Designing social inquiry : scientific inference in qualitative research / Gary King, Robert O. Keohane, Sidney Verba.
نوع المادة :![نص](/opac-tmpl/lib/famfamfam/BK.png)
- text
- unmediated
- volume
- 0691034710 (pbk) :
- $19.95
- 9780691034713 (pbk)
- H61 K5437 1994
نوع المادة | المكتبة الحالية | رقم الطلب | رقم النسخة | حالة | تاريخ الإستحقاق | الباركود | |
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UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | H61 K5437 1994 (إستعراض الرف(يفتح أدناه)) | C.1 | Library Use Only | داخل المكتبة فقط | 30010000098865 |
Browsing UAE Federation Library | مكتبة اتحاد الإمارات shelves, Shelving location: General Collection | المجموعات العامة إغلاق مستعرض الرف(يخفي مستعرض الرف)
H61 .K49 2002 مشكلة العلوم الإنسانية : تقنينها و إمكانية حلها / | H61 K49 2011 مشكلة العلوم الانسانية : تقنينها و امكانية حلها / | H61 K49 2011 مشكلة العلوم الانسانية : تقنينها و امكانية حلها / | H61 K5437 1994 Designing social inquiry : scientific inference in qualitative research / | H61 K5439 2001 Dialectical social science in the age of complexity / | H61 K5439 2001 Dialectical social science in the age of complexity / | H61 .L345 1997 Modern social theory : key debates and new directions |
Includes bibliographical references (pages [231]-238) and index.
1. The Science in Social Science -- 2. Descriptive Inference -- 3. Causality and Causal Inference -- 4. Determining What to Observe -- 5. Understanding What to Avoid -- 6. Increasing the Number of Observations.
At a moment when acute disagreement among scholars over the appropriateness of qualitative and quantitative research methods threatens to undermine the validity and coherence of the social sciences, Gary King, Robert Keohane, and Sidney Verba have written a timely and far-sighted book that develops a unified approach to valid descriptive and causal inference. They illuminate the logic of good quantitative and good qualitative research designs and demonstrate that the two do not fundamentally differ.
Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?