correcting”
Regression Models w/ k-group & Quant Variables Sources of data for this model Variations of this model Main effects version of the model –Interpreting.

Regression Models w/ k-group & Quant Variables Sources of data for this model Variations of this model Main effects version of the model –Interpreting.
ANCOVA Workings of ANOVA & ANCOVA ANCOVA, Semi-Partial correlations, statistical control Using model plotting to think about ANCOVA & Statistical control.

ANCOVA Workings of ANOVA & ANCOVA ANCOVA, Semi-Partial correlations, statistical control Using model plotting to think about ANCOVA & Statistical control.
Multiple Regression Models Advantages of multiple regression Important preliminary analyses Parts of a multiple regression model & interpretation Differences.

Multiple Regression Models Advantages of multiple regression Important preliminary analyses Parts of a multiple regression model & interpretation Differences.
Multiple Regression Models

Multiple Regression Models
Introduction the General Linear Model (GLM) l what “model,” “linear” & “general” mean l bivariate, univariate & multivariate GLModels l kinds of variables.

Introduction the General Linear Model (GLM) l what “model,” “linear” & “general” mean l bivariate, univariate & multivariate GLModels l kinds of variables.
Simple Regression correlation vs. prediction research prediction and relationship strength interpreting regression formulas –quantitative vs. binary predictor.

Simple Regression correlation vs. prediction research prediction and relationship strength interpreting regression formulas –quantitative vs. binary predictor.
Jeopardy! 100 200 300 400 500 100 200 300 400 500 100 200 300 400 500 100 200 300 400 500 100 200 300 400 500 One-Way ANOVA Correlation & Regression Plots.

Jeopardy! One-Way ANOVA Correlation & Regression Plots.
Introduction to Path Analysis & Mediation Analyses Review of Multivariate research & an Additional model Structure of Regression Models & Path Models Direct.

Introduction to Path Analysis & Mediation Analyses Review of Multivariate research & an Additional model Structure of Regression Models & Path Models Direct.

Similar presentations

Ads by Google