s4 - measuring variables & data collection [dr a bermudez]

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Amiel Nazer C. Bermudez, MD, MPH 1

Dening and Measuring Variables

Amiel Nazer C. Bermudez, MD, MPH

Learning ObjecBves

At the end of the lecture, parBcipants should be able to:

DierenBate the following: Constant versus variable QuanBtaBve versus qualitaBve variable Discrete versus conBnuous Scales of measurement Independent versus dependent versus extraneous variables

DierenBate concept, indicator, and variable Contrast conceptual and operaBonal deniBons Enumerate suggested components of an operaBonal deniBon

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Learning ObjecBves

At the end of the lecture, parBcipants should be able to:

(con%nued)

Enumerate commonly-used methods of collecBng data Enumerate basic principles in the construcBon of quesBonnaire Cite commonly-used convenBons in the construcBon of quesBonnaires

Contrast open- and close-ended quesBons Discuss basic consideraBons in the pre-tesBng of data collecBon tools

Variables

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Constant versus Variable

Constant A phenomenon whose value remains the same from person to person, from Bme to Bme, or from place to place.

Examples: speed of light, Avogadros number Variable

A characterisBc whose value diers from one individual to another, or from one period to another in the same individual.

Examples: ages of gestaBon, smoking habit

Daniel & Cross, 2013

QuanBtaBve versus QualitaBve Variable

Qualita9ve One whose categories are simply used as labels to dis9nguish one group from another.

Examples: sex, marital status, educaBonal a[ainment Quan9ta9ve

One whose categories can be measured and ordered according to quan9ty or amount, or whose values can be expressed numerically.

Example: height, weight, number of term pregnancies


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QuanBtaBve Variable Discrete versus Con%nuous

Discrete quan9ta9ve Variable that can assume only integral values or whole numbers

Examples: number of hospital beds, household size Con9nuous quan9ta9ve

Variable that can assume any value including frac9ons or decimals

Examples: serum uric acid levels, body mass index

Mendoza et al, 2010

Measurement

Dened as the assignment of numbers (or categories) to objects or events according to a set of rules.

Since measurement may be carried out under dierent sets of rules, there are several scales of measurement (i.e. nominal, ordinal, interval, and raBo)


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Scales of Measurement

Nominal Categories represent a set of mutually exclusive and exhaus9ve classes to which individuals or objects (a[ributes) may be assigned.

Examples: sex, naBonality, blood groups, religion Ordinal

Similar with the nominal scale + categories can be ranked or ordered

Examples: educaBonal a[ainment, severity of disease

Mendoza et al, 2010


Interval Similar with the ordinal scale + exact distance between all adjacent categories are equal but the zero point is arbitrary or meaningless.

Examples: temperature measurement, calendar Bme Ra9o

Similar with the raBo scale + zero point is meaningful (i.e. zero means absence of the a[ribute)

Examples: blood pressure, number of DMF teeth

Mendoza et al, 2010

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From the slides of Dr NR Juban, undated


From the slides of Dr NR Juban, undated

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Indicate whether the following is a qualitaBve or a quanBtaBve variable. Indicate also the scale of measurement.

Hospital bed capacity QuanBtaBve, discrete, raBo

EducaBonal a[ainment QualitaBve, ordinal

Mid-upper arm circumference QuanBtaBve, conBnuous, raBo

Forced expiratory volume QuanBtaBve, conBnuous, raBo

Region of residence QualitaBve, nominal

ClassicaBon of Variables in Health Research

Independent variable Factor supposed to be responsible for bringing about a change in a phenomenon or situa9on

Also called the exposure variable Dependent variable

Factor that changes with the introduc9on of an independent variable

Also called the outcome variable

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ClassicaBon of Variables in Health Research

Extraneous variables Extraneous variables may distort the true rela9onship between the independent and dependent variables

Data on extraneous variables should be collected in the research, to the extent possible, to allow for proper staBsBcal adjustment.

Some types of extraneous variables Confounders Eect measure modiers Intermediates Colliders

Extraneous Variables [opBonal] Confounder

Can result in distor9on of the true measure of eect between the exposure and outcome.

IdenBcaBon of confounders in research is based primarily on literature search.

