Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.

Many librarians use data from surveys to make decisions about how to spend money or allocate staff, often making use of popular online tools like Survey Monkey. In this era of reduced budgets, low staffing, stiff competition for new resources, and increasingly complex choices, it is especially impo...

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Main Authors: Christy Hightower, Kerry Scott
Format: Article
Language:English
Published: University of Alberta Library 2012-05-01
Series:Issues in Science and Technology Librarianship
Online Access:https://journals.library.ualberta.ca/istl/index.php/istl/article/view/1550
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author Christy Hightower
Kerry Scott
author_facet Christy Hightower
Kerry Scott
author_sort Christy Hightower
collection DOAJ
description Many librarians use data from surveys to make decisions about how to spend money or allocate staff, often making use of popular online tools like Survey Monkey. In this era of reduced budgets, low staffing, stiff competition for new resources, and increasingly complex choices, it is especially important that librarians know how to get strong, statistically reliable direction from the survey data they depend upon. This article focuses on three metrics that are easy to master and will go a long way toward making librarians' survey conclusions more powerful and more meaningful: margin of error (MoE), confidence Level (CL), and cross-tabulation table analysis. No complex mathematics or expensive software is required: two simple and free online calculators are introduced that will do the math for you. This article puts the power of improved survey analysis within reach of every librarian and includes eight recommended best practices. [ABSTRACT FROM AUTHOR]
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spelling doaj-art-e96421c4fea54bc5bf91a78f0d9b8d0d2025-07-25T20:28:23ZengUniversity of Alberta LibraryIssues in Science and Technology Librarianship1092-12062012-05-016910.29173/istl1550Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.Christy HightowerKerry Scott Many librarians use data from surveys to make decisions about how to spend money or allocate staff, often making use of popular online tools like Survey Monkey. In this era of reduced budgets, low staffing, stiff competition for new resources, and increasingly complex choices, it is especially important that librarians know how to get strong, statistically reliable direction from the survey data they depend upon. This article focuses on three metrics that are easy to master and will go a long way toward making librarians' survey conclusions more powerful and more meaningful: margin of error (MoE), confidence Level (CL), and cross-tabulation table analysis. No complex mathematics or expensive software is required: two simple and free online calculators are introduced that will do the math for you. This article puts the power of improved survey analysis within reach of every librarian and includes eight recommended best practices. [ABSTRACT FROM AUTHOR] https://journals.library.ualberta.ca/istl/index.php/istl/article/view/1550
spellingShingle Christy Hightower
Kerry Scott
Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
Issues in Science and Technology Librarianship
title Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
title_full Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
title_fullStr Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
title_full_unstemmed Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
title_short Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests.
title_sort infer more describe less more powerful survey conclusions through easy inferential tests
url https://journals.library.ualberta.ca/istl/index.php/istl/article/view/1550
work_keys_str_mv AT christyhightower infermoredescribelessmorepowerfulsurveyconclusionsthrougheasyinferentialtests
AT kerryscott infermoredescribelessmorepowerfulsurveyconclusionsthrougheasyinferentialtests