DOCSLIB.ORG

Sociology Name of Module: Processing and Analyzing Quantitative Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sociology Name of Module: Processing and Analyzing Quantitative Data 1 Module Detail and its Structure Subject Name Sociology Paper Name Methodology of Research in Sociology Module Name/Title Processing and Analyzing Quantitative Data Module Id RMS 20 Pre-requisites Some knowledge on social statistics Objectives This module will deal with the issues involved in the process of handling, managing and interpreting quantitative data collected in the process of research. It will also discuss about the basic statistical tools with the help of which we analyse social phenomena. Keywords Coding, editing, statistics, quantitative research, measures of central tendency, dispersion, coefficient correlation and regression. Role in Content Name Affiliation Development Principal Investigator Prof. Sujata Patel Dept. of Sociology, University of Hyderabad Paper Co-ordinator Prof. Biswajit Ghosh Professor, Department of Sociology, The University of Burdwan, Burdwan 713104 Email: [email protected] Ph. M +91 9002769014 Content Writer Dr. Udita Mitra Assistant Professor, Department of Sociology, Shri Shikshayatan College, Kolkata-700095 Email: [email protected] Ph. M +91 9433213816 Ph. L (O) 033-24140594 Content Reviewer (CR) Prof. Biswajit Ghosh Professor, Department of Sociology, The & Language Editor University of Burdwan, Burdwan 713104 Name of Paper: Methodology of Research in Sociology Sociology Name of Module: Processing and Analyzing Quantitative Data 2 Contents 1. Objective .................................................................................................................................... 3 2. Introduction…………………………………………………………………………………….3 3. Learning Outcome .................................................................... Error! Bookmark not defined. 4. Data Processing ........................................................................ Error! Bookmark not defined. 4.1 Editing ........................................................................................................................................ 3 4.2 Coding……………………………………………………………………………………….....3 4.3 Classification .............................................................................................................................. 4 4.4. Tabulation..................................................................................................................................4 Self-check exercise – 1..............................................................................................................4 5. Data Analysis…………………………………………………………………………………..5 6. Statistics in Social Research…………………………………………………………………...5 Self-check exercise – 2..............................................................................................................6 6.1 Measures of Central tendency .................................................................................................... 6 6.2 Measures of Dispersion ............................................................................................................ ..9 6.3 Chi-Square Test ........................................................................................................................ 13 6.4 T-test.......................................................................................................................................15 6.5 Measures of Relationship…………………………………………………………………….18 Self-check Exercise - 3…………………………………………………………………….....22 7. Limitations of Statistics in Sociology…………………………………………………………23 8. Summary..................................................................................................................................23 9. References ................................................................................................................................ 25 Name of Paper: Methodology of Research in Sociology Sociology Name of Module: Processing and Analyzing Quantitative Data 3 1. Objective This module will deal with the issues involved in the process of handling, managing and interpreting quantitative data collected in the process of research. It will also discuss about the basic statistical tools with the help of which we analyse social phenomena. 2. Introduction Quantitative research can be construed as a research strategy that emphasizes quantification in the collection and analysis of data. It entails a deductive approach to the relationship between theory and research in which the accent is placed on testing the theories. Quantitative research usually incorporates the practices and norms of the natural scientific model and of positivism in particular and it also embodies a view of social reality as an external, objective reality (Bryman 2004: 19). It also has a preoccupation with measurement and involves a process of collecting large amount of data. These data may be collected through various ways like survey and field research. The data, after collection, have to be processed in order to ensure their proper analysis and interpretation. According to Kothari (2004), technically, processing implies editing, coding, classification and tabulation of collected data so that they are amenable to analysis. These endeavours help us to search for patterns of relationship that exist among data-groups (Ibid.: 122). 