ProperBes It is an independent risk factor for the outcome / disease It is associated with the exposure It does not lie along the causal pathway between exposure and outcome (i.e. not an intermediate)

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Extraneous Variables [opBonal] Confounder (example)

SES is a confounder because: SES is a risk factor for Kawasaki Disease (Bronstein et al, 2000) Crowding is associated with SES (Adler & Newman, 2002) SES is not an intermediate between crowing and Kawasaki Disease

If SES is not properly accounted for in the study, there might be overesBmaBon of the eect of crowding on the risk for KD

Crowding Kawasaki Disease

Socio-economic status

Variable Selec9on and Deni9on

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SelecBng Study Variables

Variables to be measured in the proposed research should be selected on the basis of their relevance to the study objecBves.

In the face of nancial, logisBcal, ethical, or other limitaBons, other variables that approximate the desired variable (or concept) can be selected. For example, instead of immune status (measured by serum anBbody levels), immunizaBon status (measured by immunizaBon card) can be selected instead.

OperaBonal DeniBon Concept and Variable

CONCEPT VARIABLE SubjecBve impression No un i f o rm i t y a s t o i t s understanding among dierent people

Cannot be measured

Measurable though the degree of precision varies from scale to scale, and/or from variable to variable

Kumar R, 2011

Concept( Indicator( Variables(Concept( Indicator( Variables(

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Conceptual versus OperaBonal DeniBon

CONCEPTUAL

DicBonary deniBon DeniBon widely accepted in the scienBc community or those set forth by relevant agencies

OPERATIONAL

Meaning of the variable as specied by the researcher

Must include the acBviBes ( o r o p e r a B o n s ) f o r measuring the variable

OperaBonal DeniBons Suggested Approach

Conceptual deniBon of the variable (may be omi[ed for common variables)

How the variable will be measured in the study Categories of the variable (or values the variable is expected to assume)

Method of data collecBon Possible sources of bias

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OperaBonal DeniBon (example) Variable: Kawasaki Disease Status

Opera9onal Deni9on: Diagnosed in a child who presents with fever (T 37.7 OC) for at least 5 days AND the presence of at least four of the ve following signs, and whose illness cannot be explained by other known disease process: (1) bilateral bulbar conjuncBval injecBon, generally non-purulent; (2) changes in the mucosa of the oropharynx, including injected pharynx, injected and / or dry ssured lips, strawberry tongue; (3) changes in the peripheral extremiBes, such as edema and / or erythema of the hands or feet in the acute phase; or periungual desquamaBon in the subacute phase; (4) rash, primarily truncal; polymorphous but non-vesicular; (5) cervical adenopathy, 1.5 cenBmeters, usually unilateral lymphadenopathy.

(conBnued)


Opera9onal Deni9on: Diagnosed in a child who presents with fever (T 37.7 OC) for at least 5 days AND the presence of at least four of the ve following signs, and whose illness cannot be explained by other known disease process: (1) bilateral bulbar conjuncBval injecBon, generally non-purulent; (2) changes in the mucosa of the oropharynx, including injected pharynx, injected and / or dry ssured lips, strawberry tongue; (3) changes in the peripheral extremiBes, such as edema and / or erythema of the hands or feet in the acute phase; or periungual desquamaBon in the subacute phase; (4) rash, primarily truncal; polymorphous but non-vesicular; (5) cervical adenopathy, 1.5 cenBmeters, usually unilateral lymphadenopathy.

(conBnued)

In this example, this segment presents the conceptual deni%on of Kawasaki Disease

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(conBnued)

Opera9onal Deni9on: In the study, a child is considered to have been diagnosed with Kawasaki Disease if: (1) such diagnosis is made as a discharge diagnosis and is indicated in the childs medical records AND (2) the childs admiong clinical history is consistent with the diagnosBc criteria for Kawasaki Disease.

Categories: 0 = Without the disease; 1 = With the disease Data Collec9on: Review of discharge diagnosis AND admiong clinical history will be reviewed from medical records.


(conBnued)

Opera9onal Deni9on: In the study, a child is considered to have been diagnosed with Kawasaki Disease if: (1) such diagnosis is made as a discharge diagnosis and is indicated in the childs medical records AND (2) the childs admiong clinical history is consistent with the diagnosBc criteria for Kawasaki Disease.

Categories: 0 = Without the disease; 1 = With the disease Data Collec9on: Review of discharge diagnosis AND admiong clinical history will be reviewed from medical records.

while this segment presents the opera%onal deni%on.

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OperaBonal DeniBon (example) Variable: Crowding

Opera9onal Deni9on: Dened as the number of persons per room in the childs residence. The variable crowding will be determined by means of the quesBons: How many people live in your household? How many room divisions do you have in your house?

