Upload File

soc 681 – causal models with directly observed variables

  • Home
  • Documents
  • SOC 681 – Causal Models with Directly Observed Variables
29
SOC 681 – Causal Models with Directly Observed Variables James G. Anderson, Ph.D. Purdue University

Upload: salome

Post on 22-Jan-2016

35 views

Category:

Documents


0 download

Report

Tags:

DESCRIPTION

SOC 681 – Causal Models with Directly Observed Variables. James G. Anderson, Ph.D. Purdue University. Types of SEMs. Regression Models Path Models Recursive Nonrecursive. Class Exercise: Example 7 SEMs with Directly Observed Variables. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: SOC 681 – Causal Models with Directly Observed Variables

SOC 681 – Causal Models with Directly Observed Variables

James G. Anderson, Ph.D.Purdue University

Page 2: SOC 681 – Causal Models with Directly Observed Variables

Types of SEMs

Regression Models Path Models

Recursive Nonrecursive

Page 3: SOC 681 – Causal Models with Directly Observed Variables

Class Exercise: Example 7SEMs with Directly Observed Variables

Felson and Bohrnstedt’s study of 209 girls from 6th through 8th grade

Variables Academic: Perceived academic ability Attract: Perceived attractiveness GPA: Grade point average Height: Deviation of height from the mean

height Weight: Weight adjusted for height Rating: Rating of physical attractiveness

Page 4: SOC 681 – Causal Models with Directly Observed Variables

GPA

HEIGHT

WEIGHT

RATING

ACADEMIC

ATTRACT

e1

e2

1

1

Page 5: SOC 681 – Causal Models with Directly Observed Variables

GPA

HEIGHT

WEIGHT

RATING

ACADEMIC

ATTRACT

e1

e2

1

1

Page 6: SOC 681 – Causal Models with Directly Observed Variables

Assumptions

Relations among variables in the model are linear, additive and causal.

Curvilinear, multiplicative and interaction relations are excluded.

Variables not included in the model but subsumed under the residuals are assumed to be not correlated with the model variables.

Page 7: SOC 681 – Causal Models with Directly Observed Variables

Assumptions

Variables are measured on an interval scale.

Variables are measured without error.

Page 8: SOC 681 – Causal Models with Directly Observed Variables

Objectives

Estimate the effect parameters (i.e., path coefficients). These parameters indicate the direct effects of a variable hypothesized as a cause of a variable taken as an effect.

Decompose the correlations between an exogenous and endogenous or two endogenous variables into direct and indirect effects.

Determine the goodness of fit of the model to the data (i.e., how well the model reproduces the observed covariances/correlations among the observed variable).

Page 9: SOC 681 – Causal Models with Directly Observed Variables

AMOS Input

ASCII SPSS Microsoft Excel Microsoft Access Microsoft FoxPro dBase Lotus

Page 10: SOC 681 – Causal Models with Directly Observed Variables

AMOS Output

Path diagram Structural equations effect

coefficients, standard errors, t-scores, R2 values

Goodness of fit statistics Direct and Indirect Effects Modification Indices.

Page 11: SOC 681 – Causal Models with Directly Observed Variables

Model One

Page 12: SOC 681 – Causal Models with Directly Observed Variables

Decomposing the Effects of Variables on Achievement

Variables Direct Indirect Total

Sex -.03 - -.03

FatherEd .17 - .17

Ethnic .17 - .17

IndTrng .23* - .23*

AStress -.17* - -.17*

ActMast .02 - .02

SelfCon .42* - .42*

Page 13: SOC 681 – Causal Models with Directly Observed Variables

Model Two

Page 14: SOC 681 – Causal Models with Directly Observed Variables

Goodness of Fit: Model 2

Chi-Square = 29.07df = 15

p < 0.06 Chi-Square/df = 1.8 RMSEA = 0.086 GFI = 0.94 AGFI = 0.85 AIC = 67.82

Page 15: SOC 681 – Causal Models with Directly Observed Variables

Chi Square: 2

Best for models with N=75 to N=100 For N>100, chi square is almost always

significant since the magnitude is affected by the sample size

Chi square is also affected by the size of correlations in the model: the larger the correlations, the poorer the fit

Page 16: SOC 681 – Causal Models with Directly Observed Variables

Chi Square to df Ratio: 2/df

There are no consistent standards for what is considered an acceptable model

Some authors suggest a ratio of 2 to 1 In general, a lower chi square to df ratio

indicates a better fitting model

Page 17: SOC 681 – Causal Models with Directly Observed Variables

Root Mean Square Error of Approximation (RMSEA)

Value: [ (2/df-1)/(N-1) ] If 2 < df for the model, RMSEA is set to

0 Good models have values of < .05;

values of > .10 indicate a poor fit.

Page 18: SOC 681 – Causal Models with Directly Observed Variables

GFI and AGFI (LISREL measures)

Values close to .90 reflect a good fit. These indices are affected by sample

size and can be large for poorly specified models.

These are usually not the best measures to use.

Page 19: SOC 681 – Causal Models with Directly Observed Variables

Akaike Information Criterion (AIC)

Value: 2 + k(k-1) - 2(df)

where k= number of variables in the model A better fit is indicated when AIC is smaller Not standardized and not interpreted for a

given model. For two models estimated from the same

data, the model with the smaller AIC is preferred.

