CHAPTER-III
METHODOLOGY
The purpose of this chapter is to present the design of the study that was used to find out solution to the research problem by means of answering all the research questions. Design is the process of making decisions before a situation arises in which the decision has to be carried out. It is a process of deliberate anticipation directed towards bringing unexpected situation under control.
This chapter addresses the methodological procedure adopted for the present investigation.
This chapter includes
the description of the research design (Research Design)
the research Model(Conceptual Framework)
the description of the selection of subjects (sampling)
the measurement instruments used in this study (instrument and scaling)
the reliability and validity analysis(Reliability and Validity)
the procedure for the distribution of the questionnaire and collection of data (method of data collection)
the description of the statistical tools used for data analysis. (statistical tools)
Researcher has used descriptive research design for conducting the research. Descriptive research studies are those studies which are concerned with describing the characteristics of a particular individual, or of a group. Studies concerned with specific predictions, with narration of facts and characteristics concerning individual, group or situations are all examples of descriptive research studies. Here the researcher intends to describe the situations where the store image attributes influence the consumer purchasing of the grocery product which ends up in satisfaction and loyalty.
3.2. The research Model (Conceptual Model) Figure 3.1.Research Model/Conceptual Model
3.3. The description of the Selection of subjects (Sampling)
3.3.1Sampling frame
Primary data needed for the study was collected through question.aire issued to consumers who make grocery purchase from different formats of retailing, who constitute the sample for the study.
3.3.2. Sampling method
Probability sampling is considered to be the best technique as all elements have equal chance to be included in the sample and hence it yields greater validity and reliability.
Random represents that each item of the sampling unit has an equal chance of inclusion in the sample.
3.3.3. Population and Sample Size
A population is defined as the “total collection of individuals or objects that forms the focus of the research” whereas the sample is “a selected part or a subset of the population. A research is generally conducted to make inferences about the population based on the information available about the sample, in order to make inferences from the sample to the population.
3.3.4. Sample size determination
The study used disproportionate systematic random sampling. The total number of respondents from different type of grocery selling retailer was estimated scientifically using the
formula n=(S*Z/e) 2 for an undisclosed large population size .Initially, a pilot study of 15 respondents who does grocery purchase in klang valley region was done for scale refinement and for estimating the standard deviation (store image attribute) score, which got applied for determining the exact population size. Formula for sample size calculation when estimating means (for continuous or interval-scaled variables) are as follows:
According to Rajendra Nargundkar (2010), the formula for computing ‘n’, the sample size required (Undisclosed population size) to do the study, is: n = (ZS/e)2
Where,
Z: The ‘Z’ value represents the Z score from the standard normal distribution for the confidence level desired by the researcher. For example, a 95 per cent confidence level would indicate a Z score of 1.96 (From a standard normal distribution for a two-sided probability value of 0.95). Similarly, as the researcher desires a 90 per cent confidence level, the corresponding Z Score would be 1.645( again ,from the standard normal distribution ,for a two-sided probability of .90).
Generally, 90 or 95 per cent confidence is adequate for most marketing research studies .A 100 per cent confidence level is not practical in survey.
S: The ‘S’ represents the population standard deviation for the variable which we are trying to measure from the study. By definition, this was an unknown quantity, since we have not taken the sample yet. So, the question of knowing the value of ‘S’, the population standard deviation, does not arise.
However, we can use a rough estimate of the population standard deviation for the variable being measured .This estimate can be obtained in the following ways:
If past studies have measured this variable, we can use the standard deviation of the variable from one of the studies in recent past. It serves as a good approximation.
A very small sample can be taken as a test or pilot sample, only for the purpose of roughly estimating the population standard deviation of the concerned variable.
If the minimum and maximum values of the variable can be estimated, then the range of the variable known. Range = Maximum value –Minimum value. Assuming that in practically all variables, 99.7 per cent of the values of the variables would lie within +/- 3 standard deviations of the mean, we could get an approximate value of the standard deviation by dividing the range by 6.
