ANOVA in Marketing Research

ANOVA IN MARKETING RESEARCH BY: VIVEK GOYAL DATA ANALYSIS WORKSHOP ON SPSS. (LPU)

MARKETING RESEARCH MARKETING RESEARCH IS GENERALLY ACTIVATED WHEN THERE IS A QUESTION TO BE ANSWERED , ONLY WHEN THERE IS A INFORMATION GAP. MARKETING RESEARCH IS GENERALLY TAKEN UP AS A SHORT-TERM PROJECT WITH CLEARLY DEFINED SCHEDULED,BUDGET, WHICH SHOULD MAKE MARKETING DECISION –MAKING. WHO DOES THE RESEARCH? IT IS A INTERMITTENT ACTIVITY, BASED ON A IDENTITICAL INFORMATION GAP OR INFORMATION NEED. A LARGE NUMBER OF PROFESSIONAL RESEARCH ARE AVIALABLE TO DO THE NEEDFUL. THEY HAVE BRANCHES ( IN CASE OF INDIA) ALL OVER THE COUNTRY AND SOME HAVE GLOBAL ASSOCIATES IF RESEARCH IS GOING BEYOND INDIA.

WHEN TO DO MARKETING RESEARCH ,[object Object]

THE COST FILLING GAP THROUGH MARKETING RESEARCH IS LESS THAN TAKING WRONG DECISION WITHOUT DOING RESEARCH.

THE TIME TAKEN FOR THE RESEARCH DOES NOT DELAY DECISION –MAKING BEYOND RESONABLE LIMITS.,[object Object]

EXPERIMENTAL DESIGN THE DESIGN OF EXPERIMENT IS MOST CRITICAL WHEN IT IS ANALYSED THROUGH ANOVA. THE TYPES OF DESIGN USE IN MARKETING RESEARCH EXPERIMENT ARE: ,[object Object],THIS DESIGN IS USED WHEN THREE IS ONLY ONE CATEGORY OF INDEPENDENT VARIABLE AND ONE DEPENDENT VARIABLE. EACH CATEGORY OF INDEPENDENT VARIABLE IS CALLED THE LEVEL. IN THIS TYPE OF DESIGN WE RANDOMLY ALLOCATE VARIOUS SAMPLE TO DIFFERENT LEVEL. THEN WE CONDUCT F-TEST UNDER ANOVA TO TEST THE NULL HYPOTHESIS THAT THE MEAN VALUE OF INDEPENDENT VARIABLE.

[object Object],IT IS USED IF THERE IS ADDITIONAL VARIABLE {CALLED BLOCK} WHICH HAS A IMPACT ON RELATIONSHIP BETWEEN INDEPENDENT VARIABLE AND DEPENDENT VARIABLE. ,[object Object],WHEN TWO OR MORE VARIABLE DESIGN ARE TESTED THROUGH ANOVA THEN WE USE FACTORIAL DESIGN.

USE OF ANOVA INMARKETING RESEARCH ANOVA uses the sample that are randomly selected from the population. The population should have equal variance. ANOVA uses squared deviation of variance so that computation of distance of individual data point from their own mean or from grand mean can be summed ( i.e S.D sum to zero). The total deviation of any particular data point may be partitioned into – BETWEEN GROUP VARIANCE and WITHIN GROUP VARIANCE.

EXAMPLE: THE EXPERIMENT CONDUCTED ON ’20’ RANDOMLY SELECTED RESPONDENT (ICE-CREAM): PRICE OF 1 KG.PACK CODE Rs. 100.00 1 Rs.110.00 2 Rs.130.00 3 Rs.140.00 4 ICE-CREAM FLAVOURS CODE STRAWBERRY 1 VANILLA 2 CHOCOLATE 3 PISTA 4

OUTPUT OF SPSS OF ICE-CREAM RANDOMISED DESIGN ONE WAY ANOVA ANOVA ( FLAVOURS AND PRICE) Sum of Squares df Mean Square F Sig. FLAVOURS Between Groups 20.000 17 1.176 .471 .850 Within Groups 5.000 2 2.500 Total 25.000 19 PRICE Between Groups 26.700 17 1.571 6.282 .146 Within Groups .500 2 .250 Total 27.200 19

