CHAPTER SIX DATA ANALYSIS AND INTERPRETATION

Transcription

1 CHAPTER SIX DATA ANALYSIS AND INTERPRETATION In this chapter various factors influencing preference of using Delhi Metro were checked and the impact of demographic characteristics (age, gender) of consumers on their preference towards Delhi Metro was tested. Objective 1: To study the factors influencing preference of using Delhi Metro. 6.1 FACTOR ANALYSIS Factor analysis uses mathematical procedures for the simplification of interrelated measures to discover patterns in a set of variables (Child, 2006). Attempting to discover the simplest method of interpretation of observed data is known as parsimony, and this is essentially the aim of factor analysis (Harman, 1976). Factor analysis has its origins in the early 1900 s with Charles Spearman s interest in human ability and his development of the Two-Factor Theory; this eventually lead to a burgeoning of work on the theories and mathematical principles of factor analysis (Harman, 1976). The method involved using simulated data where the answers were already known to test factor analysis (Child, 2006). Factor analysis is used in many fields such as behavioural and social sciences, medicine, economics, and geography as a result of the technological advancements of computers. The two main factor analysis techniques are Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). CFA attempts to confirm hypotheses and uses path analysis diagrams to represent variables and factors, whereas EFA tries to uncover complex patterns by exploring the dataset and testing predictions (Child, 2006). This tutorial will be focusing on EFA by providing fundamental theoretical background and practical SPSS techniques. EFA is normally the first step in building scales or a new metrics. Finally, a basic guide on how to write-up the results will be outlined. Factor analysis operates on the notion that measurable and observable variables can be reduced to fewer latent variables that share a common variance and are unobservable, which is known as reducing dimensionality (Bartholomew, Knott, & Moustaki, 2011). These unobservable factors are not directly measured but are essentially hypothetical constructs that are used to represent variables (Cattell, 1973). For example, scores on an oral presentation and an interview exam could be placed under a factor called communication ability ; in this case, the latter can be inferred from the former but is not directly measured 119 P a g e

2 itself. EFA is used when a researcher wants to discover the number of factors influencing variables and to analyze which variables go together (DeCoster, 1998). A basic hypothesis of EFA is that there are m common latent factors to be discovered in the dataset, and the goal is to find the smallest number of common factors that will account for the correlations (McDonald, 1985). Another way to look at factor analysis is to call the dependent variables surface attributes and the underlying structures (factors) internal attributes' (Tucker & MacCallum, 1997). Likert scale developed by Rensis Likert one of the most frequently used scales to evaluate psychographic variables by different marketing researchers has been used with five point rating for the study. Likert scale is also known as summated scale because the scores of individual statements can be summed up to get the total score for a subject. The total of this score for different respondents tells us about the opinion of the subject. Likert scale is extensively used in social and behavioral sciences. Factor Analysis was used on 41 items for determining the various influencing factors for Delhi Metro. The hypotheses to assess the impact of demographic characteristics of consumers on their preference of Delhi Metro have been tested using Independent Sample T-Test and One-Way ANOVA. Before starting the data collection for the study the questionnaire was pre-tested to assess the validity and reliability. Also, any possibility of any weakness can also be ruled out at this stage. The statements of the questionnaire were discussed with the experts of marketing research and the suggestions given by them were incorporated i.e. some statements were discarded. After the final approval from experts, pilot study was undertaken on 65 respondents to ensure the appropriateness of the statements. The questionnaire was revised and the final questionnaire was administered to 650 respondents to get a targeted 601 valid responses (92.46% response). (The questionnaire is provided in the annexure). For reliability Cranach s Alpha value was checked which came out to be TABLE6.1 RELIABILITY STATISTICS Cronbach s Alpha Number Of Items P a g e

3 The demographic characteristics of the respondents depict that equal representation of respondents of different age groups. 146 respondents (24.3%) were between the age group of years, 178 respondents (29.6%) were between the age group of years, 126 respondents (21%) were between the age group of years and 151 respondents (25.1%) were of 40 years and above. Also, the descriptive statistics table shows that 342 respondents (56.9%) were males and 259 respondents (43.1%) were females. Males have a higher representation in the sample than females. TABLE 6.2 AGE OF THE RESPONDENT Frequency Percent Valid Percent Cumulative Percent Years Years Years 40 years and above Total TABLE 6.3 GENDER OF RESPONDENT Valid Cumulative Frequency Percent Percent Percent Male Female Total P a g e