The logic of this calculation is that the range is equal to 6 standard deviations for most variables. Therefore, range when divided by 6, should give a fairly good estimate of the standard deviation.
e: The third value required for calculating the sample size required for the study is ‘e’, called tolerable error in estimating the variable in question. This can be decided only by the researcher or his sponsor for the study. The lower the tolerance, the higher will be the sample size. The higher the tolerable error, the smaller will be the sample size required. If we take 95 per cent confidence level for this research study obviously we take .05 values as our tolerable error.
Hence, by extracting the standard deviation score of .793 of 55 grocery retail image store attributes / items extracted through the pilot study result of grocery retail survey, further by applying these appropriate values in the above mentioned formula, we got an estimate of 961 respondents to be surveyed in order for the researcher to understand the grocery purchase behavior of consumers located in Klang Valley. But according to (Nunally, 1978), we can take a minimum of 30 percentage of the estimated sample size as an acceptable representation.i.e.30 percentage of 961, we get a sample size of 288.
3.4. The measurement instrument used in this study (Instrument and Scaling)
3.4.1. Instrument development
The instrument designed in this section mention the scales utilized to measure the demographic profile of the grocery purchasing consumers as well as the description of scales utilized to measure other constructs within the conceptual or research model.
The survey instrument was developed by reviewing the various literatures and previous questionnaires’ related to various dimensions of the scales except the demographic section further the entire scales were validated and refined with the help of pilot study data.
3.4.2. Scaling Technique
Nominal scale and likert`s scale have been used in this study when the questionnaire was developed.
3.4.3. Nominal Scale
Nominal scale is simply a system of assigning number symbols to events in order to label them. Nominal scales provide convenient ways of keeping track of people, objects and events. This scale is used for the demographic section of the questionnaire where the questions are categorized variables.
In a likert`s scale, the respondent is asked to respond to each of the statements in terms of several degrees, usually five degrees of agreement or disagreement. At one extreme of the scale there is strong agreement with the given statement and at the other, strong disagreement, and between them lie immediate points. five point likert`s scale (5- Strongly Agree, 4- Agree, 3- Undecided, 2-Disagree, 1- Strongly Disagree) was used to all dimensions except demographic profile which consists of its own choice.
3.5. The reliability and validity analysis (Reliability and Validity)
3.5.1. Content validity
The validity of a measure refers to whether it actually measures what it claims to measure.” Content validity deals with `how representative and comprehensive the items were in creating the scale`. In this study, the researcher proposed the definitions of the constructs based on related empirical research literature. All of the instruments were derived from the existing research. In order to ensure the validity of the survey instrument, experts working in retail industry, academicians and consumers who do grocery purchase in klang valley region in the pilot study to critique and refine the instrument. Each expert was sent the proposed questionnaire and the expert panel reviewed the content of each item as they formulated and validated the instrument for the study further they made their comments and returned it to the researcher.
3.5.2. Face validity
Face validity does not refer to what the questionnaire actually measures but to what the items apparently measure. It is further highlighted that test takers and test administrators will not have confidence in the results of a test, if the test or its items do not appear valid. Panel of expert also plaid whether the measurement appears to measure what it is supposed to measure.
3.5.3. Reliability
Reliability refers to the accuracy or precision of a measuring instrument. The first step in order to examine the reliability is to run the cronbach`s alpha analysis to know the inter item co-relation consistency and to run a factor analysis to assess unidiamentionality.
The internal consistency of the instrument was assessed by Cronbach`s alpha. All constructs were expected to have higher reliability than the bench mark of 0.70, the cut of level of reliability recommended for the theory testing research (Nunally & Bernstenin, 1994). Data collected for pilot study was entered into SPSS software and the cronbach`s alpha value was calculated in order to assess the test adequacy of the research instrument. After obtaining the appropriate level of content validity and required level of reliability the final instrument was developed.