Categories: Enter response as is. Categories will be determined aser data collecBon is done

Data Collec9on: Face to face interview with the childs parents or caregiver

OperaBonal DeniBon (example) Variable: Age

Opera9onal Deni9on: The age of the child from the date of birth to the date of hospital admission in years, months and days.


Data Collec9on: The age will be determined through the childs birthdate based on the records and shall be computed by nding the dierence in year and months from the date of birth to the date of admission.

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OperaBonal DeniBon (example) Variable: Annual per capita household income

Opera9onal Deni9on: In the study, socio-economic status will be measured by the annual per-capita household income of the childs household in at least one preceding year. The per-capita household income will be determined by collecBng informaBon on the number of household members and the combined annual income of all earning members of the household. Earning members non-earning members of the household will be recorded


Data Collec9on: Face to face interview with the childs parents or caregiver

OperaBonal DeniBon (example) Variable: WasBng

Opera9onal Deni9on: Refers to the degree of the childs wasBng, should there be any expressed as z-scores. In the study, the z-scores will be determined using the WHO Antro and WHO Anthro Plus sosware.

Categories: 1 = Normal (z-score: 0.85 1.10) 2 = High WFH (z-score: > 0.85) 3 = Low WFH (z-score: < 1.10) 99 = No informaBon

Data Collec9on: WFH will be determined through reviewing the hospital chart of the child and obtaining the weight and height

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Comment on the operaBonal deniBon of the following:

For a research proposal that examines the relaBonship between consumpBon of dark chocolate and pre-eclampsia, the outcome variable was dened as:

A blood pressure of 140/90 obtained aUer the 20th weeks of pregnancy in a pregnant woman who had normal BP before the 20th week.


For a research proposal that examines the relaBonship between social contact factors and iniBaBon of tobacco use in adolescents, peer cigareTe smoking was dened as:

The variable peer cigare7e smoking refers to whether the respondent has at least one of his / her close friends smoke cigare7es in the preceding 6 months.

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For a research proposal that examines risk factors for household food insecurity, monthly household income was dened as:

This variable is obtained by dividing the combined monthly income of all earning members of the households by the total number of household members.

The categories of the variables are:

0 = PhP 1,403.40 1 = < PhP 1,403.40

Methods of Data Collec9on

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Methods of data collecBon

Query Face-to-face interview Self-administered quesBonnaire Guided self-administered quesBonnaire Telephone interview Computer-assisted interview

Test administraBon IQ test Knowledge quesBonnaire

Methods of data collecBon

Group processes Focus group discussion Nominal group process Delphi technique (consensus building)

ObservaBon Direct observaBon / ParBcipant observaBon Clinical and laboratory examinaBons Physical / chemical / environmental measurements Use of technology such as GIS, GPS

Review of records / documents

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Steps in developing the data collecBon tool

Specify study objecBves IdenBfy study variables OperaBonally dene study variables Specify sources of data IdenBfy method of data collecBon for each data source IdenBfy possible sources of errors in measurement

Steps in developing the data collecBon tool

IdenBfy data collecBon tool(s) to be used Prepare quesBons / data forms Format / Organize quesBons Translate and back translate

Pre-test Finalize and reproduce

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Guidelines in construcBng quesBonnaires

Sequencing / OrganizaBon Formaong Phrasing

Guidelines in construcBng quesBonnaires Sequencing / Organiza%on

Sequence quesBons that they may ow in a logical order

Suggested steps List the quesBons Group the quesBons in secBons Sequence the quesBons in each secBon Sequence the secBons

Blocks can be used in organizing ques9ons with a common theme

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ConvenBons Include date and loca9on of interview, and name of interviewer to facilitate quality control

Usually start with iden9ca9on sec9on / socio-demographic sec9on

Start with easy ques9ons (i.e. neutral / not too personal quesBons rst) For very long quesBonnaires, ask easy ques9ons rst, more dicult ques9ons in the middle, and then go back to easier ques9ons


ConvenBons Ask general ques9ons rst before specic ones Earlier quesBons should not inuence response to subsequent ques9ons

Include quesBons to cross check responses

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ConvenBons Use introduc9on or transi9on statements before starBng another block / secBon / group of quesBons ExplanaBon of what to expect in the next block ExplanaBon of the importance of ques9ons in the next block

ReiteraBon of conden9ality for sensi9ve ques9ons


ConvenBons For aotude quesBons, combine posi9vely- and nega9vely-stated statements Increases variety Decreases response set