3. Learning Outcome This module will help you to understand different issues involved in processing and analysing quantitative data. It will also help you to grasp the essential steps of applying various statistical measures in order to interpret data collected through social research. 4. Data Processing Data reduction or processing mainly involves various steps necessary for preparing the data for analysis. These steps involve editing, categorising the open-ended questions, coding, computerization and preparation of tables (Ahuja 2007: 304). The processing of data is an essential step before analysis because it enables us to overcome the errors at the stage of data collection. 4.1. Editing According to Majumdar (2005), error can come in at any stage of social research especially in the stage of data collection. These errors have to be kept at a minimum level to avoid errors in the results of the research. Editing or checking for errors in the completed questionnaires is a laborious exercise and needs to be done meticulously. Interviewers tend to commit mistakes like some questions are missed out; some answers remain unrecorded or are recorded at the wrong places. The questionnaires therefore need to be checked for completeness, accuracy and uniformity (Ibid.: 310). 4.2. Coding Coding implicates the process of assigning numbers or other symbols to answers so that they can be categorized into specific classes. Such classes should be appropriate to the research problem under consideration (Kothari 2004: 123). Careful consideration should be made so as not to leave out any response uncoded. According to Majumdar (2005: 313), a set of categories is referred to as “coding frame” or “code book”. Code book explains how to assign numerical codes for response categories Name of Paper: Methodology of Research in Sociology Sociology Name of Module: Processing and Analyzing Quantitative Data 4 received in the questionnaire/schedule. It also indicates the location of a variable on computer cards. Ahuja (2007: 306) provides an example to illustrate how variables can be coded. In a question regarding the religion of the respondent the answer categories of Hindu, Muslim, Sikh, and Christian can be coded as 1, 2, 3, and 4 respectively. In such cases, the counting of frequencies will not be according to Hindus, Muslims etc., but as 1, 2 and so on. Coding can be done manually or with the help of computers. 4.3. Classification Besides editing and coding of data, classification is another important method to process data. Classification has been defined as the process of arranging data into groups and classes on the basis of some common characteristics (Kothari 2004: 123). Classification can be of two types, namely Classification according to attributes or common characteristics like gender, literacy etc., and Classification according to class intervals whereby the entire range of data is divided into a number of classes or class intervals. 4.4. Tabulation Tabulation is the process of summarising raw data and displaying the same in compact form for further analysis (Kothari 2004: 127). The necessity of tabulating raw data is: It conserves space and reduces explanatory and descriptive statement to a minimum, and It provides a basis for various statistical computations. Tabulation can be done manually as well as with electronic and mechanical devices like computers. When the data are not large in number, tabulation can be done by hand with the help of tally marks. Self check exercise – 1 Question 1. Tabulate the following examination grades for 80 students. 72, 49, 81, 52, 31,38,81, 58,68, 73, 43, 56, 45, 54, 40, 81, 60, 52, 52, 38, 79, 83, 63, 58, 59, 71, 89, 73, 77, 60, 65, 60, 69, 88, 75, 59, 52, 75, 70, 93, 90, 62, 91, 61, 53, 83, 32, 49, 39, 57, 39, 28, 67, 74, 61, 42, 39, 76, 68, 65, 58, 49, 72, 29, 70, 56, 48, 60, 36, 79, 72, 65, 40, 49, 37, 63, 72, 58, 62, 46 (Levin and Fox 2006). Procedures for Tabulation/Grouping of Data The above is an array of scores which otherwise would not be very handy to use. In order to make the data meaningful and useful it must be organized and classified into frequency tables. There are certain easy steps to be followed in order to convert the raw scores into frequency tables. i. We must first find the difference between the highest and the lowest score
Load more

Recommended publications
  • Do Ties Really Bind? the Effect of Knowledge and Commercialization Networks on Opposition to Standards
