Improving tourists' satisfaction and intention to revisit the festival is an ongoing area of interest to the tourism industry. Many organizers at the festival site strive very hard to attract and retain attendees by investing heavily in their marketing and promotion strategies for the festival. To meet this challenge, the advanced analytical model though data mining approach is proposed to answer the following research question: What are the most important factors that influence tourists' intentions to revisit the festival site? Cluster analysis, neural network, decision tree, stepwise regression, polynomial regression, and support vector machine are applied in this study. The main goal is to determine what it takes not only to retain the loyalty attendees, but also attract and encourage new attendees to be back at the site.

Texas is one of about 30 states that has recently passed laws requiring voters to produce valid IDs in an effort to avoid illegal voters. This new regulation, however, might negatively affect voting opportunities for students, low-income people, and minorities. To determine the actual effects of the regulation in Dallas County, voters were surveyed when exiting the polling offices during the November midterm election about difficulties that they might have encountered in the voting process. The database of the voting history of each registered voter in the county was examined, and the data set was converted into an analyzable structure prior to stratification. All of the polling offices were stratified by the residents' degrees of involvement in the past three general elections, namely, the proportion of people who have used early election and who have at least voted once. A two-phase sampling design was adopted for stratification. On election day, pollsters were sent to select polling offices and interviewed 20 voters at a selected time period. The number of people having difficulties was estimated when data was collected.

Respondent Driven Sampling (RDS) is both a sampling method and a data analysis technique. As a sampling method, RDS is a chain referral technique with strategic recruitment quotas and specific data gathering requirements. Like other chain referral techniques (for example, snowball sampling), the chains and waves are the start point to conduct analysis. But building the chains and waves still would be a daunting task because it involves too many transpositions and merges. This paper provides an efficient method of using Base SAS^® to build up chains and waves.

Using PROC TRANSPOSE to make wide files wider requires running separate PROC TRANSPOSE steps for each variable that you want transposed, as well as a DATA step using a MERGE statement to combine all of the transposed files. In addition, if you want the variables in a specific order, an extra DATA step is needed to rearrange the variable ordering. This paper presents a method that accomplishes the task in a simpler manner using less code and requiring fewer steps, and which runs n times faster than PROC TRANSPOSE (where n=the number of variables to be transposed).

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