Marketing Research for Managers: Final Exam Study Guide
What is the major difference between cross tabulation and frequency distribution?
Frequency distribution: Describes one variable at a time. Cross tabulation: Describes two or more variables simultaneously.
What is a one sample t-test used for in marketing research?
In marketing research, the researcher is often interested in making statements about a single variable against a known or given standard.
What is the product moment correlation coefficient?
In situations like this, Product moment Correlation, r, is the most widely used statistic because it summarizes the strength of association between two metric (interval or ratio scaled) variables. Product moment correlation r, indicates the degree to which the variation in one variable, X, is related to the variation in another variable, Y. This is also known as the Pearson correlation coefficient.
What are incidence rates and how do they affect sample size?
Incidence Rates: The rates of occurrence or the percentage of person eligible to participate in the study and determine the numbers of contacts which need to be screened for a given sample size requirement.
How is multiple regression different from bivariate regression?
Multiple regression: It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). Bivariate Regression: A procedure for deriving a mathematical relationship, in the form of an equation, between a single metric dependent variable and a single metric independent variable.
What qualitative factors should be considered in determining sample size?
mean, proportion, variance, standard deviation, and size
Define spurious correlation.
Two variable cross-tabulations may show an association between the variables. However, introduction of a third variable in the cross-tabulation might reveal that there is no correlation between the two variables despite the observed initial association. Such correlations are called spurious correlations.
What strategies are available to adjust for nonresponse?
- Subsampling of Nonrespondents - Replacement - Substitution - Subjective Estimates - Trend Analysis - Simple Weighting - Imputation
How is the strength of association measured in bivariate regression? In multiple regression?
Bivariate: Multiple:
What is the two independent sample t-test used for?
- Several hypotheses in marketing relate to parameters from two different populations - Use this analysis to determine whether the means of two independent groups differ
In a pretest, data on Nike were obtained from 45 respondents. These data are given in the following table, which gives the usage, sex, awareness, attitude, preference, intention, and loyalty toward Nike of a sample of Nike users. Usage has been coded as 1, 2, or 3, representing light, medium, or heavy users. The sex has been coded as 1 for females and 2 for males. Awareness, attitude, preference, intention, and loyalty are measure on 7-point Likert-type scales (1=very unfavorable, 7 = very favorable). Note that five respondents have missing values that are denoted by 9.
(TABLE)
Provide an example hypothesis for a one sample t-test.
- The mean disappointment with Uber pricing will be higher than 4 - The mean willingness to hire UCI MBA graduates will be higher than 4 - The mean of the perception of the importance of convenience of location for a restaurant will be higher than 4
Provide an example hypothesis using a two independent sample t-test.
- The users and non-users of mobile payment service devices differ in terms of their "age" - The mean importance rating for a "college proximity to home" is higher or those that are residents of California vs. not from California - The academic reputation for Brandman University is higher than the academic reputation for Chapman - Males are more likely than females to eat at Chipotle
Describe and give examples of each non-probability sampling techniques.
1. Convenience sampling: Exploratory research ex: - Use of students, and members of social organizations - Mall intercept interviews without qualifying the respondents - Department stores using charge account lists - "People on the street" interviews 2. Judgmental sampling: ex: - Test markets selected to determine the potential of a new product - Bellwether precincts in voting behavior - Choice of expert witnesses in a court case - Selection of department stores to test a new merchandising display system 3. Quota sampling: Mall intercept interviews 4. Snowball sampling: Interviews with unclassified populations & interviews with very small, disbursed populations
Describe and give examples of probability sampling techniques.
1. Simple random sampling: Not widely used 2. Systematic sampling: Consumer mall interviews, Telephone interviews, Mall intercept interviews 3. Stratified sampling: - A two-step process in which the population is partitioned into subpopulations, or strata. - The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted. - Next, elements are selected from each stratum by a random procedure, usually SRS. - A major objective of stratified sampling is to increase precision without increasing cost. - The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible - The stratification variables should also be closely related to the characteristic of interest. - Finally, the variables should decrease the cost of the stratification process by being easy to measure and apply. ex: Comprehensive, Cost-effective sampling 4. Cluster sampling: Area sampling, Cost-constrained personal interviews
Why use adjusted R-Square versus simply R-Square?
Adjusted r squared is typically quoted more than R-squared because it is more conservative AND whenever you add another variable the adjusted R-squared is only increased by the incremental increase in explanation that the variable adds. R square will always increase when you add a new variable.
