Session 2 Updates and HW (Mohali)

Hi all,

Session Recap:
Session 2 got done today. We ventured into psychometric scaling and attempted to measure complex constructs using the Likert scale, among others. We also embarked on a common-sensical approach to survey design.

There were some Qs that "jump ahead" in the sense that I hope to cover them in Session 3 - Questionnaire Design. And Qs that seem to want to "force" a 'right' answer in a multiple choice context. Well, one issue with a lot of MKTR is that it is context-sensitive, so its hard to proclaim 'right' answers that will hold true in general. "It depends" is usally a better bet. Wherever possible I do try to point out general principles and frameworks but in many cases, the problem context decides whether something is true or not.

For example, "inferring scale reliability via analysis of demographic profiles" raised quite a few Qs [PsyScaling Q7]. Well, there are product categories where demographics alone can explain enough to evaluate scale relibility on their basis. But in an increasing number of product categories, demographics do not explain product choice very much. In such instances, its hard to conclude definitely about scale reliability on their basis. At least, that was the limited point, I was making.

Study Group Formation details:

• Since there's no project, there's no project group. However, a number of homework activities are group-based, hence pls form HW or study groups.
• Regarding group formation, pls send an email (only one per group) to the AA in the format prescribed.
• To help in group formation, pls find the list of all final registrants for MKTR_146 on LMS.
• If you are unable to find group partners and would like to be allotted to a group, let the AA know.
• Choose a well-known brand as your group name and write your group name next to your PGID and name in the spreadsheet.
• Pls complete the group formation exercise well before the start of session 3.

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Pls read *any 2* of the articles from the business press below. The following 2 HWs are based on the above articles.

• (a) The appeal of 3D movies can lead to a survey estimating preference for -> willingness to pay for -> likely demand for movies in the 3D format among a particular target segment. Here's the management problem sourced from the Economist (July 2011) The appeal of 3D movies - Cinema's great hope
• (b) Here's a desi innovation that might get a huge fillip in demand as demographics start to favor it. Demand soars for a "House-call doctor services" for the elderly and the chronically infirm. Source is Economic times, 2012.
• (c) This Economic times article from a year ago talks about a recent phenomenon they call 'showrooming'.Consumers spot deals in stores, close them online; showrooming threatens to make life difficult for electronics retailers.
• (d) Here's an interesting possibility that requires folks to look at reeeally new products - akin to forecasting email's effect on postal services in 1994 - the impact on small-scale manufacturing of 3D printing services. This too is sourced from the Economist (Dec 2011).
• (e) Here's an interview with the boss of the cafe coffee day chain and he describes some interesting looking initiatives CCD is taking in trying to leverage facebook and other social media to provide speedy feedback on CCD Ops nationwide etc.

HW Part 1: Reducing an M.P. to a D.P. to an R.O.

For each article,

• Q.1.1. write a short description of what a mgmt problem (M.P.) may look like.
• Q.1.2. Write one D.P. corresponding to the M.P.
• Q.1.3. Write an example or two of R.O.s that correspond to the D.P.

HW Part 2: Construct Analysis

• Q.2.1. List a few major constructs you find (if any) in each of the two articles that are of MKTR interest.
• Q.2.2. Pick any one construct you have listed in Q.2.1. and break it down into a few aspects.
• Q.2.3. Make a table with 2 columns. In the first column, write the names of the aspects you came up with. In the second column, corresponding to each aspect, write a Likert statement that you might use in a Survey Questionnaire to measure that aspect.

Session 2 HW submission format:

• Use a plain white blank PPT.
• On the title slide, write your name and PGID.
• For slide headers, use format "HW1: [Article name]" (and so on for the next article chosen)
• Pls mention clearly the Question numbers you are solving in the slide body. Use fresh slides for each new article
• Use a blank slide to separate HW2 from HW1.
• Save the slide deck as session2HW_yourname.ppt and put in in the dropbox on LMS before the start of session 4.

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Session 2 HW part 3 - Survey filling

Pls complete the following two surveys latest by sunday (01-Dec) midnight. I reckon it'll take you max 15 minutes on each survey.

Note: Pls answer as truthfully as you can. The data will be used to illustrate MKTR tools and R's analysis prowess in the classroom. It will *not* be shared with anybody outside the classroom.

Survey 1 (for JSM and perceptual mapping)

Survey 2 (Standard psychographic Personality profile questionnaire)

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Session 3 Preview:
In Session 3 we cover two broad topics. For the first, we continue the "principles of survey design" part and wade into Questionnaire Design Proper. Be sure to read the pre-read on Questionnaire Design as it covers the basics. It'll thus help lighten my load considerably in class. And who knows if there's another pre-reads quiz lurking somewhere in Session 3 as well...

For the second broad topic, we do Data Reduction via Factor Analysis. For this we'll need R. Two ways to get R:

(i) The easy way is to copy the .exe files for both R and Rstudio putup by Ankit on LMS. First install R by clicking on the .exe file and following instructions. Then repeat the same for Rstudio.exe.

(ii) the second way ios to directly download and install R from the following CRAN link for both the Windows and Mac versions:

Download and Install R

Download and install Rstudio (only *after R has been installed)

If you have any trouble with R installation, contact IT and let me know. Watch this space for more updates.
That's it for now. See you in the next class.
Sudhir