15-16 Marzo 2012

Come raccogliere i dati

Cosimo De Nunzio Department of Urology, Ospedale Sant’Andrea

University “La Sapienza”, Roma, Italycosimodenunzio@virgilio.it

15-16 Marzo 2012

After a researcher defines the things, phenomena, or variables to be studied, a problem and hypothesis are formulated.

The next step is for the researcher to determine how the variables or things being studies must be measured, observed, or recorded

Appropriate data collection is essential to the validity of a study

Data collection

Data collection is a term used to describe a process of

preparing and collecting data,

The purpose of data collection is to obtain information to

keep on record, to make decisions about important issues,

to pass information on to others.

Primarily, data are collected to provide information

regarding a specific topic.

Data collection: definition

Wikipedia, 2012

Data collection plan

Pre collection activity: agree on goals, target data, definitions, methods

Collection: data collection

Presenting findings: usually involves some form of sorting analysis and or presentation

Methods of Data collectionQualitative

– typically involves qualitative data, i.e., data obtained through methods such interviews, on-site observations, and focus groups that is in narrative rather than numerical form

Quantitative– use numerical and statistical processes to answer specific

questions. Statistics are used in a variety of ways to support inquiry or program assessment/evaluation.

Methods of Data collection Qualitative data collection

– they tend to be open-ended and have less structured

protocols (i.e., researchers may change the data collection

strategy by adding, refining, or dropping techniques or

informants)

– they rely more heavily on interactive interviews;

respondents may be interviewed several times to follow up

on a particular issue, clarify concepts or check the

reliability of data

Methods of Data collection Qualitative data collection

– findings are not generalizable to any specific population– Data collection in a qualitative study takes a great deal

of time. – The researcher needs to record any potentially useful

data – The qualitative methods most commonly used in

evaluation can be classified in:  • in-depth interview • observation methods • document review

Methods of Data collection

Quantitative data collection

– They produce results that are easy to summarize,

compare, and generalize. 

– Participants may be randomly assigned to different

treatments.

– Collect data on participant and situational characteristics

in order to statistically control for their influence on the

dependent, or outcome, variable.

Methods of Data collection Quantitative data collection

– To generalize from the research participants to a larger population, the researcher will employ probability sampling to select participants.

– Typical quantitative data gathering strategies include: • Experiments/clinical trials. • Observing and recording well-defined events (e.g., counting the

number of patients waiting in emergency at specified times of the day).

• Obtaining relevant data from management information systems. • Questionnaires• Administering surveys with closed-ended questions (e.g., face-to

face and telephone interviews, questionnaires etc).

Methods of Data collection: type of the study

Census: A census is a study that obtains data from every member of a population. In most studies, a census is not practical, because of the cost and/or time required.

Sample survey. A sample survey is a study that obtains data from a subset of a population, in order to estimate population attributes.

Experiment. An experiment is a controlled study in which the researcher attempts to understand cause-and-effect relationships. The study is "controlled" in the sense that the researcher controls (1) how subjects are assigned to groups and (2) which treatments each group receives.

Observational study. Like experiments, observational studies attempt to understand cause-and-effect relationships. However, unlike experiments, the researcher is not able to control (1) how subjects are assigned to groups and/or (2) which treatments each group receives.

Methods of Data collectionPros and cons

Resources. When the population is large, a sample survey has a big resource advantage over a census. A well-designed sample survey can provide very precise estimates of population parameters - quicker, cheaper, and with less manpower than a census.

Generalizability. Generalizability refers to the appropriateness of applying findings from a study to a larger population. Generalizability requires random selection. If participants in a study are randomly selected from a larger population, it is appropriate to generalize study results to the larger population; if not, it is not appropriate to generalize. Observational studies do not feature random selection; so it is not appropriate to generalize from the results of an observational study to a larger population.

Causal inference. Cause-and-effect relationships can be teased out when subjects are randomly assigned to groups. Therefore, experiments, which allow the researcher to control assignment of subjects to treatment groups, are the best method for investigating causal relationships.

