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 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]

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ANOVA in Marketing Research

  • 1. ANOVA IN MARKETING RESEARCH BY: VIVEK GOYAL DATA ANALYSIS WORKSHOP ON SPSS. (LPU)
  • 2. 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.
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  • 4. THE COST FILLING GAP THROUGH MARKETING RESEARCH IS LESS THAN TAKING WRONG DECISION WITHOUT DOING RESEARCH.
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  • 8. 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.
  • 9. 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
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  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. 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.
  • 17. 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
  • 18. 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.
  • 19. 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.
  • 20. 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
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