Page 20: SOC 681 – Causal Models with Directly Observed Variables

Model Building

Standardized ResidualsACH – Ethnic = 3.93

Modification IndexACH – Ethnic = 10.05

Page 21: SOC 681 – Causal Models with Directly Observed Variables

Model Three

Page 22: SOC 681 – Causal Models with Directly Observed Variables

Goodness of Fit: Model 3

Chi-Square = 16.51df = 14

p < 0.32 Chi-Square/df = 1.08 RMSEA = 0.037 GFI = 0.96 AGFI = 0.90 AIC = 59.87

Page 23: SOC 681 – Causal Models with Directly Observed Variables

Comparing Models

Chi-Square Difference = 12.56df Difference = 1

p < .0005 AIC Difference = 7.95

Page 24: SOC 681 – Causal Models with Directly Observed Variables

Difference in Chi Square

Value: X2diff = X2 model 1 -X2

model 2

DFdiff = DF model 1 –DFmodel 2

Page 25: SOC 681 – Causal Models with Directly Observed Variables

Decomposing the Effects of Variables on Achievement

Variables Direct Indirect Total

Sex - .09 .09

FatherEd .- .06 .06

Ethnic .29 .05 .34

IndTrng .25 .04 .29

AStress -.14 -.03 -.17

ActMast - .13 .13

SelfCon .44 - .44

Page 26: SOC 681 – Causal Models with Directly Observed Variables

Class Exercise: Example 7SEMs with Directly Observed Variables

Attach the data for female subjects from the Felson and Bohrnstedt study (SPSS file Fels_fem.sav)

Fit the non-recursive model Delete the non-significant path

between Attract and Academic and refit the model

Compare the chi square values and the AIC values for the two models

Page 27: SOC 681 – Causal Models with Directly Observed Variables

Class Exercise: Example 7SEMs with Directly Observed Variables

Felson and Bohrnstedt’s study of 209 girls from 6th through 8th grade

Variables Academic: Perceived academic ability Attract: Perceived attractiveness GPA: Grade point average Height: Deviation of height from the mean

height Weight: Weight adjusted for height Rating: Rating of physical attractiveness

Page 28: SOC 681 – Causal Models with Directly Observed Variables

GPA

HEIGHT

WEIGHT

RATING

ACADEMIC

ATTRACT

e1

e2

1

1

Page 29: SOC 681 – Causal Models with Directly Observed Variables

GPA

HEIGHT

WEIGHT

RATING

ACADEMIC

ATTRACT

e1

e2

1

1


The impact of causal beliefs on causal beliefsFinal

Trump Voters and Criminal Justice Reform Table 1-1 ......CLIN- TON/ SOME- SOC SOC SOC SOC SOC SOC SOC SOC SOC SUB TOTAL TRUMP TON OTHER TRUMP OTHER YES WHAT CONS MOD LIB CONS MOD LIB

Express 681

FLASHDOC n 681

0003609618 681..713 - link.springer.com

Machine Learning CSE 681

santantimo.etrasparenza.it · 19) soc. 20) soc. 21) soc. 22) soc. 23) soc. 24) soc. 25) soc. 26) soc. 27) soc. 28) soc. 29) soc. 30) soc. GI.TA.SI. SRL; STP SRL, DE CHIARA COSTRUZIONI

Ev 681 Programme

 · 0016/soc/soc/11-12 krushna chandra sahoo 16 0005/soc/soc/11-12 lipsa nanda 17 0007/soc/soc/11-12 truptimayee behera 18 0045/soc ... 0064/soc/soc/11-12 dayanidhi deep 27 sc 0009/soc/soc/11-12

AE-681 Composite Materials

Maf 681 blog pptx

7:(17< (,*+7+681'$

Bayesian Generic Priors for Causal Learningcvl.psych.ucla.edu/papers/GenericPrior.psychreview08.pdf · judgments of both causal strength and causal structure (whether a causal link

Ink Yield - Copier Catalogbrochure.copiercatalog.com/canon_au/TS9560brochure.pdfInk Yield A4 document: 1451 4”x6” photo: 231 ... Paper Feeding Memory Card 681 XXL 681 681 681 XXL

Gooya issue 681

ITU-T G.681

ROOM ESSENCE No 681 No.681 RG-15BK 207046 BROWN RG …€¦ · room essence no 681 no.681 rg-15bk 207046 brown rg-15br 207053 f no,4985155 ¥20,000 (*hfl]) w160xd220cm

Generalizability in Causal Inference - Carlos Cinelli and Bareinboim...Cinelli, Bareinboim, SoCal 2019 - Generalizability in Causal Inference Causal inference: causal models Inputs:

DOCUMENT RESUME ED 137 872 · DOCUMENT RESUME ED 137 872 CS 501 681 AUTHOR Ki, Young Y. 'TITLE A Causal Model of Communication Patterns of Foreign. Immigrants in the Process of Acculturation

Causal Pluralism and the Limits of Causal Analysis:public.econ.duke.edu/~kdh9/Source Materials/Research/Cartwright... · Causal Pluralism and the Limits of Causal Analysis: *

Causal Laws I and Causal Instances

681 mid term

Nepali times #681

Resol.final 681

681 French Verbs

Feral Tribune 681

Causal effects 0.6em A gentle(?) introduction · 2017. 11. 2. · Proc. of the Royal Soc. of Medicine. Section for Biostatistics Causal e ects October 31, 2017 4 / 56. The randomized

Causal Diagrams and the Identification of Causal Effects

TRIM AND CORD - Trivantage...503084 681 Ivory ABA9506 100 yd. 13/16 in. 502982 681 Ivy ABA4632 100 yd. 13/16 in. 503051 681 Jockey Red ABA03 100 yd. 13/16 in. 503073 681 Linen ABA33

CMSC 681 Project

One Piece capitolul 681

681 samurai john barry

Sementes Soc Soc

MC-681-0D sideA

Tutorial Causal Model Search - Homepage - CMU Causal Model Search ... Representing/Modeling Causal Systems 1) Causal Graphs 2) ... Load charity.txt (tabular