GIÁO TRÌNH MARKETING NGHIÊN CỨU - PHẦN 18 pptx

Determining and Interpreting Associations Among Variables Ch 18 2 Associative Analyses • Associative analyses: determine where stable relationships exist between two variables • Examples – What methods of doing business are associated with level of customer satisfaction? – What demographic variables are associated with repeat buying of Brand A? – Is type of sales training associated with sales performance of sales representatives? – Are purchase intention scores of a new product associated with actual sales of the product? Ch 18 3 Relationships Between Two Variables • Relationship: a consistent, systematic linkage between the levels or labels for two variables • “Levels” refers to the characteristics of description for interval or ratio scales…the level of temperature, etc. • “Labels” refers to the characteristics of description for nominal or ordinal scales, buyers v. non-buyers, etc. • As we shall see, this concept is important in understanding the type of relationship… Ch 18 4 Relationships Between Two Variables • Nonmonotonic: two variables are associated, but only in a very general sense; don’t know “direction” of relationship, but we do know that the presence (or absence) of one variable is associated with the presence (or absence) of another. • At the presence of breakfast, we shall have the presence of orders for coffee. • At the presence of lunch, we shall have the absence of orders for coffee. Ch 18 5 Nonmonotonic Relationship Ch 18 6 Relationships Between Two Variables • Monotonic: the general direction of a relationship between two variables is known – Increasing – Decreasing • Shoe store managers know that there is an association between the age of a child and shoe size. The older a child, the larger the shoe size. The direction is increasing, though we only know general direction, not actual size. Ch 18 7 Monotonic Increasing Relationship Ch 18 8 Relationships Between Two Variables • Linear: “straight-line” association between two variables • Here knowledge of one variable will yield knowledge of another variable • “100 customers produce $500 in revenue at Jack-in-the-Box” (p. 525) Ch 18 9 Relationships Between Two Variables • Curvilinear: some smooth curve pattern describes the association • Example: Research shows that job satisfaction is high when one first starts to work for a company but goes down after a few years and then back up after workers have been with the same company for many years. This would be a U-shaped relationship. Ch 18 10 Characterizing Relationships Between Variables 1. Presence: whether any systematic relationship exists between two variables of interest 2. Direction: whether the relationship is positive or negative 3. Strength of association: how strong the relationship is: strong? moderate? weak? • Assess relationships in the order shown above. [...]... under examination Ch 18 23 Chi-Square Analysis • Computed Chi-Square values: Ch 18 24 Chi-Square Analysis • The chi-square distribution’s shape changes depending on the number of degrees of freedom • The computed chi-square value is compared to a table value to determine statistical significance Ch 18 25 Chi-Square Analysis • How do I interpret a Chi-square result? Ch 18 – The chi-square analysis yields... cross-tabulation tables in your text, pages 52 8-5 31 Ch 18 12 Cross-Tabulations Ch 18 13 Cross-Tabulations • When we have two nominal-scaled variables and we want to know if they are associated, we use crosstabulations to examine the relationship and the Chi-Square test to test for presence of a systematic relationship • In this situation: two variables, both with nominal scales, we are testing Ch 18. .. appear again and again if we sampled other students? • We use the Chi-Square test to tell us if nonmonotonic relationships are really present Ch 18 21 Cross-Tabulations • Using SPSS, commands are ANALYZE, DESCRIPTIVE STATISTICS, CROSSTABS and within the CROSSTABS dialog box, STATISTICS, CHI-SQUARE Ch 18 22 Chi-Square Analysis • Chi-square analysis: assesses nonmonotonic associations in crosstabulation... association…we have the PRESENCE of a systematic 26 relationship between the two variables Chi-Square Analysis Chi- Square Test s Pearson Chi- Square Continuity Correction a Likelihood Ratio Fisher's Exact Test N of Valid Cases df 1 1 1 Exact Sig ( 2- sided) Exact Sig ( 1- sided) 000 Value 39.382b 35.865 34.970 Asymp Sig ( 2- sided) 000 000 000 000 100 a Computed only for a 2x2 table b 0 cells (.0%) have expected... The minimum expected count is 5.06 • Read the P value (Asympt Sig) across from Pearson Chi-Square Since the P value is . pages 52 8-5 31. Ch 18 13 Cross-Tabulations Ch 18 14 Cross-Tabulations • When we have two nominal-scaled variables and we want to know if they are associated, we use cross- tabulations to examine. above. Ch 18 11 Cross-Tabulations • Cross-tabulation: consists of rows and columns defined by the categories classifying each variable…used for nonmonotonic relationships • Cross-tabulation. Ch 18 12 Cross-Tabulations • Using SPSS, commands are ANALYZE, DESCRIPTIVE STATISTICS, CROSSTABS • You will find a detailed discussion of cross-tabulation tables in your text, pages 52 8-5 31.