Predicting the quality of a survey question from its design characteristics

Predicting the quality of a survey questionfrom its design characteristics

Daniel OberskiDept. of Methodology & Statistics,Tilburg University, The Netherlands

U N I V E R S I T A T

P O M P E U F A B R A

Predicting the quality of a survey question from its design characteristics Daniel Oberski

Question #1: Where is Tilburg?


Summary

• We wanted to predict how “good” a survey question isbased on its “design characteristics”, i.e. choices such as #categories, DK present/not, use of showcards, etc.(Willem’s talk 1 & 2)

→ Trained a random forest of regression trees to predictthe ESS multitrait-multimethod estimates of reliabilityand method effect (Cf. Melanie’s talk) from these questioncharacteristics, as coded by teams of human coders.

• Prediction powers a webapp allowing users to codequestion & get predictions ± uncertainty (Diana’s talk).


Simple example


European Social Survey, 2002

Method A:

3

ENTER START TIME: A1 TvTot CARD 1 On an average weekday, how much time, in total, do you spend watching television? Please use this card to answer.

No time at all

Less than ½ hour

½ hour to 1 hour

More than 1 hour, up to1½ hours

More than 1½ hours, up to 2 hours

More than 2 hours, up to 2½ hours

More than 2½ hours, up to 3 hours

More than 3 hours

(Don’t know)

A2 TvPol STILL CARD 1 And again on an average weekday, how much of your time watching television is spent watching news or programmes about politics and current affairs1? Still use this card. No time at all 00

Less than ½ hour 01

½ hour to 1 hour 02

More than 1 hour, up to 1½ hours 03

More than 1½ hours, up to 2 hours 04

More than 2 hours, up to 2½ hours 05

More than 2½ hours, up to 3 hours 06

More than 3 hours 07

(Don’t know) 88

1 About “politics and current affairs”: about issues to do with governance and public policy, and with the people connected with these affairs.

00 GO TO A3

01

02

03

04 ASK A2

05

06

07

88

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Design characteristics for method A


Design characteristics for method B


Reliability and common method variance estimatesfrom an MTMM experiment

For the TV watching example:

Reliability Methodvariance

Method A: 8 categories 0.796(0.01)

0.098(0.01)

Method B: write (after outlier del’n) 0.819(0.02)

0.140(0.01)


Goal of this research:

obtain a rough estimate of the reliability and method effects ofthese kinds of questions without needing amultitrait-multimethod experiment.


ESS multitrait-multimethod experiments

• In the European Social Survey (ESS), every round aboutsix MTMM experiments are done;

• Each experiment usually estimates the quality for 9questions (Method-Trait combinations).

• Range of topics is reasonably diverse, though factualquestions are underrepresented.

• In total about 1051 method-trait combinations available,3483 “questions” when the analysis is done separately percountry


“Old” multitrait-multimethod experiments

• In addition to the ESS, an older series of experiments alsoexists (Andrews; Koltringer; Saris; Scherpenzeel, 1990’s)

• These add another 1089 questions for which reliability andcommon method coefficients are estimated

• Combining the two datasets (ESS question qualities andOld experiment qualities, we created a database of 3483questions with their reliability and common methodestimates (1051 unique method-trait combinations).


Reliability and common method variance estimates of3483 questions (1051 unique method-trait comb’s)

Reliability coefficient

Reliability coefficient

0.4 0.6 0.8 1.0

0

200

400

600

800

Common method variance


0.0 0.2 0.4 0.6 0.8

0

500

1000

1500


Design characteristics of questions

• Social Desirability• Centrality• Reference period• Question

formulation• WH word used• Use of gradation• Balance of the

request• Encouragement• Showcards

present• Showcards have

pictures• ...

• Emphasis on subjectiveopinion in request

• Information about theopinion of other people

• Use of stimulus orstatement in the question

• Absolute or comparativejudgment

• Response scale: basicchoice

• Number of categories• Labels full, partial, or no• Labels full sentences• Knowledge provided• Survey mode• ...

• Order of the labels• Correspondence between

labels and numbers of thescale

• Theoretical range of thescale

• Neutral category• Number of fixed reference

points• Don’t know option• Interviewer instruction• Respondent instruction• Extra motivation, info or

definition available?• Agree-disagree scale• . . .

(Saris & Gallhofer 2007)



Coding design characteristics of the 3483 questions

• For each of the 3483 questions in all countries, a team ofcoders coded 40 design characteristics of the question;

• Some codes were automatically generated by NaturalLanguage Processing software (syllables, words, etc).

• Coders were students, assistants to the local coordinatorsof the ESS, and two experts;

• For English source version, experts double-codedquestions independently, then created consensus codes;

• Non-expert codes were quality-controlled by detailedcomparison with consensus codes for the English source;

• In a meeting between the experts and each other coder,the discrepancies were discussed and either corrected orleft in as true differences.