    ௠ Academy of Management Journal 2014, Vol. 57, No. 2, 515–540. http://dx.doi.org/10.5465/amj.2011.1064 DO TIES REALLY BIND? THE EFFECT OF KNOWLEDGE AND COMMERCIALIZATION NETWORKS ON OPPOSITION TO STANDARDS RAM RANGANATHAN Management Department, McCombs School of Business, University of Texas at Austin LORI ROSENKOPF Management Department, The Wharton School, University of Pennsylvania We examine how the multiplicity of interorganizational relationships affects strategic behavior by studying the influence of two such relationships—knowledge linkages and commercialization ties—on the voting behavior of firms in a technological standards- setting committee. We find that, while centrally positioned firms in the knowledge network exhibit lower opposition to the standard, centrally positioned firms in the commercialization network exhibit higher opposition to the standard. Thus, the in- fluence of network position on coordination is contingent upon the type of interorgan- izational tie. Furthermore, when we consider these relationships jointly, knowledge centrality moderates the opposing effect of commercialization centrality, such that the commercialization centrality effect increases with decreasing levels of knowledge centrality. In other words, firms most likely to delay the standard are peripheral in the knowledge network yet central in the commercialization network, which suggests that they have the most to lose from changes to current technology. Over the past two decades, a key thesis that re- position influences several different outcomes, verberates across strategy and organization theory including innovation (e.g., Ahuja, 2000), survival research is that interorganizational relationships af- (Baum, Calabrese, & Silverman, 2000), market share fect both firms’ strategic actions and outcomes. Em- (Shipilov, 2006), and financial performance (e.g., Za- pirical support for this idea demonstrates that a heer & Bell, 2005).
    [Show full text]
  • Deconstructing Statistical Questions Author(S): David J
    Deconstructing Statistical Questions Author(s): David J. Hand Source: Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 157, No. 3 (1994), pp. 317-356 Published by: Wiley for the Royal Statistical Society Stable URL: https://www.jstor.org/stable/2983526 Accessed: 17-01-2019 16:39 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Royal Statistical Society, Wiley are collaborating with JSTOR to digitize, preserve and extend access to Journal of the Royal Statistical Society. Series A (Statistics in Society) This content downloaded from 71.232.212.173 on Thu, 17 Jan 2019 16:39:36 UTC All use subject to https://about.jstor.org/terms J. R. Statist. Soc. A (1994) 157, Part 3, pp. 317-356 Deconstructing Statistical Questions By DAVID J. HANDt The Open University, Milton Keynes, UK [Read before The Royal Statistical Society on Wednesday, December 15th, 1993, the President, Professor D. J. Bartholomew, in the Chair] SUMMARY Too much current statistical work takes a superficial view of the client's research question, adopting techniques which have a solid history, a sound mathematical basis or readily available software, but without considering in depth whether the questions being answered are in fact those which should be asked.
    [Show full text]
  • Supply Chain Analysis in Public Works: the Role of Work Climate, Supervision and Organizational Learning
    Noer SOETJIPTO, Gogi KURNIAWAN, Sulastri SULASTRI, Ari RISWANTO / Journal of Asian Finance, Economics and Business Vol 7 No 12 (2020) 1065–1071 1065 Print ISSN: 2288-4637 / Online ISSN 2288-4645 doi:10.13106/jafeb.2020.vol7.no12.1065 Supply Chain Analysis in Public Works: The Role of Work Climate, Supervision and Organizational Learning Noer SOETJIPTO1, Gogi KURNIAWAN2, Sulastri SULASTRI3, Ari RISWANTO4 Received: September 10, 2020 Revised: November 02, 2020 Accepted: November 16, 2020 Abstract The study aims to analyze the supply chain role of supervision, discipline, work climate, and organizational learning on the performance of community services at the public works. This study took a sample of employees through purposive sampling technique at the Public Works Office and Bina Marga in a regency in East Java. Data through questionnaire was collected through a 5-point Likert scale model. The results show that the application of employee discipline affects the performance of public services, with a contribution of 39.7%, meaning that discipline and organizational learning are implementation factors that have an effect on public service performance. In stepwise regression analysis, the supervisory factor has a correlation with service performance, but it is less relevant, while the work climate is not relevant as a predictor variable to improve public service performance. The study revealed the importance of the supply chain policy of implementing good and clean governance and the enactment of the performance appraisal of the government apparatus established through Good Corporate Governance of the state apparatus. The findings provide a basis to encourage the public sector performance to smooth every step of supply chain management of every government project work, especially in the field of public services.
