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Marketing Research

Exam 2 Dr Case

QuestionAnswer
Descriptive Analysis Used by researchers to describe the sample dataset in such a way as to portray the typical respondent and to reveal the general pattern of responses.
Inference Analysis Used when marketing researchers use statistical procedures to generalize the results of the sample to the target population it represents.
Difference Analysis Used to determine the degree to which real and generalizeable differences exist in the population to help the manager make an enlightened decision on which adv theme to use.
Association Analysis Investigates if and how two variables are related. If they are related, how do you describe the relationship?
Predictive Analysis Statistical procedures and models to help make forecasts about future events.
Variability Measures that describe how similar (dissimilar) respondents or responses are to (from) "typical" respondents or responses.
Central Tendency Measures that describe the "typical" respondents or response.
Measures of Central Tendency Mode, Median, Mean.
Measures of Variability Frequency distribution, Range, Standard Deviation.
Frequency Distribution A tabulation of the number of times that each different value appears in a particular set of values.
Range Identifies the distance between lowest value and the highest value in an ordered set of values.
Standard Deviation Indicates the degree of variation or diversity in the values in such a way as to be translatable into a normal or bell-shaped curve distribution.
Which descriptive statistic to use with a Nominal scale? Mode
Which descriptive statistic to use with an Ordinal scale? Median
Which descriptive statistic to use with an Interval scale? Mean
Which descriptive statistic to use with a Ratio scale? Mean
Parameter Estimation The process of using sample information to compute an interval that describes the range of the parameter such as the population mean or the population percentage.
Parameter estimation involves three values: The sample statistics, the standard error of the statistic, and the desired level of confidence.
Sample Statistics Values that are computed from information provided by a sample. Usually a mean or a percentage.
Parameters Values that are computed from a complete census, which are considered to be precise and valid measures of the population.
Inference A form of logic in which you make general statement about an entire class based on what you have observed about a small set of members of that class.
Statistical Inference A set of procedures in which the sample size and sample statistic are used to make an estimate of the corresponding population parameter.
Two types of statistical inferences: 1. Parameter estimate 2. Hypothesis Testing
Hypothesis Testing Used to compare the sample statistic with what is believed to be the population value prior to undertaking the study.
Standard Error The measure of variability in the sampling distribution.
Confidence Interval The degree of accuracy desired by the researcher state in the form of a range with an upper and lower boundary.
Categorical Scale -Nominal and ordinal scales -Statistic is expressed as a percent. - Formula P ± z √pq/N -Mostly use mode, unless doing level of education then use median.
Metric Scale -Interval and Ratio scales. -Statistic is expressed as a mean. SPSS, one sample T test.
Market Segmentaion Based on differences between groups of consumers.
One common basis for Market Segmentation is the discovery of differences that are the following: -Statistically significant. -Meaningful: Different enough to be meaningful. -Stable. -Actionable differences.
Differences must be Statistically Significant: The differences found in the sample truly exist in the population(s) from which the random samples are drawn.
Differences must be Meaningful: One that the marketing manager can potentially use as a basis for marketing decisions.
Differences must be Stable: One that will be in place for the foreseeable future.
Differences must be Actionable: The marketer can focus various marketing strategies and tactics, such as product design or advertising, on the market segments to accentuate the differences between segments.
Testing for significant differences between two groups: Statistical tests are used when researchers want to compare means or percentages of two different groups or samples.
T Test Statistical inference test to be used with small sample sizes (n<= 30). Only do when a statistic is expressed as a mean, NEVER a -percent.
Z Test Statistical inference test to be used when the sample size is 30 or greater.
Independent Samples Treated as representing two potentially different populations. -Two groups one question.
Null Hypothesis The hypothesis that the difference in the population parameters is equal to zero.
In a Differences Test: The null hypothesis stats that there is no difference between the percentages (or means) being compared.
Significance of Differences between two percentages: Alternate to the null hypothesis is that there is a true difference between population parameters.
Formula for significance of the difference between two percentages: z= (P1-P2)/(Sp1-Sp2) p1 = % found in sample 1 p2 = % found in sample 2 Sp1-Sp2 = standard error of the difference between two percentages.
How to know the results are significant: If the H0 is true, we would expect no differences b/w the 2 percentages. In any given study, differences are expected due to sampling errors. If the H0 were true, we'd expect 95% of the z scores to fall b/w +1.96 and -1.96 stnrd errors.
Analysis of Variance (ANOVA) Used when comparing means of three or more groups. Its an investigation of the differences between the group of means to ascertain whether sampling errors or true population difference explain their failure to be equal.
Basic of ANOVA: ANOVA will "flag" when at least one pair of means has statistically significant difference, but does not tell which pair.
ANOVA Advantages Immediately notifies researcher if there is any significant difference. Arranges the means so the significant differences can be located and interpreted easily.
Paired Samples test for the difference of two means: A test to determine if two means of two different questions using the same scale format and answered by the same respondents in the samples are significantly different.
Group Comparison Table Summarizes the significant difference in an efficient manner.
Post Hoc Tests Options that are available to determine where the pair(s) of statistically significant difference between the means exist(s).
Associative Analyses Determine where stable relationships exist between two variables.
Relationship between two variables Consistent, systematic linkage between the levels of label for 2 variables.
Levels Interval or Ratio scales.
Labels Nominal or Ordinal Scales.
Nonmonotonic Relationship Two variables are associated but only very general sense. Presence of one variable is associated with the presence of another.
Monotonic Relationship General directions of relationships is known. -Increasing/decreasing relationships.
Linear Relationship "straight-linear association" between two variables.
Linear Relationship Formula: y=a+bx Where: Y= the dependent variable being estimated. A= The intercept. B= The slope. X= the independent variable used to predict the dependent variable.
Curvilinear Relationship Some smooth curve pattern describes the association.
Characterizing Relationships between Variables: Presence, direction, and strength of association.
Presence Whether any systematic (statistical) relationship exists between two variables.
Direction (pattern) whether the relationship is positive or negative.
Strength of Association Whether the relationship is consistent.
Cross-Tabulation Rows and columns defined by the categories classifying each variable; used for nonmonotonic relationships.
Cross-Tabulation Cell The intersection of a row and column.
Four types of numbers in each cell of a Cross-Tabulation Table: -Frequency. -Raw percentage. -Column percentage. -Row percentage.
Cross Tabulation Table: Frequencies are the raw numbers in the cell. Raw percentages are cell frequencies divided by the grand total. Row percentages are the row cell frequencies divided by its row total. Column %'s are the column cell frequencies divided by its column total.
Chi-Square Analysis The examination of frequencies for two nominal-scaled variables in a cross-tabulation table to determine whether the variables have a significant relationship.
Chi-Square Analysis Basics: Assesses non-monotonic association in a cross-tabulation table based upon difference between observed and expected frequencies.
How to interpret a Chi Square Result: The calculated value is compared to the table value to determine significance. SPSS compares calculated to table values and show the probability for support of the H0. A significant result means the researcher should look at the CT row and column %s .
Chi-Square Distribution Is skewed to the right , and the rejection region is always at the right-hand tail of the distribution. the shape of the distribution is dependent on the degrees of freedom.
Correlation Coefficient An index number, constrained to fall between the range of -1 and +1. Communicates both the strength and the direction of the linear relationship between two metric variables.
Covariation The amount change in one variable systematically associated with a change in another variable. Can be examined with the use of a scatter diagram.
Created by: KAzetapi
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