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ConvenBons Follow natural chronological order for some variables Ask educaBon rst before occupaBon Ask whether one smokes rst before asking the number of sBcks usually smoked

Guidelines in construcBng quesBonnaires Forma_ng

Start with the iden9ca9on page Use introductory statements May parBBon the page into two columns one column for quesBons, and the other column for answers

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Guidelines in construcBng quesBonnaires Forma_ng

Recording of responses Use check boxes or numbers to be encircled for pre-coded or open-ended quesBons

Provide ample space for answers to open-ended quesBons

Provide skipping instruc9ons and other instrucBons for interviewers

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Phrasing the quesBons Clear

Entails awareness of the level of understanding or educaBon of respondents

Do not use technical terms or complex words or phrases. Use short, simple, and direct ques9ons Do not use ambiguous ques9ons Do not use words that are prone to mis-pronouncia9ons

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Phrasing the quesBons Clear

Avoid double-barreled ques9ons When two quesBons are implied in what is meant to be one.

Example: Do you agree or disagree that AIDS can be transmi7ed through hand shaking or through other forms of physical contact?

Phrasing the quesBons Unbiased

The quesBons should not inuence the way the respondents will answer

QuesBons must not reveal the person / group of the study if it can lead the respondent to answer in a parBcular way.

QuesBons should not favor one side of the idea. QuesBons should not lead the respondent to give answers when they are not qualied to do so.

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Phrasing the quesBons Tacbul

QuesBons should not embarrass the respondent especially: If the topic is sensiBve If quesBons assume that the respondent has knowledge on the issue when he / she has none

Phrasing the quesBons Adequate

The quesBonnaire should include all the necessary informa9on to allow accurate administraBon, and recording of responses Necessary explanatory material are present Pre-coded responses are mutually exclusive and exhausBve

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Phrasing the quesBons Neither loaded nor leading

Loaded ques9on: emoBonally charged or has a false or disputed presupposiBon Example: Why did you engage in pre-marital sex?

Leading ques9on: gives the respondent an idea of desired response Example: Because AIDS is transmissible, do you think pa%ents living with AIDS should inform their sexual partners rst of the infec%on status?

Open-ended quesBons

The respondents are free to give whatever answer/s he / she nds relevant

The respondents can phrase answer in his / her own words / responses

Examples In your opinion, how is leptospirosis transmi7ed? What is your primary considera%on when purchasing food items for your children?

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Open-ended quesBons

ADVANTAGES

SBmulate free thought, solicit sugges9ons, probe peoples account of events, clarify posi9ons

Used in exploratory / qualita9ve studies

Can be used in pilot studies to guide the development of close-ended quesBons

DISADVANTAGES Entails much recall and organiza9on of thought

Probing is necessary Unsuitable for SAQ Dicult to record responses Dicult to process and analyze

Longer interview 9me

Close-ended quesBons

Answers or choices are already provided to the respondents.

May involve raBng or ranking of responses May involve asking whether the respondent agrees or disagrees with a statement

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Close-ended quesBons

ADVANTAGES

Easy to analyze Can easily measure levels or degrees of a given variable

DISADVANTAGES

Choices are limited to pre-coded responses thus there is a risk of missing important dimensions of what the respondent knows, believes in, or feels about

RaBng scales

Can be used as an alternaBve to close-ended quesBons to elicit a graded response.

Example: On a scale of 0 to 10, where 0 indicates complete disagreement and 10 indicates complete agreement, please rate you degree of agreement or disagreement on mandatory HIV tes%ng to all incoming medical students?

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Pre-tesBng

Conduct of the data collec9on process to 20-50 respondents that have characteris9cs similar to the study popula9on (i.e. for knowledge and aotude scales: 5 respondents per quesBon)

Things to look for High number of dont know responses or items les blank Incomplete quesBonnaires Inconsistent answers Most responses fall under one category Responses are irrelevant to the study

References

Borja M (2012). Lecture slides on data collecBon. UP CPH DEBS

Daniel W & Cross C (2013). BiostaBsBcs: A FoundaBon for Analysis in the Health Sciences. John Wiley & Sons, Inc.

Kumar R (2011). Research Methodology: A Step-by-Step Guide for Beginners. Sage: London

Mendoza O et al (2010). FoundaBons of StaBsBcal Analysis for the Health Sciences. Department of Epidemiology & BiostaBsBcs, College of Public Health, University of the Philippines Manila: Manila

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Thank You J

s4 - measuring variables & data collection [dr a bermudez]

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