    [Show full text]
  • In This Number Editorial Why We Need Qualitative Research
    Journal of Epidemiology and Community Health 1994;48:425-426 425 J Epidemiol Community Health: first published as 10.1136/jech.48.5.425-a on 1 October 1994. Downloaded from In this number The Duncan memorial lecture Health expectancy compared with life Dr Duncan was the first medical officer of expectancy health in Britain and is remembered in many A methodological article presents the case for ways including an annual lecture in the city adding health to years in calculations of ex- which benefited from his vision and experience, pected life span - a rather different approach Liverpool. We are pleased to be able to publish to quality of life - but advises caution in using the 1993 lecture which is the second in our and interpreting various methods of cal- lecture series following the 1993 Cochrane lec- culation. ture published earlier this year. Social inequalities and accidents The UK Society for Social Medicine Several articles pick up the theme of social We have a long-established tradition of pub- inequalities ard health and the three short lishing abstracts of lectures and posters ac- reports focus on accidents of various types in cepted by peer review for the Annual Scientific the old and the young. Short reports continue Meeting of this Society which numbered in its to be a way of communicating results much members several ofthe founders ofthis Journal. more quickly than normal, especially since the Although this is a special connection we wel- number of papers submitted to the Journal come the submission of abstracts from other each year has dramatically increased lately.
    [Show full text]
  • Academy of Accounting and Financial Studies Journal
    Volume 20, Number 3 Print ISSN: 1096-3685 Online ISSN: 1528-2635 ACADEMY OF ACCOUNTING AND FINANCIAL STUDIES JOURNAL Marianne James California State University, Los Angeles Editor The Academy of Accounting and Financial Studies Journal is owned and published by Jordan Whitney Enterprises, Inc. Editorial content is under the control of the Allied Academies, Inc., a non-profit association of scholars, whose purpose is to support and encourage research and the sharing and exchange of ideas and insights throughout the world. Authors execute a publication permission agreement and assume all liabilities. Neither Jordan Whitney Enterprises nor Allied Academies is responsible for the content of the individual manuscripts. Any omissions or errors are the sole responsibility of the authors. The Editorial Board is responsible for the selection of manuscripts for publication from among those submitted for consideration. The Publishers accept final manuscripts in digital form and make adjustments solely for the purposes of pagination and organization. The Academy of Accounting and Financial Studies Journal is owned and published by Jordan Whitney Enterprises, Inc., PO Box 1032, Weaverville, NC 28787 USA. Those interested in communicating with the Journal, should contact the Executive Director of the Allied Academies at [email protected] Copyright 2016 by Jordan Whitney Enterprises, Inc., Weaverville, NC, USA EDITORIAL REVIEW BOARD MEMBERS Robert Marley Liang Song University of Tampa Michigan Technological University Robert Graber Steve Moss University of Arkansas – Monticello Georgia Southern University Michael Grayson Chris Harris Brooklyn College Elon University Sudip Ghosh Atul K. Saxena Pennn State University, Berks campus Georgia Gwinnett College Rufo R. Mendoza Anthony Yanxiang Gu Certified Public Accountant State University of New York Alkali Yusuf Hema Rao Universiti Utara Malaysia SUNY Osweg Junaid M.