Table .3.1. Cronbach`s Alpha cut-off coefficient and Implied Reliability
Alpha coefficient Implied reliability
< 0.5 Unacceptable > 0.5 Poor > 0.6 Questionable > 0.7 Acceptable > 0.8 Good > 0.9 Excellent
3.6 Reliability Checking on Pilot study data Table 3.2.Reliability Statistics on pilot study data
Cronbach's
Alpha N of Items
.876 68
The alpha coefficient for the sixty eight items is .876, suggesting that the items have relatively high internal consistency. (Note that a reliability coefficient of .70 or higher is considered "acceptable" in most social science research situations, but a value of .9 and above is desired) The Item-Total Statistics table presents the Cronbach's Alpha if Item Deleted in the final column, as shown below:
Table 3.3.Item-Total Statistics on pilot study data
Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted quality 271.31 1662.731 .669 .873 ProdInfor m 271.15 1695.308 .184 .875 Variety 271.00 1671.833 .611 .874 National 271.85 1686.641 .241 .875 Value 271.92 1674.910 .544 .874 stock 271.69 1666.897 .608 .873 HighValue 271.69 1682.231 .428 .874 Private 271.77 1681.192 .561 .874 Fresh 271.38 1654.256 .669 .872 Organ 271.62 1648.756 .733 .872 Dietary 271.46 1664.269 .565 .873 Health 271.31 1648.231 .718 .872 Labeling 271.31 1671.564 .525 .874
EasLabel 271.23 1665.192 .596 .873
Table 3.3.Item-Total Statistics-Continued. culture 271.46 1630.103 .815 .870 Image 271.38 1643.756 .813 .871 Preference 271.31 1642.231 .884 .871 New 271.31 1656.231 .776 .872 List 271.69 1667.231 .603 .873 Loca 271.38 1649.090 .740 .872 TarPro 271.54 1651.603 .807 .872 offPromo 271.46 1646.603 .820 .872 Accr 271.69 1654.897 .635 .872 Discou 271.31 1648.231 .718 .872 Race 271.15 1681.641 .380 .874 Card 271.46 1671.103 .567 .874 Novel 271.54 1641.269 .861 .871 Crdcard 271.85 1650.808 .746 .872 Loyalty 271.31 1657.064 .669 .872 everlow 271.69 1687.897 .230 .875 Adapp 271.54 1647.769 .770 .872 Adconf 271.31 1662.064 .810 .873 Adspro 271.62 1648.756 .733 .872 Parklot 270.92 1659.410 .835 .872 PubTran 267.15 1353.474 .160 .982 Proxhom 271.15 1653.808 .855 .872 BusSch 271.31 1645.231 .757 .872 Valetp 271.46 1673.769 .385 .874 Opnhrs 271.69 1647.231 .674 .872 Phyfcl 271.15 1664.808 .810 .873 CashCnt 271.15 1649.808 .803 .872 ChkLines 271.38 1653.423 .680 .872 Carybag 271.23 1659.692 .795 .872 RestAre 271.23 1656.192 .735 .872 ShelfSGN 271.15 1657.808 .786 .872
Table 3.3.Item-Total Statistics-Continued Aislewdth 271.15 1657.808 .786 .872 Vrtcrd 271.15 1656.141 .708 .872 Exchan 271.08 1649.744 .892 .872 Grocprod 271.54 1666.436 .511 .873 Websal 271.54 1664.269 .610 .873 Assist 271.31 1674.564 .568 .874 Finser 271.08 1647.744 .926 .872 pop 271.15 1651.308 .898 .872 Salesp 270.85 1663.974 .749 .873 SalesPrd 270.92 1662.077 .785 .873 Compl 271.38 1662.590 .555 .873 Salesmd 271.31 1675.064 .410 .874 SalsGreet 271.15 1654.808 .728 .872 ShpList 271.77 1689.359 .235 .875 Aroma 271.62 1689.423 .214 .875 Decor 271.69 1686.064 .256 .875 Luxatmos 271.69 1700.397 .061 .876 Music 271.69 1692.064 .237 .875 Temp 271.23 1659.192 .689 .873 Ext 271.31 1656.564 .770 .872 Atmos 271.54 1647.269 .777 .872 points 271.69 1660.731 .710 .873
Item-total Statistics gives statistics for relationships between individual items and the whole scale. The important bits for our purposes of scale refinement are the last two columns. Corrected item-total correlations are the correlations between scores on each item and the total scale scores. If the scale is internally consistent you would expect these correlations to be reasonably strong. In this case, the correlations of the most variables fall above .5 or more, indicating good internal consistency. The variables which are below .5 can be deleted from the construct to strengthen the scale internal consistency. The final column tells us what Cronbach's alpha would be if we deleted an item and re-calculated it on the basis of the remaining three items.