ANOVA( SALES AND FLAVOURS) Sum of Squares df Mean Square F Sig. SALES Between Groups 147616.875 3 49205.65 212.011 .000 Within Groups 65546.875 16 4096.680 Total 213163.750 19 FLAVOURS Between Groups .000 3 .000 .000 1.000 Within Groups 25.000 16 1.563 Total 25.000 19

ANOVA ( PRICE*SALES) Sum of Squares df Mean Square F Sig. PRICE Between Groups .000 3 .000 .000 1.000 Within Groups 27.200 16 1.700 Total 27.200 19 SALES Between Groups 11463.750 3 3821.250 .303 .823 Within Groups 201700.000 16 12606.250 Total 213163.750 19

SALES*FLAVOURS OF ICE-CREAM Crosstab Count FLAVOURS 1 2 3 4 Total SALES 900 0 0 0 1 1 930 0 0 1 0 1 1000 1 1 0 0 2 1020 0 0 0 1 1 1025 0 1 0 0 1 1050 1 0 0 0 1 1075 1 0 0 0 1 1090 0 0 0 1 1 1100 0 0 1 0 1 1110 0 0 0 1 1 1125 0 0 1 0 1 1150 0 1 0 0 1 1175 0 1 0 0 1 1180 0 0 1 0 1 1200 1 0 0 1 2 1220 0 1 0 0 1 1225 0 0 1 0 1 1310 1 0 0 0 1 Total 5 5 5 5 20

Chi-Square Tests( SALES*FLAVOURS) Value dfAsymp. Sig. (2-sided) Pearson Chi-Square 52.000(a) 51 .435 Likelihood Ratio 49.900 51 .517 Linear-by-Linear Association .813 1 .367 N of Valid Cases 20 a 72 cells (100.0%) have expected count less than 5. The minimum expected count is .25.

SALES*PRICE OF ICE-CREAM Crosstab Count PRICE 1 2 3 4 Total SALES 900 0 0 0 1 1 930 0 0 0 1 1 1000 0 0 0 2 2 1020 0 0 1 0 1 1025 0 0 1 0 1 1050 1 0 0 0 1 1075 0 0 1 0 1 1090 1 0 0 0 1 1100 0 0 1 0 1 1110 0 1 0 0 1 1125 1 0 0 0 1 1150 0 1 0 0 1 1175 1 0 0 0 1 1180 0 1 0 0 1 1200 1 1 0 0 2 1220 1 0 0 0 1 1225 1 0 0 0 1 1310 1 0 0 0 1 Total 8 4 4 4 20

Chi-Square Tests(PRICE*SALES) Value dfAsymp. Sig. (2-sided) Pearson Chi-Square 56.250(a) 51 .285 Likelihood Ratio 50.515 51 .493 Linear-by-Linear Association 12.232 1 .000 N of Valid Cases 20 a 72 cells (100.0%) have expected count less than 5. The minimum expected count is .20.

Chi-Square Tests(PRICE*FLAVOURS) Value dfAsymp. Sig. (2-sided) Pearson Chi-Square .000(a) 9 1.000 Likelihood Ratio .000 9 1.000 Linear-by-Linear Association .000 1 1.000 N of Valid Cases 20 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is 1.00.

DESCRIPTIVE Descriptive Statistics N Minimum Maximum Mean Std. Deviation Variance Statistic StatisticStatisticStatistic Std. Error Statistic Statistic SALES 20 900 1310 1104.25 23.685 105.920 11219.14 FLAVOURS 20 1 4 2.50 .256 1.147 1.316 PRICE 20 1 4 2.20 .268 1.196 1.432 Valid N (listwise) 20

REFERENCE ,[object Object],[object Object]

ANOVA in Marketing Research

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ANOVA in Marketing Research