The Work Programme of COMPASS Research Centre · COMPASS Research Centre 6th Wellington Colloquium Statistics NZ, Conference Room 3 August 2012. The University of Auckland New Zealand

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Using Advanced Techniques with Existing Data

The Work Programme ofCOMPASS Research Centre

6th Wellington ColloquiumStatistics NZ, Conference Room3 August 2012

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ndColloquium Outline

10.00 Introduction – Peter DavisContributing to Social ScienceContributing to Policy

10.45 Micro-simulation work programmeModelling the Early Life-CourseDr. Barry Milne, Research FellowPolicy Modelling and Demographic AgeingRoy Lay-Yee, Senior Research Fellow

12.15 LUNCH BREAK

13.00 Updating the NZSEI – Dr. Barry Milne

13.30 Health Services Research and Policy Evaluating Performance in the Public Hospital SectorProfessor Peter Davis (University of Auckland COMPASS)Dr. Barry Milne (University of Auckland, COMPASS)Dr. Jaikishan Desai (Victoria University Wellington, HSRC)

15.00 AFTERNOON TEA - CONCLUSION

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Core team members: two research fellows, two statisticians, and data managerExpertise in modelling and health services research

Survey/statistical work (NZ Election Survey)

Summer school

Data repository

COMPASS Team –Open for business!

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Colloquium PurposeCommunicate! Collaborate! Transfer! Translate!

COMPASS Work Programme

Contributing to Social Science• Updating a socio-economic index• Managing research data• Summer school

Contributing to Policy• Lifting the performance of the health system• Promoting micro-simulation techniques

Colloquium

A N U P D A T E O F T H E N E W Z E A L A N D

S O C I O E C O N O M I C I N D E X ( N Z S E I )

B A R R Y M I L N E

B R I A N B Y U N , A L A N L E E , P E T E R D A V I S

Assessing socio-economic status through occupation

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DISCLAIMER: Access to the data used in this study was provided by Statistics New Zealand under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975. The results presented in this study are the work of the author, not Statistics New Zealand.

Why measure SES?

Research Can test hypotheses about the impact of unequal distribution

of opportunities, advantages, resources and power on Health, wellbeing, life choices, use of services, crime

Moderating the impact of other risk factors

Can investigate SES stability and mobility, both within one’s life and inter-generationally

Describing populations

Funding allocation Social and health services are sometimes funded (in-part)

based on the socio-economic characteristics of the areas that they serve.

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MANAGING RESEARCH DATAFOR PROFESSIONAL SOCIAL SCIENCE

PETER DAVISMARTIN VON RANDOWGERARD COTTERELL

RC33 CONFERENCE, THE UNIVERSITY OF SYDNEYTUESDAY, 10 JULY 2012

MANAGING RESEARCH DATA

www.compass.auckland.ac.nzwww.nzssds.org.nz

MANAGING RESEARCH DATA

Data access – preserving and making available research data sets & metadata

Research support – ‘Enhanced Publications’and related knowledge products

Teaching – hosting metadata, teaching data subsets and associated workbooks

Preserving and making available research data sets & metadata

DATA HOLDINGS & EXTERNAL USAGE

50 data sets archived including New Zealand Election Study data (1987–2008)– Professor Jack Vowles

International Social Survey Programme data for New Zealand (1991–2010) – Professor Philip Gendall

World Internet Project for New Zealand (2007 & 2009)– Professor Charles Crothers

Health data sets (adverse events, oral health care,primary care, sexual health) – Professor Peter Davis

150 registered users; 30 specific data requests Mainly re NZES, ISSP and oral health surveys

Promoting enhanced publications and related knowledge products

EXPLANATION

What are they Publications enriched with three types of information:

Research data (evidence of the research) Extra materials (to illustrate or clarify) Post‐publication data (commentaries, ranking)

Why are they useful Promote the availability of reusable scientific data Allow verification of the outcomes of the research Reduce the need to ‘re‐invent the wheel’(i.e. code and data are publicly available for re‐use)

Improving efficiency and underpinning transparency in research: enhancing research

resources via a research repository

Improving efficiency and underpinning transparency in research: enhancing research

resources via a research repository

Emma Gullery

COMPASS Research Centre and the Department of Statistics

End of Project Presentation

February 2012

Improving efficiency and underpinning transparency in researchEmma GulleryEmma Gullery

Project Objectives

Utilise the New Zealand Social Science Data Serviceby publishing online methods and data that have been analysed.