4 The study comprise of descriptions and tabular displays to present suitable context for depicting conclusions from the data collected. Tables prove to be apt method to improvise the method of presentation of the analysis.. Factor Analysis has been used to find out the important factors of preference towards Delhi Metro for the study by using SPSS Software 19.0 version. The questionnaire comprised of 6 negative statements and reverse coding was done for negative statements. Factor analysis is a statistical technique that reduces data and allows simplification of the co relational relationships between continuous variables. Exploratory factor analysis is used to identify constructs and further investigate relationships between key interval scaled questions to ascertain reasons for preference of metro from a sample of 601 respondents. To test, following steps were taken: At the first stage correlation matrices were computed. It proposed to go ahead with factor analysis as there is enough correlation. A study of Kaiser-Meyer-Olkin s Measure of Sampling Adequacy (MSA) found enough correlation for all the variables (KMO & Bartlett s Table 6.4) Kaiser-Meyer-Olkin MSA s score of indicated that the sample size is adequate for sampling. Bartlett test of sphericity is used to test the overall significance of correlation matrices and it also provided support for the validity of the factor analysis (KMO & Bartlett s Test Table 6.4). TABLE 6.4 KMO & BARTLETT S TEST Kaiser-Meyer-Olkin Measure of Sampling Adequacy..939 Bartlett's Test of Sphericity Approx. Chi- Square Df 820 Sig..000 Once it is concluded that the data is suitable for factor analysis, data is extracted using Principal components analysis that helps determine the factor underlying the relationship between variables. The total variable Explained box is suggesting that it extracts nine factors which accounts for % of the variance of the relationship between variables. (Total Variance Explained Table 6.5). 122 P a g e

5 There are only nine factors, each having Eigen value exceeding 1. The Eigen values for nine factors were 4.359, 4.286, 4.082, 4.009, 3.327, 2.096, 2.091, 2.080, and respectively. (Total Variance Explained Table 6.5) The percentage of total variance is used as an index to determine how well the total factor solution accounts for what the variables together represent. The index for present solution accounts for % of the total variations. It is pretty good extraction as it can be economize on the number of factors (from 41 it has reduced to 9 factors) while we have lost 32.30% information content for factors. The percentage of variance explained by factor one to nine for factors are %, %, 9.957%, 9.777%, 8.115%, 5.111%, 5.099%, 5.074% and 3.480% respectively (Total Variance Explained Table 6.5). Communalities Table 6.6 tells us that after nine factors are extracted and retained, the communality is for variable 1, for variable 2 and so on. It means 60.5% of the variance of variable 1 is being captured by the nine extracted factors together. The proportion of variance in any one of the original variables, which is being captured by the extracted factor, is known as communality (Nargundkar, 2002). TABLE 6.5 TOTAL VARIANCE EXPLAINED Initial Eigen Values Extraction Sums Of Squared Loadings Rotation Sums Of Squared Loadings % Of % Of Compo % Of Cumulative Varianc Cumul Varianc Cumula nent Total Variance % Total e ative % Total e tive % P a g e

6 P a g e

7 Loading on factors can be positive or negative. A negative loading indicates that this variable has an inverse relationship with the rest of the factors. The higher the loading the more important is the factor. However Comrey (1973: 1346) suggested that anything above 0.44 could be considered salient, with increased loading becoming more vital in determining the factor. All the loadings in the research are positive. (Communalities Table 6.6) 125 P a g e

8 TABLE 6.6 COMMUNALITIES Initial Extraction Initial Extraction Initial Extraction S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S P a g e

9 TABLE 6.7 COMPONENT MATRIX Component S22 Automatic doors in the coaches are very convenient. S21 Delhi Metro is not easy to board. S41 Display Screens in the coaches provide correct information. S11 Smart card facility is easily available. S7 Delhi Metro helps in reducing the overall time of journey. S14 Frisking at stations makes you feel safe. S15 Separate coaches for women are available. S17 Lighting at stations is sufficient. S27 There are proper sheltered waiting areas. S34 Route maps are well displayed at stations P a g e

10 S25 Seats are reserved for senior citizens. S28 Delhi Metro has sufficient seating arrangements for commuters. S37 Announcements in both languages are properly done. S24 Seats are reserved for handicapped people in Delhi Metro. S20 Escalators are available at the stations S12 There is automatic fare collection system. S18 Proper lighting is not there in coaches. S1 Adequate feeder bus services are available. S38 Delhi Metro provides timely information about delays. S13 There are no CCTV cameras at the stations. S8 Delhi Metro is economical P a g e

