Metric Final Study Guide

O check if the missing data item is actually required

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o Check if the missing data item is actually required to meet your research goals. If not, you might consider putting aside the problem for now. o Consider if this particular respondent is missing many data points. If so you might decide that exclusion is best. Imputing data- means filling in a value when there is missing data. Imputation needs to be well documented (keep those notes thorough on your “reason” sheet), and can be executed only in certain circumstances. Here are some examples: (1) Sometimes you can impute data based on information gathered from other items for this respondent that are very similar in nature. This is best if possible. (2) Sometimes you can impute data based on average score for this question from all participants. (3) This is preferable to item (2). Sometimes you can impute data based on average from respondents who are similar in some key dimension. Let’s say you interested in differences in satisfaction with new and returning retail customers. You are missing a satisfaction rating from a returning retail customer. You could average the satisfaction ratings from only returning customers (excluding new customers), and impute that average rather than the average for the whole sample. Statistical Modeling: Predictive Value- •Allows us to understand relationships between constructs.

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•Allows us to control for aspects that co-vary/ or overlap in their occurrences. •Allows us to check how much different factors contribute to an outcome. •Allows us to see if one factor works through another in an observed effect •Allows us to understand the conditions under which certain relationships occur. Linear regression can simply show the relationship between 2 variables. Very much like correlation does. Multi-Linear Regression modeling- new product creativity as the degree to which a new product is perceived to be novel and novel in a manner that is meaningful to target customers. o Modeling allows us to understand relationships between constructs o Modeling allows us to control for aspects that co-vary/or overlap in their occurrences. o Compute the unique contribution of each factor o Modeling allows us to check how much different factors contribute to an outcome Mediation Modeling- allows us to see how predicting factors work through a mediating factor to get to the outcome Multiple Mediation Modeling- allows us to see how predicting factors work through more than 1 mediating Moderation Modeling- allows us see if certain conditions must be met to see a relationship Serial Mediation Modeling- shows us multiple processes that happen in succession. Customer Indexing- is a way to represent customer share within a market, used in targeting consumers. o Formula is (% of category consumption/ % of population)* 100 o For example Age 18 - 34 is 38% of population, and they are 28% of your category’s consumption. (28/38)*100 = 73.68. This group indexes at 73.68.
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• Spring '18
• Danny Weathers
• new product development, researcher, Operationalization Concretization

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