Session 2- Data Collection & Analysis- Questionnaire Design PDF

**Session 2- Data collection & analysis** **Questionnaire design** ***[The steps for an efficient decision-marking ]*** 1. Decide wheter **existing information** is adequate or whether **additional information** is required 2. If necessary, **collect** more information in a reasonable and...

**Session 2- Data collection & analysis** **Questionnaire design** ***[The steps for an efficient decision-marking ]*** 1. Decide wheter **existing information** is adequate or whether **additional information** is required 2. If necessary, **collect** more information in a reasonable and thoughful way 3. **Summarize the available data** in a useful and informative maner 4. **Analys**e the available data 5. Draw **conclusions,** make **decisions a**nd assess the risk of an incorrect decision A blue and white rectangle with white text Description automatically generated 1. **Methods of data collection** ***[Data collection ]***  ***[Example]** of primary data* 1\] field studies / 2\] experiment / 3\] Online data Field studies Experiments ------------------------ -------------------------- -------------------------- Independent variable X Measured Controlled/manipulated Moderating variable Z Measured Controlled / Manipulated Advantage Association/ correlation Causality Advantage Reflects reality Lack of reality Experiments : it's change only one data to see the differents *Thomke S. and Manzi J. (2014), The discipline of Business Experimentation, Harvard Business Review, December.* ***[Types of experiments : Between vs Within ]*** a. **Definition :** **BETWEEN-SUBJECTS** designs: Independent samples (participants are randomly assigned to different conditions) **WITHIN-SUBJECTS** designs: Paired samples (the same participant tests all conditions) b. **How it works** -Between ( diff people for two question ) / within ( same people for two questions) Obejctive : compare two differents paire How it works ( betwen you separte / within you don't ) : c. **Rationale for each design type :** Between-subjects design Within-subjects design --------------------------------------------------------------------------- ------------------------------------------------------------------------- 1\. At least **one control** group and one **experimental group** differ 1\. Researchers test the **same participants repeatedly** to assess on a variable differences between conditions. 2\. Every experimental group is given **an independent variable treatment 2\. There are no control groups in within-subjects designs because that the researcher believes will have some effect on the outcomes**, participants are tested before and after independent variable while control groups are given no treatment or a fake treatment treatments. 3\. **You compare the dependent variable measures between groups** to see 3\. **The pretest is similar to a control condition** where no whether the independent variable manipulation is effective**. If the independent variable treatment is given yet, while **the posttest takes groups differ significantly**, **you can conclude th**at **your place after all treatments are administered.** independent variable manipulation likely caused the differences.** When we use a panel we use a within Panel : ask to participate to the studie at different time Sample : you take a random population and you ask the same qquestion d. **Pros and cons for each design type :** +-----------------------+-----------------------+-----------------------+ | | Between-subject | Within-subjects | | | design | design | +=======================+=======================+=======================+ | Pros | 1\. **Fewer threats | 1**. Fewer | | | to internal | participants** | | | validity\*** | compared to between- | | | because it prevents | subjects design. | | | carryover effects | | | | | 2\. **No Individual | | | (i.e., being in one | differences** | | | experimental | across treatments | | | condition on a | | | | subsequent condition) | | | | | | | | 2\. Shorter | | | | durationPrevents | | | | **fatigue effects** | | +-----------------------+-----------------------+-----------------------+ | cons | 1\. Requires **more | 1\. Longer duration | | | participants** | **Fatigue effects** | | | | | | | 2\. **Individual | 2\. **Hawthorne | | | differences** may | effect** (changing | | | threaten validity | one's behavior) | | | | Threats to | | | | **internal | | | | validity** | +-----------------------+-----------------------+-----------------------+ e. **Confound effect.** When we want to do an experience it's important to not change the « image » of the product for example a bootle elongation because it's can change the perceiption of the quality. 2. **Designing a questionnaire** f. **STEP 1 : information** What information will be sought? What information do you want from the survey ? The hypotheses determine what information will be sought and from whom, because they specify what information relationships will be investigated. **What are the questions i want my survey to get answers to write every single question !!** **For the quetsionnaire :** **-** Write all the question you have on mind \- Identitify the variables \- And you write the good question (1 variable for each question)  g. **STEP 2 : Administration** What method of administration to use ? After specifying the basic information that will be sougt, you need to specify how it will be gathered h. **STEP 3 : conte** ***[VARIABLES : ]*** **IV :** Before/ after **DV :** Consumer experience &reactions (**individual variables) (DEPENDENT VARIABLES)** **MEDIATING :** Experiences **=/** **MODERATING :** Samples **Variable that explain the Variable that changes it affects the** ***[EXAMPLE :]*** Mediator variable ***[EXAMPLE :]*** Mediator variable [What content ? what from ? what wording ? what sequence ? ] ✓ Is the question necessary ? Check your model ! ✓ Are several questions needed instead of one ? ***[(mistakes) Avoid writing your own questions]*** *For complex variables, use existing questions/scales from literature* *Look for scales in articles OR in scales handbooks ("Handbook of Marketing Scales", Bearden et al., 2012*) ***[ASKING THE QUESTIONS PROPERLY?]