11 S39 Delhi Metro does not provide correct information on cancellations S40 Delhi Metro has a good Lost and Found service. S33 Delhi Metro has proper mobile network. S30 Delhi Metro coaches should have washrooms. S26 AC in the coaches is very effective. S31 Delhi Metro maintains good standards of cleanliness at stations. S32 Delhi Metro coaches are not very clean S19 Delhi Metro provides easy parking facility. S35 Delhi Metro has insufficient standing arrangements for commuters S29 The staff is friendly S10 There is sufficient number of token counters at the stations P a g e

12 S36 The staff is informative S3 Connectivity to the airport is very useful. S23 Delhi Metro station is near to your home. S2 There should be connectivity to all major railway stations S9 Delhi Metro should provide services on public holidays. S6 Delhi Metro service is available on weekends. S5 Delhi Metro should provide services at night S4 Frequency of Delhi Metro is sufficient. S16 Delhi Metro is less prone to breakdowns Extraction Method: Principal Component Analysis. 9 factors extracted Factors are rotated for better interpretation since unrotated factors are ambiguous. The goal of rotation is to attain an optimal simple structure which attempts to have each variable load on as few factors as possible, but maximizes the number of high loadings on each variable (Rummel, 1970). Ultimately, the simple structure attempts to have each factor define a distinct cluster of interrelated variables so that interpretation is easier (Cattell, 1973). For example, variables that relate to language should load highly on language ability factors but should have close to zero loadings on mathematical ability. Rotation is necessary when extraction 130 P a g e

13 technique suggest there are two or more factors. To provide a clear picture of the item loading vis-à-vis the factor and also to get an idea related to the difference among the factors from the initial extraction, rotation of the factors is designed. Large communalities indicate that a large number of variance has been accounted for by the factor solution. Varimax rotated factor analytic results for factor influencing the choice of metro is shown in Rotated Component matrix Table 6.8. TABLE 6.8 ROTATED COMPONENT MATRIX Component S28 Delhi Metro has sufficient seating arrangements for commuters. S7 Delhi Metro helps in reducing the overall time of journey. S20 Escalators are available at the stations S19 Delhi Metro provides easy parking facility. S1 Adequate feeder bus services are available. S35 Delhi Metro has insufficient standing arrangements for commuters S8 Delhi Metro is economical S33 Delhi Metro has proper mobile network P a g e

14 S40 Delhi Metro has a good Lost and Found service. S30 Delhi Metro coaches should have washrooms. S31 Delhi Metro maintains good standards of cleanliness at stations. S32 Delhi Metro coaches are not very clean. S26 AC in the coaches is very effective S34 Route maps are well displayed at stations. S18 Proper lighting is not there in coaches. S17 Lighting at stations is sufficient. S15 Separate coaches for women are available. S14 Frisking at stations makes you feel safe. S13 There are no CCTV cameras at the stations S25 Seats are reserved for senior citizens. S24 Seats are reserved for handicapped people in Delhi Metro P a g e

15 S39 Delhi Metro does not provide correct information on cancellations. S37 Announcements in both languages are properly done. S38 Delhi Metro provides timely information about delays. S27 There are proper sheltered waiting areas S11 Smart card facility is easily available. S12 There is automatic fare collection system. S21 Delhi Metro is not easy to board. S41 Display Screens in the coaches provide correct information. S22 Automatic doors in the coaches are very convenient S6 Delhi Metro service is available on weekends. S9 Delhi Metro should provide services on public holidays. S5 Delhi Metro should provide services at night S23 Delhi Metro station is near to P a g e

16 your home. S3 Connectivity to the airport is very useful. S2 There should be connectivity to all major railway stations S29 The staff is friendly S36 The staff is informative S10 There is sufficient number of token counters at the stations S4 Frequency of Delhi Metro is sufficient. S16 Delhi Metro is less prone to breakdowns The Nine factors shown in Table 6.8 have been discussed below:- Factor 1: Travelling Convenience It is the most vital factor, which explains % of the variation. Delhi Metro has sufficient seating arrangements for commuters (0.751), Delhi Metro helps in reducing the overall time of journey (0.746), Escalators are available at the stations (0.744), Delhi Metro provides easy parking facility (0.732), Adequate feeder bus services are available (0.716), Delhi Metro has insufficient standing arrangements for commuters (0.688), Delhi Metro is economical (0.683) emerge with good positive correlations. 134 P a g e

17 FIGURE 6.1 ESCALATORS AT DELHI METRO STATION FIGURE 6.2 FEEDER BUS SERVICE PROVIDES LAST MILE CONNECTIVITY 135 P a g e