Supply well‐documented work for collaborationso other researchers do not have to replicate the same work.

Compile user‐friendly code available for download online.

Emma GulleryEmma Gullery Improving efficiency and underpinning transparency in research

Data & Filtering

National Minimum Data Set (NMDS) Records of hospital discharge data as far back as 1988. Analyses performed on the data to assess quality of healthcare,

provisions of services and government policy review. Includes both public and private hospital events. Filtered to make comparable across years.

Filtering Involves removing certain groups of observations for different reasons. MoH performs several steps to enable valid comparisons across time. Changes in code definitions and diagnosis criteria often affect subgroups.


Filtering Code


Annotations


nzssds.org.nz – Indicators


The nzssds.org website – In‐hospital mortality


Hosting metadata, teaching data subsets and associated workbooks

TEACHING

Workbooks and teaching data sets designed for self-directed learning of SPSS Three produced so far based on:

ISSP 2002 (family & gender roles); ISSP 2009(social inequality); NZES 2008 (election data)

Metadata used in teaching Illustrative study metadata used in survey class Five studies with varying survey characteristics

Survey data used in teaching Quantitative projects for research methods class ISSP used extensively for range of topics of interest

FUTURE DEVELOPMENTS

Data service functions1. Data saving and sharing

Qualitative data Links with librarians, science policy

2. Enhanced publications & knowledge products Encourage “open science” platform

3. Teaching data sets and workbooks Parallel suite for qualitative data Links with other departments, universities

New Zealand Social Statistics Network ‐ short courses in research methods

• Research methods courses offered since 2005• Courses

– Wellington February each year– Auckland – July

• Most courses are 5 days• For more information

– www.nzssn.org.nz– Contact: [email protected]

Proposed courses for Feb. 2013, Wellington

• INTRODUCTION TO STATISTICS• QUALITATIVE RESEARCH TECHNIQUES• CASE STUDY RESEARCH• APPLIED COMPUTER‐ASSISTED QUALITATIVE DATA ANALYSIS USING NVivo• INTRODUCTION TO STRUCTURAL EQUATION MODELLING USING AMOS™ or Mplus• INTRODUCTION TO SURVEY DESIGN• INTRODUCTION TO SOCIAL NETWORK RESEARCH AND ANALYSIS• FUNDAMENTALS OF SPSS• DATA ANALYSIS USING STATA• INTERMEDIATE STATISTICS• INTRODUCTION TO PROGRAM EVALUATION• APPLIED MULTIVARIATE USING STATA• MIXED METHODS IN SOCIAL RESEARCH• APPLIED STRUCTURAL EQUATION MODELING USING Mplus• ADVANCED QUALITATIVE DATA ANALYSIS USING NVivo 9

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Colloquium PurposeCommunicate! Collaborate! Transfer! Translate!

COMPASS Work Programme

Contributing to Social Science• Updating a socio-economic index• Managing research data• Summer School

Contributing to Policy• Lifting the performance of the health system• Promoting micro-simulation techniques

Colloquium

Lifting the performance of New Zealand’s health system.

A research collaborative

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The performance question

The performance question How do we apply evidence and science to our increasingly

expensive and strained health systems to maximise their fairness (equity), their efficiency, and their effectiveness?

