New User Special Price Expires in

Let's log you in.

Sign in with Facebook


Don't have a StudySoup account? Create one here!


Create a StudySoup account

Be part of our community, it's free to join!

Sign up with Facebook


Create your account
By creating an account you agree to StudySoup's terms and conditions and privacy policy

Already have a StudySoup account? Login here

Marketing Research Exam 2

by: Talia Standring

Marketing Research Exam 2 MKTG 632

Marketplace > San Francisco State University > Marketing > MKTG 632 > Marketing Research Exam 2
Talia Standring
GPA 3.49

Preview These Notes for FREE

Get a free preview of these Notes, just enter your email below.

Unlock Preview
Unlock Preview

Preview these materials now for free

Why put in your email? Get access to more of this material and other relevant free materials for your school

View Preview

About this Document

scales, design, data collection, probability, samples, sampling
Marketing Research
Study Guide
Marketing, research, Sampling, data
50 ?




Popular in Marketing Research

Popular in Marketing

This 5 page Study Guide was uploaded by Talia Standring on Tuesday March 22, 2016. The Study Guide belongs to MKTG 632 at San Francisco State University taught by in Fall 2015. Since its upload, it has received 42 views. For similar materials see Marketing Research in Marketing at San Francisco State University.


Reviews for Marketing Research Exam 2


Report this Material


What is Karma?


Karma is the currency of StudySoup.