    [Show full text]
  • Population Based Research
    Dr. Ken Barger 2010 POPULATION-BASED RESEARCH 1. Science a. Science b. Scientific Research c. Basic Research Issues 2. Concepts a. Systems b. Culture c. Society d. Ethnocentrism e. Adaptation 3. The Research Process a. Formulate the Research Issue b. Research Plan (1) Research Goals (2) Research variables - dependent, independent, control (3) Research population - definition, sampling (4) Research Designs Cohort/longitudinal model Cross-sectional model Case-control model (5) Data collection techniques (6) Data analysis (7) Reporting (8) Logistics (9) Schedule (10) Project management (11) Research proposals c. Data Collection Qualitative techniques: participant-observation Quantitative techniques: Public survey d. Data Analysis - check data, patterns, relationships, cause e. Interpretations f. Reporting 4. Principles in Scientific Research Grounded scientific research is based on control of biases Biases: Conceptual, methodological, situational, personal Recognition: Reactions - positive or negative, us or them Maximize chances of best predictive understandings Asking valid questions is essential for valid answers Separate facts from the interpretation of facts Professional and ethical standards Appendix 1: Participant-Observation Methods Appendix 2: Survey Methods Appendix 3: Ethnosemantic Methods POPULATION-BASED RESEARCH 1. Science a. Science Science: The study of natural phenomena Purpose: Discovery of natural laws and principles Causal relationships Predictive laws/relationships b. Scientific Research Science is a method of inquiry
    [Show full text]
  • Statistical Practice: Reprints and Permissions: Sagepub.Co.Uk/Journalspermissions.Nav Putting Society DOI: 10.1177/0263276414559058 on Display Tcs.Sagepub.Com
    Article Theory, Culture & Society 2016, Vol. 33(3) 51–77 ! The Author(s) 2015 Statistical Practice: Reprints and permissions: sagepub.co.uk/journalsPermissions.nav Putting Society DOI: 10.1177/0263276414559058 on Display tcs.sagepub.com Michael Mair University of Liverpool Christian Greiffenhagen Loughborough University W.W. Sharrock University of Manchester Abstract As a contribution to current debates on the ‘social life of methods’, in this article we present an ethnomethodological study of the role of understanding within statistical practice. After reviewing the empirical turn in the methods literature and the chal- lenges to the qualitative-quantitative divide it has given rise to, we argue such case studies are relevant because they enable us to see different ways in which ‘methods’, here quantitative methods, come to have a social life – by embodying and exhibiting understanding they ‘make the social structures of everyday activities observable’ (Garfinkel, 1967: 75), thereby putting society on display. Exhibited understandings rest on distinctive lines of practical social and cultural inquiry – ethnographic ‘forays’ into the worlds of the producers and users of statistics – which are central to good statistical work but are not themselves quantitative. In highlighting these non-statis- tical forms of social and cultural inquiry at work in statistical practice, our case study is an addition to understandings of statistics and usefully points to ways in which studies of the social life of methods might be further developed from here. Keywords ethnomethodology, knowledge, quantitative-qualitative divide, social life of methods, statistics, understanding Introduction Stemming from a growing interest in the ‘social life of method’ (Law et al., 2011; Savage, 2013; and for a more general overview see Mair Corresponding author: Michael Mair.
    [Show full text]
  • Graduate 2020-2021 Catalog
    GRADUATE CATALOG 2020 - 2021 _______________________________________________________ Published by St. Thomas University, Miami Gardens, Florida The programs, policies, requirements and regulations published in this catalog are subject to change as circumstances may require. CONTENTS ACCREDITATION .......................................................................................... 5 BOARD OF TRUSTEES ................................................................................... 5 PRESIDENT'S MESSAGE ................................................................................ 6 VISITING THE UNIVERSITY ......................................................................... 7 Location Map .............................................................................................. 7 Campus Map............................................................................................... 8 ASSOCIATIONS & MEMBERSHIPS ................................................................ 9 MISSION STATEMENT, CORE VALUES & VISION STATEMENT .................. 11 GRADUATE ADMISSIONS ........................................................................... 12 International Student Admissions Procedures ............................................. 15 FINANCIAL AFFAIRS ................................................................................ 18-23 FINANCIAL INFORMATION ........................................................................ 22 VETERANS ADMINISTRATION .............................................................
