PDF Archive

Easily share your PDF documents with your contacts, on the Web and Social Networks.

Share a file Manage my documents Convert Recover PDF Search Help Contact



Lecture2 PDF.docx .pdf


Original filename: Lecture2_PDF.docx.pdf

This PDF 1.5 document has been generated by / Skia/PDF m62, and has been sent on pdf-archive.com on 05/08/2017 at 09:31, from IP address 183.82.x.x. The current document download page has been viewed 167 times.
File size: 101 KB (1 page).
Privacy: public file




Download original PDF file









Document preview


STRUCTURAL EQUATION MODELING REAL TIME APPLICATION WITH
EXAMPLES
BASIC INTRODUCTION- II

Latent Vs. Manifest/ Observed Variables in SEM analysis
What is Latent & Observed in SEM?
1. SEM consists of two parts
● Measurement model(s) for each latent variable
● Path analysis between the latent and observed variables
2. Variable Labels
● Latent variable
factor
construct
● Observed variable
measured variable manifest variable
indicator
generally considered endogenous
Observed or manifest variables:
Latent variables are not observed directly, it follows that they cannot be
measured directly.
● The unobserved variable is linked to one that is observable, thereby
measurement is possible.
● directly measurable (through questionnaire)
● include measurement error
● The term behaviour is used here in the very broadest sense to include
scores on a particular measuring instrument.
Examples:
1. Self-report to an attitudinal scale.
2. Scores on achievement test.
Upcoming Weeks…..
Lecture -3
Structural Equation Modeling (SEM) using AMOS
1. Exogenous and Endogenous latent variables

© 2017-2018 All Rights Reserved, No part of this document should be modified/used without prior consent
Statswork ™ - www.statswork.com
INDIA: Nungambakkam, Chennai – 600 034
UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX


Document preview Lecture2_PDF.docx.pdf - page 1/1

Related documents


lecture2 pdf docx
lecture 3 pptx
lecture1 pdf
markov model vs shapley value
dissertation statistics services
approaching data analysis


Related keywords