lecture 9 of 47c5 social research process i:

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Lecture 9 of 47C5 Social Research Process I:

Using Secondary Datasets Paul Lambert, 8.10.03, 9-10am

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47C5: Survey research lectures

Lecture 8: The Survey Method

Intro. to & qualities of survey method

Lecture 9: Using Secondary Datasets

Data access and issues

Lectures 11/12: Sampling

Sample design, data collection / analysis

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Resources for lectures 8,9,11,12

• Lecture slides on WebCT site

• 2 Reading lists: – Initial list in 47C5 unit outlines– Some additions on further list at WebCT site

Also: http://staff.stir.ac.uk/paul.lambert/teaching.htm

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L9: Using Secondary Datasets

1) Introduction and background

2) Accessing secondary datasets

3) Qualities of secondary datasets

4) Data analysis / management issues

5) Key variables in survey research

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1) Introduction and Background

• Vast quantity of surveys conducted

• An efficient step would be to analyse existing data (secondary) rather than personally collect your own (primary)

• Data archives collate survey datasets and supply them for secondary analysis

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Large scale data

Lecture 8: Modern social survey analysis most often either large scale secondary or small scale primary

• Several assets of large scale surveys: – Generalise – Multivariate (more variables and more cases)

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Large surveys’ high expenses:

• Government funds many large surveys

(also EU; LA’s; charities; commercial)

• Often made available freely or at low cost An ideal research tool (see ESRC):

– Quick to access– Methodological rigour– Falsifiable – others can access also

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Secondary analysis of surveys

• Makes particular sense when large scale datasets are desirable

• Also often applies to smaller surveys

• Involves particular issues of data analysis, management and interpretation

• …Is a highly marketable skill!

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2) Accessing Secondary datasets

• Internet and computing developments have revolutionised delivery of data resources

• Three steps to data access:1. Find out survey details / documentation

2. Apply for access from archive or collectors

3. Obtain and analyse the data

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2.1) Finding Details

• The modern way: Internet search, eg UK data archive, UK

Question Bank, many others (reading list)

• The old fashioned way: Look out for research reports using datasets

and contact authors / data collectors directly

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The UK data archive www.data-archive.ac.uk

• ESRC Efforts to encourage usage

• ‘Athens’ authentication

• Survey descriptions and lists of research

• Variable lists

• ‘NESSTAR’ to browse data

• Links to more sources for secondary data

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2.2) Applying for access

• The modern way: Email / webpage forms, agree to conditions

of access (anonymised data to reduce ‘disclosure risk’)

• The old fashioned way: Personal contacts and requests to original

data collectors

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2.3) Obtaining / analysing data

• The modern way: Download data from supplier (usually compressed

and portable format), use with documentation and variable lists in data analysis package (eg SPSS)

• The old fashioned way: A plain text computer file on disk, and copy of

original questionnaire, arrive by post: good luck!

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3) Qualities of Secondary data

• Efficient: cheap & quick to access / analyse

• Scale of data larger than most can afford

• Methodological rigour of major suppliers: – Sampling – Questionnaire and variable design – Trained interviewers and data entry

• Falsifiable nature of analysis

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Some drawbacks

• Distance from data collection – Harder to assess reliability / validity– Many variables already pre-coded – Can’t change / add anything in study

• Time delays in accessing to results

• Data analysis / management complex

• May be bracketed with survey originators

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Analytical possibilities vary by survey data type

One division: Mirco-social v’s Macro-social most social survey analysis uses former

• Macro-social data– Government statistics www.statistics.gov.uk– Cross-national statistics (UN, OECD)– Macro-economic time series (trends / forecasts)– Beware: many critiques of ‘official statistics’

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Types of micro-social data

• Census’s– General overview of whole population– Disclosure risk issues

• Cross-sectional surveys – Most widely used sources– Huge range of topic coverage– May be used to study small / rare populations

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..more types of micro-social data

• Longitudinal datasets– Repeated cross-sections– Panel datasets– Cohort studies – Retrospective studies – Strengths: understand process and causality– Problems: sampling and attrition; complexity

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..more types of micro-social data

• Cross-nationally comparative datasets– Focussed surveys (IPUMS census’s; ISSP;

World Values Survey; European Social Survey)– Longitudinal studies (LIS; ECHP; CHER)– Many analytical attractions, but issues of

comparable analysis are complex

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Some major UK social surveys

• Cross-sectional:

OPCS Census British Crime Survey

Labour Force Survey British Social Attitudes

New Earnings Survey British Election Studies

Family Expenditure S. Policy Studies (Ethnicity)

General Household Survey Social Mobility enquires

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Some major UK social surveys

• Longitudinal:

OPCS Census Longitudinal Study

Labour Force Surveys (repeated X section)

British Household Panel (Scottish, W, NI extensions)

Cohort studies: 1946, 1958, 1970, 2001, YCS

British election panel studies

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4) Data analysis & management

• ..become core skills in using secondary surveys…

• Software packages – SPSS, SAS, STATA, .. – with wide capabilities

• Good and bad practice – should only do sensible things with data… (see 47C6)

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Data Analysis

• Good practice– Reflects properties of variables– Describes output in appropriate context

• Bad practice (..is widespread)– Forcing data into style of analysis – Attributing false properties to data– Over zealous conclusions

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Data AnalysisAssessing appropriateness of data analysis

techniques is inherent to assessing survey research findings (need to learn about

statistics and analysis..)

• Secondary data analysis misuses common – too easy to get data & run (bad) analyses

• Primary theme: must remember social context and theories throughout analysis

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Data management

• Matching data files

• Coding / transforming variables

• Dealing with ‘missing’ data

Secondary dataset management tends to be:

• More complex • More error prone • Subject to external scrutiny

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5) Key variables in social investigations

• Variable operationalisation key to surveys

• Choices: - in initial data collection

- in data recoding / analytic treatment

• In secondary analysis, researcher can only influence latter

• Here: Comment on some widely used variables (cf Burgess 1986, others)

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Age and gender:

• Age– Linear or grouping or quadratic.. - which has

most social significance?– Age / Period / Cohort confusion

• Gender: – Deceptively simple, politically sensitive– Concepts of sexuality; masculinity

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Education and occupation:

• Education– Changing ‘levels’ of education over time– Education as proxy for ability, intelligence?

• Occupation– Contested meanings of labour market status– Occupational indicators of stratification– Occupational gender segregation

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Ethnicity and Health:

• Ethnicity– Existence of groups or racist language?– Identity v’s nationality v’s religion v’s ..

• Health– Subjective nature of self-reports– Changing terminology and social stigmas

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Income and crime

• Income– High non-response and recording errors– Current income general well-being?

• Crime– Most crimes not reported– Categories of crimes arbitrary / debated /

changing

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Key variables: summary

• Methods guidelines on appropriate handling

‘Harmonised concepts and questions’; textbooks; papers / debates specific issues

• Choices / approximations always used

• Research reports and methods appendices must explain and justify position taken

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Summary: Secondary datasets

• Wealthy resource for survey analysis

• Issues and problems in use – but benefits outweigh disadvantages

• To understand, best tactic is to read social science research reports based on relevant secondary datasets