Domain of question # questions # unique comb’sInternatl politics 64 64Health 244 88Living conditions 433 234Other beliefs 292 292Work 509 119Family 109 7Personal relations 308 62Consumer behavior 26 26Leisure activts 131 63National gvt 169 31Institutions 412 35Political parties 30 30Trade unions 12 12Economy 266 16Other 370 89


Concept of question # questions # unique comb’s

Evaluative belief 703 136Feeling 1044 161Importance 96 96Expectation 39 30Facts, behavior 111 60Complex concept 44 44Other simple 1338 608


Meta-analysis dataset

• For each of the 3483 questions, we have in the database:• The estimated quality (reliability and common method

coefficients)• About 50 design characteristics (through hand- and

automatic coding)

• Predict reliability and method effect estimates from designcharacteristics


Meta-analysis using random forests of regression trees

→


Example of one (1) regression tree (CART).


|

domain=3,4,7,11,13,14,112

domain=3

gradation>=0.5 position< 339.5

position>=410

concept=1,2 position< 404.5

concept=1,73,78

position< 322.5

ncategories>=4.5

domain=6,101,103,120

domain=4,7,11,13,14,112

gradation< 0.5 position>=339.5

position< 410

concept=73,75,76 position>=404.5

concept=2,76

position>=322.5

ncategories< 4.5

1.955n=1988

1.724n=1303

0.9636n=108

0.4959n=36

1.198n=72

1.793n=1195

1.642n=722

2.023n=473

1.544n=108

1.28n=76

2.17n=32

2.165n=365

1.97n=217

2.45n=148

2.394n=685

1.489n=138

2.622n=547

2.384n=233

2.799n=314

2.681n=260

3.364n=54

Example regression tree for reliability coefficient


Obtaining a prediction from a random forest


Training the forest of regression trees


Random forest algorithm (Breiman 2001)

1 Randomly sample (w/ replacement) cases (questions)→ some “in-bag” cases;→ Rest is “out-of-bag”.

2 Randomly sample features (characteristics);3 Grow a regression tree on the in-bag cases using the

subset of selected features;4 Calculate mean square prediction error on the out-of-bag

cases→ built-in cross-validation;5 Back to (1) until 1500 trees have been grown.


joined.data

Ordered by number of missing items

avgabs_intronabst_intro

nnouns_introshowc_boxesshowc_questshowc_startopinionothershowc_horizshowc_lettershowc_over

positionncategories

range.correspondencescal_neutral

absoluteconceptdomain

dont_knowform_basic

futureinstr_interv

instr_responmotivation

pastscale_basic

stimulususedshowcard

interviewervisual

fixrefpointslabels

scale_corresscale_trange

labels_grammcomputer.assisted

intr_requestavgabs_total

avgsy_totalavgwrd_introavgwrd_total

balancecentrality

countryencourage

gradationintropresentknowledge

labels_orderlanguage

nabst_totalnnouns_total

nsub_questnsyll_total

numsub_intronwords_intronwords_totalquestiontype

rel.estrel.est.orig

repetitionscale_urange

socdesirstudy

subjectiveopsymmetry

used_WH_wordval.est

val.est.origfrom


Missing data• Imputed using multiple imputation with chained equations

(MICE) (van Buuren & Groothuis-Oudshoorn 2011).• A small random forest (bush?) is grown for each imputed

dataset• These bushes are joined to form the final forest• “raimforest” (RAndom IMputation forest)


Out-of-bag crossvalidation

• R2 for logit(reliability coef.) prediction: 0.64• R2 for logit(validity coef.) prediction: 0.84


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Quality prediction

Quality predicted from random forests

MT

MM

est

imat

e of

qua

lity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0


Fitted versus residuals for random forest predictions

Reliability

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Back to the example




Evaluation and future work


Some of the challenges encountered

• (Coding process)• (MTMM estimation process)• Mapping the old coding system into the new...• Missing data; non-applicable characteristics (→ raimforest)• Multicollinearity, aliasing of interactions (→ RF)• Propagating the uncertainty about the MTMM estimates

(not solved)• Coming up with a reasonable prediction interval (→ RF)• Dealing with main vs. supplementary (estimate an order

effect and assume the coded question is like the mainquestionnaire)

• ...


Things that still need doing in my opinion• Publish journal articles to let the world know about this

meta-analysis!• Model was cross-validated on ESS R1–3. Also validate

predictions against new ESS MTMM results.• Evaluate practical effect on estimates of interest before &

after correction for reliability and method effect: howdifferent are the estimates in practice?

• Seriously reduce the number of codes• Planned experiments to reduce multicollinearity, aliasing in

the meta-analysis• Propagate uncertainty about the MTMM estimates• Perhaps compare w/ linear regression/other meta-analysis

techniques• ...


Thank you for your [email protected]


Science

Predicting the quality of a survey question from its design characteristics