B4 Essay Example | Topics and Well Written Essays - 1000 words

B4 - Essay Example Ordinal and interval variables collect measurements. Interval data is actually measured on a continuous scale (actual quantities of some quality like height or age) while ordinal data is numerical form of classification, where whole numbers are used to denote order but the numbers themselves are not measures but a form of classification (GraphPad.com). Table 1: Variables Measured in the Survey Interval variables Ordinal variables Categorical variables Age Age category Gender Distance travelled Distance category Reason 1 Regularity of visits Reason 2 Satisfaction with: price Department Number of items Purchase Service Payment Quality Follow up Overall Store Contact The variables in the top row are italicized to indicate that they are independent variables. In this survey, it was hypothesised that demographic factors such as age and gender (pre-existing qualities or ‘independent variables) might influence opinions and behaviour of respondents (dependent variables). For men and wo men might differ in the distance they are prepared to travel to a store. Description of the Data Table 2 shows the number of women and men in the sample and various measures of their age profile. Table 2: Demographics of the Sample Gender All Women Men Number of people 582 373 (64%) 209 (26%) Mean age 42.6 42.8 42.3 Minimum age 17 17 17 Median age 42 42 42 Maximum age 75 75 74 The sample comprises 582 shoppers between the ages of 17 and 75, nearly two-thirds of who are women and just over third men. The age profiles of the men and women are very similar. Analysis of the distance travelled by respondents to the store where they were interviewed revealed a wide disparity. The modal distance (the most common length or trip) was less than a mile, but many had travelled much further, up to 53 miles. The median distance travelled was 5 miles and the mean just under 10. This indicates a positively skewed distribution where it is difficult to say what is the ‘typical’ distance travelled to the company’s stores. Inferential Statistics Table 3 shows the results for all shoppers, with men and women grouped separately. Separating women’s and men’s responses in this way allows a preliminary assessment of whether the independent variable (in this case gender) is influencing the dependent variable (distance travelled to the store). Table 3: Distance Travelled to the Store where Interviewed Distance travelled Less than 1 mile 1-5 miles 5-10 miles 10-30 miles Over 30 miles Total Women 49 (13%) 149 (40%) 83(22%) 69 (19%) 23 (6%) 373 Men 23(11%) 74 (35%) 51 (24%) 52 (25%) 9 (4%) 209 Total 72 223 134 121 32 582 The message is mixed: a higher proportion of the women than of the men travelled the shortest distances, but at the other end of the scale women were also more likely than men to have travelled the longest distances. A possible means of determining whether there is a difference between the distances men and women are prepared to travel to the company’s shops is to compare the mean raw distance (using the actual mileages rather than the categories) travelled by respondents of each gender. The mean distance travelled by the female respondents was 9.54 miles compared with 10.26 miles by the men. The standard deviations of the two samples are similar (11.1 and 10.6), so it is appropriate to conduct a ‘type 2’ test, but since the samples are independent and of different sizes we use an independent t-test