You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 03/22/16
 MKTG632  Study Guide for Exam II  Measurement Scales  • Should know the attitude scales discussed in class  Questionnaire Design  • Should be aware that you need to clearly state the research objectives before designing the  questionnaire.  Step 1: consider the research objectives Step 2: specify the appropriate questions that should be in your survey       The Likert scale requires the respondents to indicate a degree of agreement or  disagreement w/ each of a serious of statements about the stimulus objects. Example: Survey  that says strongly disagree, disagree, etc.    The Semantic Differential scale is a seven­point rating scale with end points associated  with bipolar labels that have semantic meaning. (think opposite)    Example: Powerful – Weak; Unreliable ­ Reliable • Should know the difference between open­ ended and multiple­choice questions.  Open­ended question: where the respondent is allowed to answer in their own words.  The  purpose of these questions is to obtain more detailed information, especially regarding  respondent’s opinions and views on particular subjects.  Takes a lot of effort to analyze  information and works best in smaller populations.   Advantage: you can get full information and a more honest answer Disadvantage: a lot of data to narrow down; no criteria; broad Example: Please describe, in as much detail as possible, your typical morning routine. (Provides more useful data for a company trying to understand how its consumers get ready and where  their product can fit into that routine). Multiple­choice questions: a form of question where the individual being interviewed may only  answer from a pre­determined list. • Should be able to identify “wrongly worded” questions that we discussed in class and how to  reword them.  “Wrongly worded” questions: convey a different meaning than what was sought to be conveyed, wrong data will be collected through responses to such questions. ex. badly worded: How short was ___? correctly worded: How would you describe ___’s height? Be clear about what you are asking. Leading Questions: they already lead you towards an answer ex. Weren’t you happy with the service last night at Holiday Inn? Double­Barreled Question: the question has too many components, so the survey taker could  agree with one part of the statement but not the other, which would not be accurate • Should know the ideal sequence in a questionnaire.  Screening Example: Did you shop at Macy’s in the past 3 months? Warm­ups Example: How often do you shop in Macy’s? Main Questions (easy to difficult) Example: How important is each of the following factors selecting a department store? Complicated Example: Please rate Macy’s customer service on the following various aspects. Demographics Examples: What is your age? Sampling Design  • Should know the difference between sampling and census and why we do sampling as  opposed to conducting a census study.  Census: Data collection from or about every member of the population of interest.  Also called  canvassing the population by asking everyone a set of questions. Sample: A subset of all the member of a population of interest. Administrative Sampling: choosing with bias Random Sampling: choosing randomly Non­Sampling Error: interviewer errors, measurement errors, response errors As sample size increases (amount of people),  random sampling errors decrease (because you  get a better representation of the population), but the non­sampling errors increase (due to more room for human error) • Should know what a sample frame means and what is meant by frame error.  Sample frame: master list of all sample units in the population. Example: telephone book, mailing list ex. Telephone book,  • Given a sample frame, should be able to identify the sources of frame error.  Sources of Sample Frame Error:  occurs when the wrong sub­population is used to select a sample ex. people surveyed who have telephones were mostly republican when the majority of  americans at the time didn’t even have phones; incorrect conclusion that majority of America is  republican Target Population Vs. Sample Frame: Buyers vs. lists • Should know the differences between the various sampling methods and why and when they  are used.  Nonprobability vs. Probability • Should know the difference between probability and non­probability sampling.  Nonprobability sampling techniques: rely on personal judgment in the element selection  process.  Prohibits estimating the probability that any element will be included in the sample Pros: less cost, less time, can make samples of population that are reasonably representative,  good for research for which accuracy isn’t very important. Convenience Samples: you choose places of convenience that have a high traffic of potential  respondents or people who are easily accessible.  This is the simplest and most convenient way of sampling. Judgement Samples: based on your knowledge, choose a sample of people that would  represent the population of interest. Snowball Samples: an initial group of respondents is selected, usually at random.  Subsequent  respondents are selected based on referrals by this initial group.  This is generally used while  investigating characteristics that are rare in the population.   Quota sampling: may be viewed as two­stage restricted judgmental sampling. Quota sample is  based on personal judgement. (personal judgement) Probability sampling: members of the population have a known chance of being selected in the  sample Simple Random Sampling: every element has an equal probability of being selected into the  sample.   The probability of selection = sample size / population size Systematic Sampling:  Skip Interval = population size / sample size Every 200 people for example Cluster Sampling: heterogeneity; more cost effective; selecting a sample of subgroups and then  collecting data from all or a sample of the elements in the subgroup; when not every group is  represented and different from each other (not a good representation of the area because it is  only one group for example, only selecting people from the Marina of SF when choosing an SF  group) Step 1: Divide population into subgroups called clusters, which represent the entire population Step 2: Randomly select some clusters Step 3: Select members of each chosen clusters to be included in the sample. Examples: amazon prime emails Subgroups: email service provider; last names If used for SF cities, the different areas don’t repersent SF well. (RANDOM IN SUB GROUP) Stratified Sampling: homogenous; selecting a sample of elements from each subgroup; offers  better precision since it gets more representative from every sub group (SAME IN SUB  GROUP) Step 1: Divide population into 2 or more homogenous subsets (mutually exclusive and  collectively exhaustive) Step 2: Select a probability sample from each stratum Example: Groups: (AAA) / (BBB) / (CCC) — Similarity only between subgroups.  We could  select representatives from each subgroup. Suppose secondary data tell you that the usage habits of instant video services among  households with kids is different from that among households without kids.  In general,  households with kids tend to use less instant video services.  Your secondary data also tells you that roughly 35% of households have kids and the remaining  households do not have kids in  the target population. Q:   If you want to have a sample size of 1000, how many households with kids would you  survey?  A: 350 = .35 * 1000 Problems: don’t have the means to distinguish the subgroups; can’t identify the bases for  stratification Sample Size Determination  • Should know that the sample size determination techniques discussed apply only for  probability sampling and that too only for simple random sampling.  Similar opinions in a sample = smaller sample size (amazon example), don’t need as much  representation Proportional Allocation (stratified sampling): same fraction of strata Disproportional Allocation (stratified sampling): various fractions of strata Ad Hoc Methods: Rule of thumb: assume that each member of  the sample was independently and  randomly selected. (at least 100 cases) Budget Constraints: Buy only what you can afford Comparable Studies: use past studies to determine sample size • Should know the various factors that affect the sample size required and how they affect it.  (confidence level, accuracy, and variability) 1.The higher the confidence level, the higher the sample size (confidence level = how certain  you think something is) 2.The higher the accuracy, the higher the size. 3.The higher the variability, the higher the size. • Should know that there are two cases and should know when to use the two formulae.  CASE A: nominal scales (percentage/proportion) p = estimated percentage (proportion) q = 100­p if p is in percent    = 1­p if p is in proportion z = level of confidence (from a normal table) z = 1.96 for 95% confidence     2.58 for 99% confidence     1.65 for 90% confidence e = acceptable sample error If P is unknown, use 50% because it is the most conservative estimate CASE B: for data that involve interval or ratio scales n = required sample size z = level of confidence (from a normal table) z = 1.96 for 95% confidence     2.58 for 99% confidence     1.65 for 90% confidence e = acceptable error s = variability indicated by estimated standard deviation of the population • Should know what to do when the variability (or proportion) of the population is not known.  Variability = P(1­P) Higher the number, the larger the variability • Should be able to determine the sample size required under a given situation.  The following z values and formulae will be provided on the test (NO explanations about the  symbols) “z” values:  Formulas:  Z (90%) = 1.65  Z (95%) = 1.96  Z (99%) = 2.58  n=z^2(pq)/e^2 n=z^2s^/e^2


Buy Material

Are you sure you want to buy this material for

50 Karma

Buy Material

BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.


You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

Why people love StudySoup

Jim McGreen Ohio University

"Knowing I can count on the Elite Notetaker in my class allows me to focus on what the professor is saying instead of just scribbling notes the whole time and falling behind."

Anthony Lee UC Santa Barbara

"I bought an awesome study guide, which helped me get an A in my Math 34B class this quarter!"

Jim McGreen Ohio University

"Knowing I can count on the Elite Notetaker in my class allows me to focus on what the professor is saying instead of just scribbling notes the whole time and falling behind."

Parker Thompson 500 Startups

"It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

Become an Elite Notetaker and start selling your notes online!

Refund Policy


All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email


StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here:

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.