*** Do respondents have the necessary information? Can the respondent answer? Will respondents give the information? Even though they have the information, there's always a question if they will share it. *[(mistakes) Social desirability bias] is a type of response bias that is the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others Examples: politics, drugs use, sex habits, \...* Once the content of the individual questions is determined, you need to decide on the particular form of the response. - Open-ended questions - Multichotomous question - Dichotomous questions - Scales ***[Open-ended question]*** : respondents are **free to reply to open-ended questions in their own words** rather than being limited to choosing from a set of alternatives. ( not madatory question) ❑How old are you? ***[Multichotomous question]*** : it's a fixed-alternative question ; respondents are asked to **choose the alternative that most closely corresponds to their position on the subject.** How old are you ?  Less than 20  20-29  30-39  40-49  50-59  60 or over How many long-distance telephone calls do you make in a typical week?  Fewer than 5  5-10  More than 10 ***[Dichotomous question]*** : it's also a fixed-alternative question but one in which **there are only two alternatives.** « Do you intend to purchase an automobile this year » ? ❑ Yes ❑ No Very useful when you have to use « dummy variables » in a regression analysis ***[Scales]*** : it's another fixed alternative question. **2 kinds of scales** : the **Likert-type scale** and the **Osgood-type scale.** When responding to a Likert question, respondents specify their level of agreement to a statement. Often five ordered response levels are used, although many psychometricians advocate using seven or nine levels. Please circle the number that represents the extent to which you agree with the statement: I totally I disagree(1) totally agree (7) I'm satisfied with the computer software I've 1 2 3 4 5 6 7 been using Involves the **phrasing of each question** Critical : poor phrasing of a question will cause respondents to skip it and not answer it, or they'll answer the question incorrectly. - Use **simple** words - **Avoid ambiguous words** and questions : For instance, « do you personaly drink wine ? » wording: incorrect what about people who only drink during festivities. you must ask about the frequency.   - **Avoid implicit** assumptions - **Avoid generalizations** and estimates - **Avoid double-barreled** questions - **Be careful with the age range.** People's behavior to drinking wine is not the same when 18 or 29 ask what age they are open question - **Avoid asking question with many variable.** As postif answer as negatif - **Don't ask about the impact** Some people don't know what impat them **Never ask question about the effect.** - **Don't tell respondent to rank option ( not interesting for study)** - Example : in wich aspect do you think our store is more appealing rank them. - Instead rate each factor from 1-5, instead of ranking - What is your level of wine knowledge ? - Don't put beginer/intermediate/ advanced - **Instead ask 5 questions and with your analyses , you will know if the respondent has knowledge** - Be **accurate** in your answers - If you ask to rate from 1 to 5 alway say what does **1 and 5 represent** - How to measure experience - **Calculate the curerage** - If you want to include multiple questions to conclude one answer male all **the question have the same type of answer** like (1-5) or (yes-no) ***[BIASES DUE TO POOR PHRASING : ]*** It's necessary to be totaly honest when we ask something and tell all the important element for instance we ask « will tou marry me » but we must ask « are you really sure you want to spend all your days, nights, week-ends and holidays with me, while you could have much more fun doing something else with someone else? » ***[IT'S MEASUREMENT BIAS :]*** When the method of observation tends to produce values that systematically differ from the actual value in some way\ →for example with the use of an improperly calibrated scale, or when questions on a survey are worded in a way that tends to influence the response i. **STEP 4 : design ** Physical characteristics of the questionnaire ✓ **Acceptance** of the questionnaire ***[THEN SEQUENCING THE QUESTION : ]*** - Make the questionnaire reflect the **importance** of the study → Avoid sloppy questionnaires. - **The introduction** is also important. The cover letter introduces the study, it must convince the respondents to cooperate. ✓ Questionnaire **length** is important: smaller questionnaires are better than larger ones. j. **STEP 5 : pretest** - **Examine each word** of every question to ensure the question is not confusing, ambiguous, offensive, or leading. - **Get peer evaluations** of the draft questionnaire. ***[FINALLY,PRETEST AND REVISE ]*** Use personal interviews among respondents similar to those to be used in the actual study**, and obtain comments to discover any proble**ms with the questionnaire Eliminate questions that do not provide adequate information **and revise questions that cause problems.** 3. **Sampling and biases** k. **Sample =/ population**  l. **Zize doesn't matter** **Common misconception that** if the size of a sample is small compared to the population size, the sample can't possibly accurately reflect the population. **Probability samples :**Every member of the population has a known and equal chance of being selected. ***[NONPROBABILITY SAMPLES :]*** - **[Convenience sample]** : the researcher selects the most accessible population members from whom to obtain information - **[Quota sample]** : the researcher interviews a prescribed number of people in each of several categories (ex: 50 women and 50 men) m. **Biases** **Selection Bias** : when the way the sample is selected some part of the population of interest. For instance when your research focuses on wine lovers whilst your sample involves non-drinkers\... **Non-response bias :** When respondents who choose not to respond may be inherently different than the population n. **Coding the data**