18 FIGURE 6.3 ADEQUATE PARKING FACILITY AVALIABLE AT ALL METRO STATIONS Factor 2: Facilities for Commuters There are seven loads to this factor. This factor is the second important factor, which accounts for nearly % of the variations. Delhi Metro has proper mobile network (0.824), Delhi Metro has a good Lost and Found service (0.812), Delhi Metro coaches should have washrooms (0.792), Delhi Metro maintains good standards of cleanliness at stations (0.79), Delhi Metro coaches are not very clean (0.773), AC in the coaches is very effective (0.769) signifies that facilities for commuters is an important factor. Factor 3: Safety Measures for Commuters There are five significant variables with a variation of 9.957% and these are Route maps are well displayed at stations (0.76), Proper lighting is not there in coaches (0.75), Lighting at stations is sufficient (0.75), Separate coaches for women are available (0.75), Frisking at stations makes you feel safe (0.71), There are no CCTV cameras at the stations (0.69) depicts that safety measures for commuters plays an important role in selecting metro as a mode of travel. 136 P a g e

19 FIGURE 6.4 RESERVED SEATS FOR LADIES FIGURE 6.5 SEPARATE COACHES FOR WOMEN Factor 4: Ease of Travel This factor has the six loadings, which has of the variation, and this comprises of Seats are reserved for senior citizens (0.77), Seats are reserved for handicapped people in Delhi Metro (0.76), Delhi Metro does not provide correct information on cancellations (0.75), Announcements in both languages are properly done 137 P a g e

20 (0.73), Delhi Metro provides timely information about delays (0.7), There are proper sheltered waiting areas (0.69) respectively show ease of travel is also a significant factor for preferring metro. Factor 5: Automated Services The next important factor, which carries a loading of 8.115% of the variation, comprises of five loadings, Smart card facility is easily available (0.74), There is automatic fare collection system (0.72), Delhi Metro is not easy to board (0.71), Display Screens in the coaches provide correct information (0.71), Automatic doors in the coaches are very convenient (0.7) signifies that automated services is vital factor for preferring metro. FIGURE 6.6 AUTOMATIC FARE COLLECTION SYSTEMS 138 P a g e

21 Factor 6: Extended Availability Extended availability is the next factor, which influences preferring metro and has 5.111% of the variation. This factor has three loading- Delhi Metro service is available on weekends (0.79), Delhi Metro should provide services on public holidays (0.79), Delhi Metro should provide services at night (0.75). Factor 7: Connectivity Connectivity is the factor which explains 5.099% of variance and has three loadings. Delhi Metro station is near to your home (0.79), Connectivity to the airport is very useful (0.77), and There should be connectivity to all major railway stations (0.75). Factor 8: Friendly Staff Friendly staff is a factor, which influences preferring metro and has 5.074% of the variation. The staff is friendly (0.827), the staff is informative (0.795), There is sufficient number of token counters at the stations (0.791) Factor 9: Frequency Frequency has 3.480% of the variation explained and has two statements. Frequency of Delhi Metro is sufficient (0.873), Delhi Metro is less prone to breakdowns (0.732). 139 P a g e

22 FIGURE 6.7 DELHI METRO RUNS WITH INCREASED FREQUENCY DURING PEAK HOURS 140 P a g e

23 Travelling Convenience Facilities For Commuters Safety Measures For Commuters Ease Of Travel Automated Services Extended Availability Friendly Staff Frequen cy FIGURE 6.8 NINE FACTORS EXTRACTED FROM FACTOR ANALYSIS After reaching the factors, a hypothesis testing has been conducted for the second objective: - Objective 2: To analyze the influence of demographic characteristics of consumers on their preference towards Delhi metro In this study there is one demographic where we have two independent groups i.e. gender. Independent sample t-test is used for comparing the difference between these groups. For, demographics having more than two categories or groups like age- one way ANOVA is applied to test whether there is a significant difference between the mean scores of various categories. Post Hoc analysis is used for further ascertaining 141 P a g e

24 which groups differ among their mean score. When assumption of Homogeneity of Variance sustains, Tucky s method is used else Games Howell method is used. 6.2 EFFECT OF AGE ON VARIOUS FACTORS OF PREFERENCE TOWARDS METRO H01: There is no significant difference between the mean scores of various factors of preference towards metro for different age groups. TABLE 6.9 ANOVA BETWEEN AGE AND VARIOUS VARAIBLES OF FACTORS OF PREFERENCE TOWARDS METRO Levene Statistic Sig. F Sig. Statistica Sig. Travelling Convenience Facilities for Commuters Safety Measures for Commuters Ease of Travel Automated Services Extended Availability Connectivity Friendly Staff Frequency Analysis of Variance TABLE 6.9 reflects travelling convenience and automated services differs significantly on the basis of age. No significant difference was observed on the remaining variables between the age groups. Hence, null hypothesis stands REJECTED in case of convenience and automated services. For further analysis post hoc was used. 142 P a g e