After removing the above underlined variables which are below the value of .5 in the corrected item-total correlations column, where in this case the correlations of the variables which are below .5, indicating bad consistency. Now the reliability statistics has improved from .876 to . 983 (Cronbach's Alpha value) and similarly, Cronbach's Alpha if Item Deleted column value also got improved and the Item-Total Statistics reflect a good statistical figures appropriate for a good scale.
3.7. Reliability checking on Total Sample Size Data
Table 3.4.Reliability checking on Total sample size data
Cronbach's
Alpha N of Items
.983 55
The above alpha coefficient for the fifty five items ,after deleting poor internally inconsistent elements is .983, suggesting that the items have relatively high internal consistency which meets the desired value of above .9.
Table 3.5.The improved item-Total statistics after removing the item variables on the total sample size data
Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted quality 217.21 970.797 .704 .982 Variety 217.00 982.923 .550 .983 Value 217.93 983.610 .522 .983 stock 217.57 974.879 .592 .983 Private 217.64 986.247 .583 .983 Fresh 217.43 967.187 .651 .982 Organ 217.57 961.495 .775 .982 Dietary 217.43 974.264 .584 .983 Health 217.29 959.758 .785 .982 Labeling 217.29 977.912 .585 .983 EasLabel 217.21 972.797 .661 .982 culture 217.50 951.962 .766 .982 Image 217.43 957.648 .823 .982 Preference 217.29 959.604 .874 .982 New 217.36 972.247 .660 .982 List 217.71 982.066 .483 .983 Loca 217.36 965.786 .708 .982 TarPro 217.57 969.033 .711 .982 offPromo 217.43 963.495 .797 .982 Accr 217.64 965.786 .679 .982 Discou 217.36 965.170 .655 .982 Card 217.43 973.956 .590 .983 Novel 217.50 959.192 .845 .982 Crdcard 217.79 967.874 .690 .982 Loyalty 217.29 970.527 .658 .982 Adapp 217.43 962.879 .728 .982 Adconf 217.21 969.874 .844 .982 Adspro 217.57 965.033 .709 .982 Parklot 216.93 972.533 .799 .982 Proxhom 217.07 967.610 .820 .982 BusSch 217.29 962.220 .740 .982 Opnhrs 217.64 963.786 .654 .982 Phyfcl 217.14 976.747 .776 .982 CashCnt 217.14 965.363 .786 .982
Table 3.5.The improved item-Total statistics after removing the item variables-Continued ChkLines 217.36 964.401 .734 .982 Carybag 217.14 967.516 .854 .982 RestAre 217.14 964.901 .796 .982 ShelfSGN 217.07 965.918 .858 .982 Aislewdth 217.07 965.918 .858 .982 Vrtcrd 217.07 965.148 .767 .982 Exchan 217.00 964.000 .878 .982 Grocprod 217.50 970.731 .625 .982 Websal 217.50 968.731 .748 .982 Assist 217.29 984.835 .516 .983 Finser 217.00 962.154 .919 .982 pop 217.14 966.132 .887 .982 Salesp 216.79 974.489 .750 .982 SalesPrd 216.86 973.516 .769 .982 Compl 217.29 966.374 .666 .982 SalsGreet 217.07 968.225 .706 .982 ShpList 217.71 989.297 .327 .983 Temp 217.21 973.720 .642 .982 Ext 217.29 972.066 .715 .982 Atmos 217.50 964.423 .745 .982 points 217.64 974.709 .660 .982
To conclude, through the pilot study survey data, question numbers namely 2,4,7,15,26,31,36,39,58,60,61,62,63,64 has been removed for the purpose of reliability requirements and content validity requirements and certain questions also got rephrased due to content validity requirements.
