OCR Nationals Level 3 Unit 3

March 2012 M Morison Know the different types of errors that can affect a study Understand why it is necessary to identify potential errors and constraints Be able to identify and describe some of the constraints and areas for potential errors in your own study, and identify ways in which you can prevent these from affecting your study

March 2012 M Morison A02 - Design and Carry Out a data collection activityMonday April 2nd

March 2012 M Morison Pass Candidates will list some of the constraints that could affect the reliability of their study. Merit Candidates will describe most of the constraints that could affect the reliability of their study and identify some areas of potential error in their sampling regime. Distinction Candidates will describe the constraints that could affect the reliability of their study and identify areas of potential error in their sampling regime. 4

March 2012 M Morison  No matter reliable your sample size is or how good your data collection method is, there is always the potential for errors to occur.  There are several different types of errors:  Sampling errors  Design errors  Human errors  Respondent errors

March 2012 M Morison  The sample selected may be too small to get a reliable enough range of data;  It may be too small to be truly representative;  An inappropriate sampling method may have been used;

March 2012 M Morison  You may need to expand your sample and collect more data.  You may need to begin your data collection again using a different method.

March 2012 M Morison  Generally decreases as the sample size increases (but not proportionally)  Depends on the size of the population under study  Depends on the variability of the characteristic of interest in the population  Can be accounted for and reduced by an appropriate sampling plan

March 2012 M Morison  If there is an error in the questionnaire design, this could cause problems with the respondent's answers  If you ask the wrong questions, you won’t get the data you require  This in turn, can create processing errors  Processing errors can also arise through incorrect calculations /functions/formulae within your spreadsheet  These types of errors often lead to a bias in the final results.

March 2012 M Morison  Pilot your questionnaire to make sure it returns the data you are looking for  Give exemplar formats for answers – make it clear that when you ask for gender you are expecting M or F, for example  Test your spreadsheet out with test data first and check your answers against a calculator to make sure your functions and formulae are working correctly

March 2012 M Morison  People make mistakes!  Questions could be asked incorrectly  Answers could be recorded incorrectly  The person collecting the data might select the wrong people /inappropriate people to interview

March 2012 M Morison  Pilot your questionnaire to make sure it returns the data you are looking for  Give exemplar formats for answers – make it clear that when you ask for gender you are expecting M or F, for example  Thorough training for anyone who is collecting data on your behalf

March 2012 M Morison Poor questionnaire design  It is essential that sample survey or census questions are worded carefully in order to avoid introducing bias.  If questions are misleading or confusing, then the responses may end up being distorted. For more information, refer to this section on Questionnaire design. Questionnaire design

March 2012 M Morison Interview bias  An interviewer can influence how a respondent answers the survey questions.  This may occur when the interviewer is too friendly or too aloof, or prompts the respondent.  To prevent this, interviewers must be trained to remain neutral throughout the interview. They must also pay close attention to the way they ask each question. If an interviewer changes the way a question is worded, it may impact the respondent's answer.

March 2012 M Morison  Respondents can provide incorrect answers.  Faulty recollections, tendencies to exaggerate or underplay events, and inclinations to give answers that appear more 'socially desirable' are several reasons why a respondent may provide a false answer.

March 2012 M Morison  Non-response errors are the result of not having obtained sufficient answers to survey questions. There are two types of non-response errors: complete and partial.

March 2012 M Morison Complete non-response errors  The results fail to include the responses of certain units in the selected sample.  the respondent is unavailable or temporarily absent  the respondent is unable or refuses to participate in the survey  If a significant number of people do not respond to a survey, then the results may be biased since the characteristics of the non- respondents may differ from those who have participated.

March 2012 M Morison  This type of error occurs when respondents provide incomplete information.  For certain people, some questions may be difficult to understand.  To reduce this form of bias, care should be taken in designing and testing questionnaires.

March 2012 M Morison  The most common non-response error is caused by people simply not returning the questionnaire.  You may need to increase your proposed sample size to cover the people who may not respond.  Another common cause is people not understanding the questions.  This can be avoided by including a help sheet with the questionnaire, or comments on the questionnaire itself.

March 2012 M Morison To ensure the reliability of your data, there are some questions you need to ask yourself:  What is the source of the data? Did you collect it yourself, or was it collected by somebody else on your behalf?  Could the subjects have a reason for misrepresenting the information/ answering incorrectly?  When you are entering your data into your spreadsheet, is it possible that the data might have been altered by anybody in any way?  Do you think the sample size was adequate? What is the level of sampling error?  Were the survey questions easy to understand?

March 2012 M Morison  List potential errors that could affect results  How can you minimise these?