25 TABLE 6.10 DESCRIPTIVE OF TRAVELLING CONVENIENCE FOR AGE Travelling Convenience N Mean Years Years Years Years and Above Total TABLE 6.11 POST HOC TEST ON TRAVELLING CONVENIENCE FOR AGE (I) Age Of The Mean 95% Confidence Interval Dependent Responde (J) Age Of The Differen Std. Lower Upper Variable nt Respondent ce (I-J) Error Sig. Bound Bound Games- Howell Years Years Years years and above Travelli ng Conven ience Years Years 40 years and above Years Years years and above Years Years years and above Years Years P a g e

26 31-40 Years When we see the Post hoc TABLE 6.11 of travelling convenience, age groups years differ significantly from years. Descriptive TABLE 6.10 shows travelling convenience has the highest mean score in years (M=3.8974) as compared to the age group of years (3.5669), which shows that the youngest group of respondents have higher preference for travelling convenience as compared to respondents in the age group of years. Youngsters are in their start of careers and do not have that much of disposable income to travel luxuriously through their own transport, thus opt for a convenient and economical mode like metro. Also, they tend to travel to various locations and like to save time which metro offers due to their fixed time schedules and lack of ambiguity related to road traffic conditions. TABLE 6.12 DESCRIPTIVE STATISTICS OF AUTOMATED SERVICES FOR AGE N Mean Years Years Automated Services Years years and above Total P a g e

27 TABLE 6.13 POST HOC TEST ON PREFERENCE FOR AUTOMATED SERVICES FOR DIFFERENT AGE GROUPS (I) Age Of The Mean Differe 95% Confidence Interval Dependent Respond (J) Age Of The nce (I- Std. Lower Upper Variable ent Respondent J) Error Sig. Bound Bound Automated Games Years Services Howell Years Years years and above Years Years Years years and above Years Years Years years and above years and above Years Years Years As per the Post hoc TABLE 6.13 the respondents of years differ significantly from the respondents of the years. The respondents of years (M=4.1427) have higher mean score on automated services as compared to the respondents of the age years (M=3.8667) as per the Descriptive TABLE We 145 P a g e

28 can say that younger respondents prefer automated services to older age groups. This is attributed to the fact that youngsters are keener on using technology as compared to their elder counterparts. Also, people in their early ages tend to travel more as compared to people in later age due to various job responsibilities etc. 6.3 EFFECT OF GENDER ON VARIOUS FACTORS OF PREFERENCE TOWARDS METRO H02: There is no significant difference between the mean scores of various factors of preference towards metro for different genders TABLE 6.14 GROUP STATISTICS OF GENDER TOWARDS PREFERENCE OF METRO Gender Of The Respondent N Mean Std. Deviation Std. Error Mean Travelling Convenience Facilities for Commuters Safety Measures for Commuters Ease of Travel Automated Services Extended Availability Male Female Male Female Male Female Male Female Male Female Male Female P a g e

29 Connectivity Friendly Staff Frequency Male Female Male Female Male Female TABLE 6.15 METRO T-TABLE OF GENDER AND VARIOUS FACTORS OF PREFERNCE TOWARDS INDEPENDENT SAMPLES TEST Levene's Test For Equality Of Variances T-Test For Equality Of Means Sig. Std. (2- Mean Error Tail Differe Differe F Sig. T Df ed) nce nce Equal variances assumed Travelling Convenience Equal variances not assumed Facilities for Commuters Safety Measures for Commuters Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not P a g e

30 assumed Ease of Travel Automated Services Extended Availability Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Connectivity Equal variances not assumed Friendly Staff Equal variances assumed Equal variances not assumed Equal variances assumed Frequency Equal variances not assumed Independent sample T-test table reveals that there is no significant difference in the mean scores of various factors of preference towards metro for males and females. Hence our null hypothesis stands accepted for various factors. Hence, we can say that males and females have no difference in factors towards preference of metro. Metro offers a convenient and easy mode of transport for people. Moreover, it is also a very safe transportation. Thus, it is used by males and females alike. 148 P a g e