3.8. The Procedure for distribution of the questionnaire and collection of data (Method of Data collection)
The procedure for distribution of questionnaire and collection of data provide all sequential steps of data collection in statistical scientific manner.
3.8.1. Primary Data
Researcher has used both primary and secondary data. Data that have been collected from preliminary/first hand experience is known as primary data. Primary data in this research is concerned with the survey instrument which was having four sections of questions namely Demographic Profile and store image attributes, consumer satisfaction scale and loyalty scale.
3.8.2. Secondary Data
Secondary data were collected through journals, text books, websites, unpublished Research thesis, and Published PhD thesis.
3.9. Pilot study
3.9.1. Data collection for Pilot study
A pilot study was conducted before the main study was implemented. The purpose of the pilot study was to test the appropriateness of the instrumentation, time demands, and clarity of questionnaire presentation for the purpose of the current study.
Pilot test of the survey questionnaire was given to fifteen respondents (N=15) randomly selected from consumer located in Klang valley region. It took approximately five to fifteen minutes for the respondents to complete the survey questionnaire.
3.10. Administration of the final questionnaire
Once the survey questionnaire is finalized researcher personally went to different formats of retailing stores entrance for data collection and met the customer randomly whoever i.e. every 3rd customer who completes the transaction from the cash counters. Researcher collected two hundred and eighty eight completed questionnaires.
3.11. The description of the Statistical tools used for data analysis (Statistical Tools) Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in social sciences. It is important to recognize the appropriate design, and to understand how to effectively implement it.
Statistical methods provide a powerful tool for interpreting patterns in data, but can create an opaque layer between scientists and their target audience. Intuitive graphics and clear language are the keys to bringing scientific insight to resource protection efforts. The data tell a story,
statistics are the tools used to check our facts.
Tools for data analysis includes Frequency distribution Chi-Square
ANOVA
Factor Analysis
Multiple regression analysis
3.11.1. Frequency distribution
A frequency table is a simple way to display the number of occurrences of a particular value or characteristic.
3.11.2. Chi-square test
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis.
3.11.3. Analysis of variance test
Coolican (1999, p. 389) maintains that “Analysis of variance procedures are powerful parametric methods for testing the significance of differences between sample means where more than two conditions are used, or even when several independent variables are involved.” ANOVA makes it feasible to appraise the separate or combined influences of several independent variables on the experimental criterion (Mouton & Marais, 1990). ANOVA was therefore used to establish if a statistical significant difference exist between the levels of stress based on the biographical variables.
This is used to test the significance of the differences among more than two sample means. The steps followed are,
1. Between columns variance was calculated by the following formula σ2 = Σ n j (xj – xj)2 / k-1
2. Within the column variance was calculated by using the following formula. σ2 = Σ [n j –1 / nt – k] S j2
3. F ratio was computed by the following formula Between column variance
F = Within column variance
4. The Null Hypothesis (H0) and Alternative Hypothesis (Ha) were framed as stated below. H0 : No significant difference among sample means.
Ha : Significant difference exists among sample means.