Health System in Transition report on England (2011) – LSE Health/European Observatory

• Doubled nominal expenditure 1997-2008• Continuous structural innovation and transformation• 50,000 more doctors; 100,000 more nurses and midwives

Yet• Productivity did not increase• Health inequalities were not reduced

Although• Access to elective care improved, as did overall health status

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Introducing Microsimulation

WorkshopSydney, July 9, 2012

International Sociological Association – Research Committee RC33 on Logic and Methodology – 8th Conference on Social Science Methodology

Roy Lay-Yee and Barry Milne

University of Auckland, New Zealandand COMPASS Research Centrewww.compass.auckland.ac.nz

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• Various features

• Computing platform

• Data integration

• Implementation

• Application

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Start with a real sample of people. A cross-sectional sample of the NZ population over 65

Apply statistically-derived rules to reproduce patternsA sample of NZers over 65 with typical biographies over a year

We have created a virtual world (our simulation model)

Predict what might happen if conditions were to changeTest plausible scenarios in a society that is demographically ageing

JAVA / R tool for simulation, to assist policy decision making31

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to policy making

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Provides an evidence base and a decision-support tool

Recognises importance of human agency (in social context)

Focuses on micro processes that generate macro structure→ pathways for policy intervention

Enables thought experiments, testing policy scenarios, making policy options explicit

It’s easier for policy makers to grasp and interpret (narratives, graphics)

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ndInternational example 1:EUROMOD - Europe

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• A static tax-benefit model for the European Union (2000s)

• Enables researchers and policy analysts to calculate, in a comparable manner, the effects of taxes and benefits on household incomes and work incentives for the population of each country and for the EU as a whole

Institute for Social & Economic Research University of Essex

https://www.iser.essex.ac.uk/euromod/

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ndInternational example 2:LIFEPATHS - Canada

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• Dynamic model of individuals and families from 1872 birth cohort to today• Creates synthetic life histories from birth to death that are representative of the

history of Canada’s population• Can be used to evaluate government programs, or to analyse societal issues of a

longitudinal nature, e.g. intergenerational equity

Statistics Canada

http://www.statcan.gc.ca/microsimulation/lifepaths/lifepaths-eng.htm

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APPSIM - Australia

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• Dynamic model simulates the life cycle of 200,000 individuals (1% sample of census) from 2001 to 2050

• Shows how the Australian population develops over time under various scenarios• Allows the social and fiscal impacts of policy changes over time to be simulated

National Centre for Social & Economic Modelling University of Canberra

http://www.natsem.canberra.edu.au/models/appsim/

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• Data sources – quantitative, qualitative, findings from other studies, guesstimates

• Combining data

• Combining parameters

• Data → base file (initial conditions)

• Parameters (from statistical analyses) → simulation rules

Integration

PCASO: New Zealand and Australian data sources and model contributions

Study National Health Surveys

General Practice Survey

(Doctors)

National Health Survey

General Practice Survey

(Patient visits)

Country New Zealand New Zealand Australia New Zealand

Year 1996/7 (children)2002/3 (adults)

2001/2 1995 2001/2

Sample Children & adults Doctors (GP) Children & adults Patient visits

N 13,548 244 53,828 9,272

Model Component

Community Practitioner Morbidity; Community

Morbidity; Practitioner

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• Test ‘what if’ policy scenarios

o Projection into the future; alternative settings

o Simulate impact of policy change (beforehand)

o Assumes everything else is the same

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10.00 Introduction – Peter DavisContributing to Social ScienceContributing to Policy

10.45 Micro-simulation work programmeModelling the Early Life-CourseDr. Barry Milne, Research FellowPolicy Modelling and Demographic AgeingRoy Lay-Yee, Senior Research Fellow

12.15 LUNCH BREAK

13.00 Updating the NZSEI – Dr. Barry Milne

13.30 Health Services Research and Policy Evaluating Performance in the Public Hospital SectorProfessor Peter Davis (University of Auckland COMPASS)Dr. Barry Milne (University of Auckland, COMPASS)Dr. Jaikishan Desai (Victoria University Wellington, HSRC)

15.00 AFTERNOON TEA - CONCLUSION

The Work Programme of COMPASS Research Centre · COMPASS Research Centre 6th Wellington Colloquium Statistics NZ, Conference Room 3 August 2012. The University of Auckland New Zealand

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