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Original Essays | September 17, 2014

Merritt Tierce: IMG Has My Husband Read It?



My first novel, Love Me Back, was published on September 16. Writing the book took seven years, and along the way three chapters were published in... Continue »
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    Love Me Back

    Merritt Tierce 9780385538077

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Quantitative Applications in the Social Sciences #78: Data Theory and Dimensional Analysis

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Quantitative Applications in the Social Sciences #78: Data Theory and Dimensional Analysis Cover

 

Synopses & Reviews

Synopsis:

By examining some of the basic scaling questions, such as the importance of measurement levels, the kinds of variables needed for Likert or Guttman scales and when to use multidimensional scaling versus factor analysis, Jacoby introduces readers to the most appropriate scaling strategies for different research situations. He also explores data theory, the study of how real world observations can be transformed into something to be analyzed, in order to facilitate more effective use of scaling techniques.

Synopsis:

For many readers, data theory is probably unfamiliar. Data isn't usually the subject matter of theory in and of itself. However, in this volume, William Jacoby introduces a theory of data idea. It examines how real world observations are transformed into something to be analyzed that is, data. Jacoby explores some of the basic ideas of data theory, and considers their implications for research strategies in the social sciences. "Like others in the series, it is reassuringly slim. It is intended for a general social science readership and is a worthwhile read even for experienced data analysts. since it draws attention not only to often overlooked assumptions, but also to often ignored analysis possibilities." --Telephone Surveys "On the whole, this book contains a lot of useful information." --Journal of Classification

Product Details

ISBN:
9780803941786
Introduction:
Lewis-Beck, Michael S.
Publisher:
Sage Publications (CA)
Introduction by:
Lewis-Beck, Michael S.
Introduction:
Lewis-Beck, Michael S.
Author:
Jacoby, William G.
Location:
Newbury Park, Calif. :
Subject:
General
Subject:
Statistical methods
Subject:
Research
Subject:
Social sciences
Subject:
Social sciences -- Statistical methods.
Subject:
Scaling
Subject:
Probability & Statistics - General
Subject:
Scaling (Social sciences)
Subject:
Sociology-Reference and Methodology
Edition Description:
Includes bibliographical references p. 85-88.
Series:
Quantitative Applications in the Social Sciences
Series Volume:
no. 78
Publication Date:
19910231
Binding:
TRADE PAPER
Language:
English
Illustrations:
Yes
Pages:
96
Dimensions:
8.66x5.36x.22 in. .26 lbs.

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Related Subjects

History and Social Science » Law » General
History and Social Science » Sociology » Reference and Methodology
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Quantitative Applications in the Social Sciences #78: Data Theory and Dimensional Analysis New Trade Paper
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Product details 96 pages Sage Publications - English 9780803941786 Reviews:
"Synopsis" by , By examining some of the basic scaling questions, such as the importance of measurement levels, the kinds of variables needed for Likert or Guttman scales and when to use multidimensional scaling versus factor analysis, Jacoby introduces readers to the most appropriate scaling strategies for different research situations. He also explores data theory, the study of how real world observations can be transformed into something to be analyzed, in order to facilitate more effective use of scaling techniques.
"Synopsis" by , For many readers, data theory is probably unfamiliar. Data isn't usually the subject matter of theory in and of itself. However, in this volume, William Jacoby introduces a theory of data idea. It examines how real world observations are transformed into something to be analyzed that is, data. Jacoby explores some of the basic ideas of data theory, and considers their implications for research strategies in the social sciences. "Like others in the series, it is reassuringly slim. It is intended for a general social science readership and is a worthwhile read even for experienced data analysts. since it draws attention not only to often overlooked assumptions, but also to often ignored analysis possibilities." --Telephone Surveys "On the whole, this book contains a lot of useful information." --Journal of Classification
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