    [Show full text]
  • Concealing Social Responsibility? Investigating the Relationship Between CSR, Earnings Management and the Effect of Industry Through Quantitative Analysis
    A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Moratis, Lars; van Egmond, Max Article Concealing social responsibility? Investigating the relationship between CSR, earnings management and the effect of industry through quantitative analysis International Journal of Corporate Social Responsibility (JCSR) Provided in Cooperation with: CBS International Business School, Cologne Suggested Citation: Moratis, Lars; van Egmond, Max (2018) : Concealing social responsibility? Investigating the relationship between CSR, earnings management and the effect of industry through quantitative analysis, International Journal of Corporate Social Responsibility (JCSR), ISSN 2366-0074, Springer, Cham, Vol. 3, Iss. 8, pp. 1-13, http://dx.doi.org/10.1186/s40991-018-0030-7 This Version is available at: http://hdl.handle.net/10419/217416 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence.
    [Show full text]
  • Research Methodology for Ethnostatistics in Organization Studies
    The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2046-6749.htm Ethnostatistics Research methodology for in organization ethnostatistics in organization studies studies: towards a historical ethnostatistics 327 Stella Stoycheva and Giovanni Favero Received 4 March 2019 ’ Revised 15 January 2020 Department of Management, UniversitaCa Foscari Venezia, Venice, Italy 19 May 2020 Accepted 22 May 2020 Abstract Purpose – While quantification and performance measurement have proliferated widely in academia and the business world, management and organization scholars increasingly agree on the need for a more in-depth focus on the complex dynamics embedded in the construction, use and effects of quantitative measures (pertaining to the thread of research called ethnostatistics). This paper develops a pluralistic method for conducting ethnostatistical research in organizational settings. Whilst presenting practical techniques for conducting research in live settings, it also discusses how historical approaches which focus on source criticism and contextual reconstruction could overcome the limitations of ethnostatistics. Design/methodology/approach – The methodological approach of this paper encompasses an in-depth discussion of the ethnostatistical method, its underlying assumptions and its methodological limitations. Based on this analysis, the authors propose a pluralistic method (model) for conducting ethnostatistical research in organizational settings based on the integration of 1) research practices employed by one of the authors conducting ethnostatistical research in a large multinational organization and 2) best practices from ethnographic and historical research. Findings – This paper suggests how historical approaches can successfully join ethnostatistical enquiries in an attempt to overcome some limitations in existing conventional methods.
    [Show full text]
  • Graduate Catalog
    GRADUATE CATALOG 2018 - 2019 _______________________________________________________ Published by St. Thomas University, Miami Gardens, Florida The programs, policies, requirements and regulations published in this catalog are subject to change as circumstances may require. CONTENTS ACCREDITATION .......................................................................................... 5 BOARD OF TRUSTEES ................................................................................... 5 PRESIDENT'S MESSAGE ................................................................................ 6 VISITING THE UNIVERSITY ......................................................................... 7 Location Map .............................................................................................. 7 Campus Map............................................................................................... 8 ASSOCIATIONS & MEMBERSHIPS ................................................................ 9 MISSION STATEMENT, CORE VALUES & VISION STATEMENT .................. 11 GRADUATE ADMISSIONS ........................................................................... 12 International Student Admissions Procedures ............................................. 15 FINANCIAL AFFAIRS ...................................................................................... 19 FINANCIAL INFORMATION ........................................................................ 23 VETERANS ADMINISTRATION ...................................................................
    [Show full text]
  • 25008401. Pfv. Goa
    Edinburgh Research Explorer The Role of Administrative Data in the Big Data Revolution in Social Science Research Citation for published version: Connelly, R, Playford, C, Gayle, V & Dibben, C 2016, 'The Role of Administrative Data in the Big Data Revolution in Social Science Research', Social Science Research, vol. 59, no. September 2016, pp. 1-12. https://doi.org/10.1016/j.ssresearch.2016.04.015 Digital Object Identifier (DOI): 10.1016/j.ssresearch.2016.04.015 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Social Science Research Publisher Rights Statement: © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/). General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 27. Sep. 2021 Social Science Research xxx (2016) 1e12 Contents lists available at ScienceDirect Social Science Research journal homepage: www.elsevier.com/locate/ssresearch The role of administrative data in the big data revolution in social science research * Roxanne Connelly a, , Christopher J.
    [Show full text]