Exam #3 - Chps. 9 - 13 Flashcards
What are the steps in the data preparation process?
VECT DE DC
Validation
Editing
Coding
Transcribing/Data Entry
Data Cleaning
In the VALIDATION step of the data preparation process, what questions can be answered?
- Did people fill out the survey correctly?
- Did people fill out the survey more than once?
- Did they consent?
- Did the preview verify that they’re part of the targeted study group?
In the EDITING step of the data preparation process, what questions can be answered?
- Was all of the information collected?
- Did the respondent follow all of the skip pattern steps correctly?
- Are responses to open-ended questions clear?
What things count as survey mistakes?
- questions unanswered
- questions not filled out properly
- open-ended questions
- unclear responses
How can you deal with “bad” surveys?
- return to the field (recontact respondents) NOT RECOMMENDED
- assigning missing values
- discarding unsatisfactory respondents
The process of assigning numerical values and codes to the various responses to a particular question is…
coding
True or False: close-ended questions are easy to code.
True
True or False: open-ended questions are easy to code.
False
The process of physically entering numerical values into the computer is…
data entry
What is considered ‘intelligent’ data entry?
When the system is set up so you can’t mess up circling, for example.
What is considered ‘dumb’ data entry?
Excel, for example, where you manually enter the data and it doesn’t tell you if it’s wrong.
What are considered univariate data analysis techniques?
metric data
nonmetric data
What are considered multivariate data analysis techniques?
dependence techniques (regression)
interdependence techniques (factor analysis)
The trustworthiness of qualitative analysis depends on the…
rigor of the process used for collecting and analyzing the data.
What are the steps of data analysis?
- data reduction
- data display
- conclusion drawing / verification
For data analysis, what do you do in data reduction?
find the themes, code them and categorize the data according to those themes
For data analysis, what do you do in data display?
put the data into tables or figures to reduce, summarize and convey major ideas
For data analysis, what do you do in conclusion drawing / verification?
draw insights and relationships from the data
What are examples of biases you must take into consideration when drawing conclusions from the data?
- selectivity -> leads to overconfidence in the data
- co-occurrences -> misconstrued as correlations or causal relationships
- extrapolating the rate of instances in the population from those being observed
- some sources may be unreliable
What is a statistic?
a sample measurement of a population parameter
Which variables are considered ‘metric?’
interval and ratio
(they’re continuous)
(you can compute sample means, standard deviation, etc.)
Which variables are considered ‘nonmetric?’
nominal and ordinal
(they’re not continuous)
(they’re categorical!)
What is the main goal in analyzing the data?
to be able to generalize sample results to the population
Which variables are continuous?
metric.
interval and ratio.
Which variables are categorical?
nonmetric.
nominal and ordinal.
List the steps in the hypothesis testing process.
- determine Ha and Ho.
- determine type of test (t,Z,chisquared).
- determine level of significance (alpha).
- compute the test statistic.
- compute the critical value.
- compare the test statistic and the critical value.
- reject or retain the null.
- state the answer in terms of the marketing research problem.
The test of means is for what kind of variable?
continuous variables
The test of proportions is for what kind of variable?
categorical variables
This kind of analysis has to do with evaluating ONE survey question.
univariate
This kind of analysis has to do with evaluation TWO+ survey questions.
multivariate
What are the univariate data analysis techniques?
metric and non-metric
What are the multivariate data analysis techniques?
dependence and interdependence
This data analysis technique measures the strength of a linear association between two metric (continuous) variables.
correlation
This data analysis technique analyzes a metric independent variable and a metric dependent variable.
regression
This data analysis technique determines if one variable depends on or influences another.
regression
This data analysis technique analyzes a set of metric independent variables and a non-metric dependent variable.
discriminant analysis
This data analysis technique groups objects or people in regard to certain variables.
cluster analysis
This data analysis technique takes the info contained in a larger set of metric variables and summarizes it into a smaller set of summary factors.
factor analysis
This data analysis technique determines the ‘ideal’ combination of attributes and levels for a new product.
conjoint analysis
Goals: degree of association, direction of association
These are the goals for…
correlation
Goals: measure influence (explanation), predict values for dependent variable
These are the goals for…
regression
Goals: classify people into groups based on their values of the independent variables
These are the goals for…
discriminant analysis
Goals: those within a group are similar and those outside a group are different
These are the goals for…
cluster analysis
Goals: simplify data, reduce a set of variables
These are the goals for…
factor analysis
Goals: tradeoffs among attributes and level of attributes are examined to understand what consumers prefer
These are the goals for…
conjoint analysis
“Are quality perceptions related to price perceptions? And if so, in what direction?”
This is an example question for…
correlation
Why does correlation not necessarily equal causation?
In order for causation to occur, you must have all three:
- correlation
- temporal sequence
- non-spurious association
“Do price perceptions influence quality perceptions?”
This is an example question for…
regression
“What factors influence choice?”
This is an example question for…
discriminant analysis
Choice models are used for…
discriminant analysis
Segmentation is another word for…
cluster analysis
Perceptual maps are used for…
factor analysis
“What is the ideal combination of chocolate type, filling, and amount of nuts for a new candy bar?”
This is an example question for…
conjoint analysis
Why should you caution when using a conjoint analysis?
adding attributes / levels exponentially increases the product combinations evaluated
(think of the chocolate bar example!)
What are the three types of readers of a final report?
- those who only read the executive summary
- those who read the executive summary and the body of findings
- those who read the entire report and appendix
Amongst presentation barriers, what is selective perception?
When the client hears what they want to hear as a conclusion of the data instead of what is actually true.
Why is research always needed?
consumer wants and needs are always changing
