Four of the main techniques used to analyze surveys are frequencies, crosstabulations, 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 who responded to that question, if missing responses are removed. Frequencies can be used for single or multiple variables and for both descriptive and evaluative research. For example, when looking at gender, one might look at the percentage of the sample that are male and the percentage that are female; when looking at age, one could look at the percentage of people in each of the age groups. Another example of using frequencies is to determine 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. Additionally, the frequencies of categories for a variable can be combined to create a cumulative percentage for certain types of variables where categories can be grouped, such as age or how much someone has spent on something.

One drawback of this approach is that if there are multiple choices for different categories of a variable, the percentages will add up to more than 100%, which could make it difficult to compare responses to that variable across samples. Another disadvantage is when there are multiple questions, as there will be multiple charts of frequency and percentage and cumulative percentage, which can be unwieldy for presenting the data. Also, the frequency procedure does not work well when there are numerous categories for ordinal or Likert-type variables.

Cross tabulations involve performing a cross tabulation of two or more variables to look at the relationship between those variables. These are used in explanatory and evaluative research. For example, one might cross-tabulate a demographic variable, such as age or gender, and the response to a question to see if there are any differences between groups in their response 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 you want to make (i.e., whether you want to compare the demographics of a particular movie or whether you want to compare the film preferences of members of a demographic group). In addition to two-way crosstabulations, a three-way or more crosstabulation can be used, if the sample size is large enough. For example, one can see the gender and age breakdown of different movies.

The advantage of using cross tabulations is that differences between different groups can be compared and the results used to help explain these differences. Cross tabulations can also be used to compare different groups of users and clients in evaluative research.

The disadvantage of cross tabulations 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 against each other. Also, not all crosstabulations may be significant, although it may not be clear which are significant or not until the crosstabulations have been performed. Another drawback is that the number of items that can be tabulated against each other may be limited if the sample size is small.

Means, involves finding the means 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 scales or ordinal data. There is no point in using means if numerical codes have been used for nominal variables.

The advantage of using a mean is that it 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 when ranking or grading something.

However, a disadvantage of using media could occur if the average has resulted in 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 make it 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, such as in a few people with a very high income that skews the entire distribution, so that the average income is much higher for everyone. In such cases, a median might be a more accurate statistic, since 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 scatterplot. The bar graph, 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 graph and the category measured on the other.

If two variables are tabulated against 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 make up the total. stack for each of the categories into which a variable is divided. An additional variable may be shown in a stack next to each other, such as for a study conducted in two cities or in two different years.

The advantage of using a graph 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 disadvantage of using a graph is that the graph could be misleading based on how it is drawn to show differences between groups. For example, if there is a large difference between the groups, but the percentage categories on the side are close together, this could downplay the differences; or, conversely, if there are only small differences, spacing out the percentage categories could make the differences appear larger than they are. So, too, it can be hard to tell what the actual percentages are unless they’re written on or above the bars.

Pie charts are a type of chart that divides the number or percentages of categories or responses for 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 doesn’t work well when there are multiple responses such that the total is greater than 100%.