Where do data come from?

Take a step back – if we’re starting from baseline, how do we collect / find data?

– Secondary data• data someone else has collected

– Primary data• data you collect

Secondary Data: Sources

County health departmentsVital Statistics – birth, death certificatesHospital, clinic, school nurse recordsPrivate and foundation databasesCity and county governmentsSurveillance data from state government

programsFederal agency statistics - Census, NIH, etc.

Secondary Data: Limitations

When was it collected? For how long?– May be out of date for what you want to analyze.– May not have been collected long enough for detecting

trends.

Is the data set complete?– There may be missing information on some observations– Unless such missing information is caught and corrected for,

analysis will be biased.

Secondary Data: Limitations

Are there confounding problems?– Sample selection bias?– Source choice bias?– In time series, did some observations drop out over time?

Are the data consistent/reliable?– Did variables drop out over time?– Did variables change in definition over time?

Is the information exactly what you need?– In some cases, may have to use “proxy variables” – variables

that may approximate something you really wanted to measure.– Are they reliable? – Is there correlation to what you actually want to measure?

Secondary Data – Advantages

No need to reinvent the wheel.– If someone has found the data, take advantage of it.

It will save you money.– Even if you have to pay for access, often it is cheaper in terms of

money than collecting your own data. It will save you time.

– Primary data collection is very time consuming. It may be very accurate.

– When especially a government agency has collected the data. It has great exploratory value

– Exploring research questions and formulating hypothesis to test.

Primary Data - Examples

Surveys Focus groups Questionnaires Diaries Personal interviews Biophysiologic Measures (in vivo/in vitro) Experiments and observational study

QuestionnairesAdvantages: Can be posted, e-mailed or faxed with a wide geographic coverage Can cover a large number of people or organisations. Relatively cheap. Avoids embarrassment on the part of the respondent. Possible anonymity of respondent. No interviewer bias. Disadvantages: Design problems. Questions have to be relatively simple. Historically low response rate (although inducements may help). Time delay whilst waiting for responses to be returned. Require a return deadline. Several reminders may be required. International valididity Not possible to give assistance if required. Problems with incomplete questionnaires. Respondent can read all questions beforehand and then decide whether to complete or

not.

Personal Interviews(structured; semistructured; unstructured)

Advantages: Serious approach by respondent resulting in accurate information. Good response rate. Complete and immediate. Interviewer in control and can give help if there is a problem. Can investigate motives and feelings. Can use recording equipment. If one interviewer used, uniformity of approach. Used to pilot other methods. Disadvantages: Need to set up interviews, time consuming and geographic limitations. Can be expensive. Normally need a set of questions. Respondent bias – tendency to please or impress, create false personal image, or end

interview quickly. Embarrassment possible if personal questions. If many interviewers, training required.

Phone interviewsAdvantages: Relatively cheap and quick. Can cover reasonably large numbers of people or organisations. Wide geographic coverage. High response rate. Help can be given to the respondent. Can tape answers. Disadvantages: Questionnaire required. Not everyone has a telephone. Repeat calls are inevitable – average 2.5 calls to get someone. Time is wasted. Respondent has little time to think. Cannot use visual aids. Can cause irritation. Good telephone manner is required.

Primary Data - Limitations

Do you have the time and money for:– Designing your collection instrument?– Selecting your population or sample?– Administration of the instrument?– Entry/collection of data?

Uniqueness– May not be able to compare to other populations

Researcher error– Sample bias– Other confounding factors

Data collection: Take home message

Data collection is essential for study validity

Medical clinical and experimental research is mostly based

on quantitative method of data collection

Will the data answer my research question?

If that data exist in secondary form, then use them to the

extent you can, keeping in mind limitations.

But if it does not, and you are able to fund primary collection,

then it is the method of choice.

Urocampus 2011, PCa working group

Thank you !