Four of the main techniques used to analyze surveys are frequencies, crosstabs, means, and graphs. The techniques and their advantages and disadvantages can be described as follows.
Frequencies involve counting the number of instances of the categories for each variable and finding the percentages for each selected category based on the total number of people in the survey or those who answered that question, if missing answers are removed. Frequencies can be used for single or multiple variables and for both descriptive and evaluative research. For example, when considering gender, one could look at the percentage of the sample that are men and the percentage that are women; By looking at age, one could look at the percentage of people in each of the age groups. Another example of using frequencies is determining the percentage of people who choose an action in a forced choice question.
The advantages of using frequencies is that it is an easy way to provide an overview of the responses to a questionnaire. Also, the frequencies of the categories of a variable can be combined to create a cumulative percentage for certain types of variables, where the categories can be grouped, such as age or how much someone has spent on something.
A downside to this approach is that if there are multiple choices for different categories for a variable, the percentages will add up to more than 100%, which could make it difficult to compare responses to that variable between samples. Another disadvantage is when there are multiple questions, as there will be multiple graphs of frequency and cumulative percentages and percentages, which can be difficult to handle to present the data. Furthermore, the frequency procedure does not work well when there are numerous categories for ordinal or Likert-type variables.
Cross tables, involves performing a cross tabulation of two or more variables to observe the relationship between those variables. These are used in explanatory and evaluative research. For example, you could cross-tabulate a demographic variable, such as age or gender, and the answer to a question to see if there is any difference between the groups in their answer to that question, such as whether different movies attract more. to younger or older age groups or to men or women.
The choice of which total to use as a row or column percentage depends on the data, depending on the comparison that you want to make (that is, if you want to compare the demographics of a particular movie or if you want to compare the preferences of the movie for members of a demographic). In addition to two-way crosstabulations, a three-way or more crosstabulation can be used, if the sample size is large enough. For example, you can see the sex and age breakdown of different movies.
The advantage of using crosstabs is that the differences between different groups can be compared and the results can be used to help explain these differences. Crosstabs can also be used to compare different groups of users and customers in evaluative research.
The downside to crosstabs is that they can lead to a large number of tables when there are multiple responses, due to the many different ways that variables can be tabulated with each other. Also, not all crosstabs may be significant, although it may not be clear which ones are or are not significant until the crosstabs have been performed. Another disadvantage is that the number of items that can be tabulated with each other can be limited if there is a small sample size.
Means, involves finding the averages or averages for certain types of variables, and this method of analysis is used for all types of research: descriptive, explanatory and evaluative. However, means can only be used if there are ordinal data or scales. There is no point in using means if numeric codes have been used for nominal variables.
The advantage of using a mean is that you can provide a single statistic that can be used to compare different responses, rather than trying to look at a frequency table that shows the percentage of responses for different categories in ranking or rating something.
However, a disadvantage in the use of media could occur if the mean is the result of very different responses, such as when a large percentage of respondents strongly agree with something and a large percentage of respondents strongly disagree. This would be a bimodal distribution, and the average of the two results would seem like there is little opinion, because it averages the very different results. A mean is also a disadvantage when there are some extreme cases, as in some people with a very high income that skews the entire distribution, so the average income is much higher for everyone. In such cases, a median might be a more accurate statistic, as it more accurately reflects the midpoint of the data.
Charts are a way of presenting the results of an analysis in graphical form, such as a bar chart, stacked bar chart, pie chart, line chart, or scatter plot. The bar chart, also called a histogram, is the most common form used in leisure and tourism research, showing the number or percentage of cases on one axis of the chart and the measured category on the other.
If two variables are cross-tabulated with each other, these results can be displayed in a stacked bar chart, in which one variable is displayed in one color or pattern and the other variable is displayed in the other, so together they form the total. stack for each of the categories into which a variable is divided. An additional variable could be displayed next to a stack, as for a study conducted in two cities or in two different years.
The advantage of using a chart is that it visually shows the count or percentage differences in the results for different variables, rather than just looking at the count or percentages in a table. One downside to using a graph is that the graph could be misleading depending on how it is drawn to show the differences between groups. For example, if there is a large difference between the groups, but the percentage categories on the side are very close together, this could downplay the differences; Or conversely, if there are only small differences, spacing the percentage categories can make the differences appear to be greater than they are. So, too, it might be difficult to know what the actual percentages are unless they are written on or above the bars.
Pie charts are a type of chart that divides the number or percentages of categories or responses of a variable into sections of a pie chart. The advantage of a pie chart is that it is useful for showing the relative size of different responses when there is a significant total, such as 100%. However, a pie chart does not work well when there are multiple responses, so the total is greater than 100%.