Margins of Error: A Study of Reliability in Survey Measurement

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John Wiley & Sons, 2007. gada 9. jūl. - 416 lappuses
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Enhance the quality of survey results by recognizing and reducing measurement errors.

Margins of Error: A Study of Reliability in Survey Measurement demonstrates how and hwy identifying the presence and extent of measurement errors in survey data is essential for improving the overall collection and analysis of the data. The author outlines the consequences of ignoring survey measurement errors and also discusses ways to detect and estimate the impact of these errors. This book also provides recommendations of improving the quality of survey data.

Logically organized and clearly written, this book:

  • Deconstructs the data gathering process into six main elements of the response process: question adequacy, comprehension, accessibility, retrieval, motivation, and communication
  • Provides an exhaustive review of valuable reliability estimation techniques that can be applied to survey data
  • Identifies the types of questions and interviewer practices that are essential to the collection of reliable data
  • Addresses hypotheses regarding which survey questions, sources of information, and questionnaire formats produce the most reliable data

In conjunction with research data gathered on nearly 500 survey measures and the application of an empirical approach grounded in classical measurement theory, this book discusses the sources of measurement error and provides the tools necessary for improving survey data collection methods.

Margins of Error enables statisticians and researchers in the fields of public opinion and survey research to design studies that can detect, estimate, and reduce measurement errors that may have previously gone undetected. This book also serves as a supplemental textbook for both undergraduate and graduate survey methodology courses.

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1.–5. rezultāts no 59.

vii. lappuse
Reliability Theory for Survey Measures 35 3.1 Key Notation 36 3.2 Basic Concepts of Classical Reliability Theory 36 3.3 Nonrandom Measurement Error 41 3.4 The Common-Factor Model Representation of CTST 42 3.5 Scaling of Variables 43 3.6 ...
1. lappuse
For example, concepts like religiosity, depression, intelligence, social status, attitudes, psychological Well being, functional status, and personality may be difficult to measure precisely because they largely reflect unobserved ...
6. lappuse
... empirical data are made with respect to more abstract concepts and theories, not simply observable variables. Measurement, thus, requires the clear specification of relations between theoretic constructs and observable indicators.
7. lappuse
It is important in this context not to confuse the indicators of concepts with the theoretical constructs they are thought to reflect. To do so loses sight of the purpose of measurement. In principle, it is the theoretical constructs ...
8. lappuse
On a practical level, once concepts and indicators are defined and agreed on, measurement is possible only if we can assume some type of equivalence across units of observation, e.g., respondents or households. As Abraham Kaplan (1964) ...

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Margins of Error: A Study of Reliability in Survey Measurement
Duane F. Alwin
Priekšskatījums nav pieejams - 2007
Margins of Error: A Study of Reliability in Survey Measurement
Duane F. Alwin
Priekšskatījums nav pieejams - 2007

Bieži izmantoti vārdi un frāzes

Alwin analysis approach assess assumption attitudes bipolar Chapter coefficients components concept covariance covariance matrix cross-sectional CTST model disagrees discussion estimated reliability estimates of reliability example extent F-ratio fact factors factual feeling thermometer rating given Heise interview involves issue Joreskog Krosnick latent class model latent variables levels of measurement levels of reliability Linear Regression matrix measurement error measurement reliability methods MTMM multiple indicators multiple measures National Election Study null hypothesis number of response Number Question Content observed variables p-value panel study parameters path diagram polychoric population present Question Content Note question length questionnaire random error random measurement reinterview reliabil reliability estimates reliability of measurement Reliabilitya reports respondent agrees respondent thinks respondent would place Respondent’s feeling thermometer response categories SAFCH SAFMO sample scales schooling self-reports specific variance statistical survey data survey measurement survey questions survey research Table tion trait true score types unipolar validity versus

Par autoru (2007)

Duane F. Alwin, PHD, is McCourtney Professor of Sociology and Demography and Director of the Center on Population Health and Aging at Pennsylvania State University. In addition to a concentration on survey methodology, his research interests include a wide range of phenomena concerned with the connection of human development and social change, as well as the impact of demographic and historical processes on individual cognitive, ideological, and attitudinal development. Dr. Alwin has published extensively in social science literature and is the recipient of more than two dozen prestigious awards, grants, and special university honors.

Bibliogrāfiskā informācija

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NosaukumsMargins of Error: A Study of Reliability in Survey Measurement
Wiley Series in Survey Methodology
AutorsDuane F. Alwin
Izdevums:ilustrēts
IzdevējsJohn Wiley & Sons, 2007
ISBN0470146303, 9780470146309
Apjoms416 lappuses
  
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