What is the major difference between judgmental and convenience sampling?
Convenience Sampling: Attempts to obtain a sample of convenient elements. Respondents are selected because they happen to be at the right place at the right time. Judgmental Sampling: Form of convenience sampling in where population elements are selected based on judgment of the researcher
Discuss the advantages and disadvantages of each non-probability sampling technique.
Convenience: (A) Least expensive, least time-consuming; sampling units are accessible, cooperative; and easy to measure (DA) many potential sources of bias are present; not representative of any population; not able to generalize findings Judgmental: (A) low cost; convenient to use; less time-consuming than most techniques (DA) no direct generalizations to a specified population; results entirely dependent on the judgment of the researcher Quota: (A) Lower cost in selecting elements for each quota; greater convenience in selecting elements for each quota; with tight controls on interviewers and interviewing, results can be compared to probability techniques (DA) No assurance that a sample is representative; increasing controls decreases the ease of conducting the interviews; many sources of bias can be present due to selection process Snowball: (A) Good at locating and estimating various characteristics that are rare in population; low sample variance and costs when used (DA) Difficulty in obtaining the initial sample; limited to studies of a particular sample
What is the cross tabulation test used for?
Cross tabulation is a statistical tool that is used to analyze categorical data. Displaying a distribution of cases by their values on two or more variables is known as contingency table analysis and is one of the more commonly used analytic methods in the social sciences.
What is the major difference between a parametric and non-parametric test?
Parametric tests: Appropriate when the variables are measured on an interval scale. Non-parametric tests: Appropriate when the variables are measured on a nominal or ordinal scale, also can be used if the data is interval.
Differentiate probability and non-probability sampling techniques.
Probability Sampling techniques: Selected by chance Non-probability: Doesn't use chance selection procedures, relies on personal judgment of the researcher
Discuss the reasons for the frequent use of cross tabulations. What are some of its limitations?
Reasons: - Ease of comprehension - Versatility - Clarity - Simplicity Limitations: Cross-tabulation, though meant for describing the joint distribution of two or more variables, is seldom used in computations involving more than three variables↓ - Interpretation can become complex - Also, since the number of cells increases multiplicatively, maintaining an adequate number of respondents in each cell becomes problematic. a. Consequently, the statistics computed could be unreliable. - Besides, since only two or three variables are tabulated at a time, cross-tabulation is not a very efficient way of examining the relationships when there are several variables.
What are the main uses of regression analysis?
Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. Regression analysis can be used in the following ways: - Determine whether the independent variables explain a significant variation in the dependent variable: whether a relationship exists. - Determine how much of the variation in the dependent variable can be explained by the independent variables: strength of the relationship. - Determine the structure or form of the relationship: the mathematical equation relating the independent and dependent variables. - Predict the values of the dependent variable. - Control for other independent variables when evaluating the contributions of a specific variable or set of variables. Regression analysis is concerned with the nature and degree of association between variables and does not imply or assume any causality.
Under what conditions would a sample be preferable to a census? A census to a sample?
Sample is preferable (to census) when: - Cost constraints prohibit full sampling - Time available is short - The population size is large - The variance of the characteristics is small - The cost of sampling error is low - The cost of non-sampling is high - The nature of measurement is destructive - Attention must be given to individual cases Census is preferable (to sample) when: - Cost constraints are minimal - Time available is long - The population size is small - The variance of the characteristic is large - The cost of sampling error is high - The cost of non-sampling error is low - The nature of measurement is non-destructive - Attention need not be given to individual cases
Discuss the advantages and disadvantages of each probability sampling technique.
Simple Random Sampling: (A) It is easily understood (DA) Sample results may be projected to the target population Systematic Sampling: (A) Potential of increased representativeness of the sample vs. SRS; random sampling is done only once; can be performed even when knowledge of the composition of the sampling frame doesn't exist (DA) Ordering of elements is critical. Improper ordering may decrease the representativeness of the sample Stratified Sampling: (A) Increases the precision of sampling by controlling sources of sample variation; ensures all important subpopulations are represented in the sample (DA) Disproportionate sampling requires estimates of the population variance to compute strata size; extra care must be taken in creating the strata to control for sampling variation Cluster Sampling: (A) Cost-effective sampling method; a sampling frame is needed only for those cluster selected in the sample (DA) Results in samples that have lower precision; generally difficult to form heterogenous clusters
What is adjusted R-Square used for?
The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model.
