For a sample to be suitable it must be representative of the relevant population. When a sample fails to include all the characteristics available in the population it is said to be biased. Selection bias a rises when the sampling procedures are insufficiently random resulting in a sample that is not representative of the relevant population, the result is a sample that over represents or under represents certain characteristics of the population (Lucey, 2003). The data set of Little Town café has a sample size of 57 observations of its guests over a three year period. Since the data is to be used to forecast the number of guests in order to make staffing decisions, the data set can be said to be representative of the relevant population i.e. the data is representative of the guests who visit Little Town Café.
The validity of a data set is the extent to which the data set is appropriate to the research question and the strength of association of with the variables being investigated (Curwin & Slater, 2008). In the case of Little Town café, the researcher aims at investigating the average number of guests during the tourist season in order to plan the number of staff required. The data set contains the guest that visited Little Town Café during the tourist season over a three-year period and thus it is valid.
Reliability refers to the truthfulness, accuracy and consistency of the data set, this means that the same data set would be obtained if the process was repeated (Curwin &Slater, 2008). The data set of Little Town Café contains the number of guests that visited the café over the three year period during the tourist season. The same data would be obtained if the process was repeated meaning that the data set is reliable.
Lucey (2003) observes that the validity and reliability of the data set can be affected by factors such relating to the researcher, the respondents, the social context, and the methods of data collection and analysis. Researcher bias and incompetency can reduce the validity and reliability of the data set. Respondents may also provide different responses to male and female respondents (Lucey, 2003). The researcher can keep the researcher effect to a minimum by being aware of the possibility of bias and through proper training (Lucey, 2003). The researcher must also learn how to develop rapport with the respondents in order to obtain more accurate information (Lucey, 2003).
The respondents can reduce the validity and reliability of the data by giving untruthful responses. Bias may be introduced by respondents who want to make the situation either look better or worse than it really is (Lucey, 2003). In addition, respondents may give false information in an attempt to respond in a way they believe the researcher expects them to respond (Lucey, 2003). For instance, guests who may want to impress the researcher may rate the hotel to be better than it actually is. The researcher can reduce this bias by carefully reviewing extreme responses and a careful selection of respondents.
The social context under which the data is gathered may affect the validity and reliability of the data. For instance, respondents may behave differently when they are in a group than when they are alone. To reduce this bias, the researcher can interview the same respondent in different contexts for instance, privately, and make note of any variations in responses. In addition the researcher should specify the social, physical, and interpersonal context in which the data was gathered (Lucey, 2003).
The data collected for Little Town Café was collected through observation of the actual guests who visited. The data collected was quantitative and free from researcher bias, respondent bias, or influenced by the social context in which it was gathered. Therefore, the data is valid and reliable.
I selected a bar graph to present the information because it allows for easy comparison between the different periods at a glance i.e. by looking at the height of the bars you can easily make comparisons of the number of guests between 2012 and 2014.
The average numbers of guest were 86, 77 and 92, with a standard deviation of 22, 19, and 22 guests in 2012, 2013, and 2014 respectively. The median numbers of guests were 91, 86, and 96 in 2012, 2013, and 2014 respectively. The most common number of guest (mode) in 2012 was 107, while in 2013 the most common number of guests was 94, while in 2014; the most common number of guests was 91.
The mean is the average that is calculated by summing up all the observations in the data set, divided by the number of observations (Lucey, 2003). The median is the middle value found when data in a data set is arranged either in ascending or descending order. The mode is the most frequently occurring value in the data set (Lucey, 2003). The standard deviation is a measure of dispersion that shows how the data is scattered around the mean (Lucey, 2003). A sample with a high standard deviation is said to be less homogenous or less consistent (Lucey, 2003). The standard deviation is obtained by finding the square root of the variance. Variance is calculated by squaring the deviations of individual observations from the mean and dividing result by the number of observations.
Reference List
Curwin, J., & Slater, R. (2008). Quantitative methods for business decisions. London: South-
Western Cengage Learning.
Lucey, T. (2003). Quantitative techniques. London: Thomson.