5. When the probability value of F ratio was more than the significance value à = 0.05 the H0 was not rejected. When the probability value of F ratio was less than the 0.05 level the H0 was not accepted.
6. Homogeneity test was also used to classify the sample means into groups. When all the sample means were in the same group homogeneity was established. Otherwise heterogeneous nature was identified.
7. In this study ANOVA was used to find out the significance of the differences between different variables of the samples selected.
3.11.4. Factor analysis
A factor is an underlying dimension that account for several observed variables. There can be one or more factors, depending upon the nature of the study and the number of variables involved in it.
Factor analysis
Factor analysis involves many terminologies which are presented in this subsection for better understanding of the related techniques.
Correlation coefficient matrix
It is the matrix of correlation coefficients of the original observations between different pairs of input variables
Factor loadings, Li (j)
It is a matrix representing the correlation between different combinations of variables and factors.
Communality,
It is the sum of squares of the factor loadings of the variable i on all factors: =
Eigen value
It is the sum of squares of the factor loadings of all variables on a factor Eigen value of the factor j =
Note: The sum of the Eigen value s of all factors (if no factor is dropped) is equal to the sum of
the communalities of all variables. Rotation
After obtaining factor loadings, one should examine whether the factor loading matrix possess simple structure. If a factor loading matrix has a simple structure, it is easy to make interpretations about the factors. If there is no simple structure, then the n – dimensional space of the factors should be rotated by an angle such that the factor loadings are revised to have a simple structure which will simplify the process of interpretation of the factors. Such rotation is called rotation of factors.
A simple structure means that each variable has very high factor loading (as high as 1) on one of the factors and very low factor loading (as low as 0) on other factors. The communalities of each variable before and after factor rotation will be the same.
The popular methods of rotation of factors are varimax method and promax method. Varimax method of factor rotation employs orthogonality between different pairs of factors axes. This
means that the angles between different pairs of factors axes are even after rotation.
The promax method employs oblique rotation. This means that the angles between different
3.11.5. Multiple regression analysis
Multiple regression analyses the common and separate influences of two or more variables on a dependent variable (Kerlinger, 1986), and it is used to establish the extent to which various differing variables add to predict another variable (Guyatt, Walter, Shannon, Cook Jaeschke & Heddle, 1995). Multiple regression was therefore used to determine if the selected sources of stress statistically significantly explain the variance in total stress experienced by teachers.
Procedure
As stated earlier, if the number of independent variables in a regression model is more than one, then the model is called as multiple regression. In fact, many of the real-world application demand the use of multiple regression models.
A sample application is as stated below:
Y = b0+b1X1 + b2X2 + b3X3 + b4X4
Where Y is the economic growth rate of a country; X1, the time period: X2, the size the
population of the country; X3, the level of employment un percentage; X4, the percentage literacy; b0 is the intercept; and b1, b2, b3 and b4 are the slopes of the variables X1, X2, X3, and X4,
respectively.
In this regression model, X1, X2, X3, and X4, are the independent variables and Y is the dependent variables.
Regression model with two independent variables using normal equations Suppose the number of independent variables is two as shown in the following model.
One can use the following normal equations and solve for the constants to design the regression equation.
= nb0 + b1 ∑ X1 + b2 ∑ X2
= b0∑ X1+ b1∑ X12 + b2 ∑ X1 X2
= b0∑ X2+ b1∑ X1 X2+ b2 ∑ X22
Where n is the total number of combinations of observations. The solution to the above set of simultaneous equations will form the results for the coefficients (b0, b1 and b2) of the regression model.
3.12. Chapter summary
This chapter summarise the design of the study that was used to find out the solution to the research problem by means of answering all the research questions. This chapter addresses the methodological procedure adopted for the present investigation like sampling techniques used i.e. simple random disproportionate sampling, pilot study undertaken, and questionnaire development process and also explain the various statistical tools (Chi-square ,Anova, Correlation ,Factor analysis and Multiple regression analysis) used for this study.
