## 3 Research Design  
### Data Selection and Data Preprocessing  
The data analyzed in this study is from ALA Joblist's database [19] from 2009 through 2014. According to [PERSON]'s work, ALA Joblist covers 23% of available academic library positions, ranked only next to Academic Employment Network [18]. Although directly gathering data from institutions may provide more representative data, it would be much too time-consuming.  
The data source, however, brings deviation between the end result of the study and actual job market. For example, [PERSON]'s work shows that ALA Joblist covers a remarkably higher percentage of job ads from masters (20%) and doctoral (27%) institutions than from associates (0%) and baccalaureate (10%) institutions [18]. Another subsequent potential limitation is that some job ads posted on ALA Joblist may point to a Web page where the position is more fully described. These Web pages are typically removed from the Web after the position is filled, making the information more difficult to obtain [20].  
With a total of 9573 job ads posted on ALA Joblist from 1 January 2009 to 25 June 2014, the data include both full-texts and basic information showing brief descriptions. As the first step of the data analysis, Figure 1 shows the path and procedure of the data processing. After discarding job ads from Canada, there are 9198 job ads left as the total of data records analyzed directly in this study using statistical computing techniques. Their full texts are preprocessed further for content analysis.  
In the preprocessing stage, the authors transition each full-text ad into several record units (see Figure 2). In his seminal text on content analysis, [PERSON] identifies three separate units of analysis that must be considered in content analysis: sampling units, context units, and recording units [12]. In this study, sampling units are full-texts of all 9198 job ads; context units are blocks describing requirements; recording units are segments in context units, each of which is supposed to describe a competency. The example for context units and recording units is shown in Figure 3.  
To extract context units from a sampling unit, the authors divide it into sentences and build a rule-based classifier, which is designed to determine whether a sentence belongs to a context unit. For example, a context unit usually begins with a \"Required Qualifications.\" in a single line and ends before an url link for application (See Figure 3). The classifier gets a hit rate of 90% and a recall rate of 84% in sentences from 10 randomly picked job ads.  
After that, the authors discard job ads without context unit, in which no job requirements are mentioned, and divide the remaining sentences into recording units (recording units may be divided by semicolons). Via this process, the authors try to avoid mixing multiple competencies in one recording unit.  
Figure 1: Overview of data processing.  
Finally the authors transfer recording units from text into \"bag-of-words\" (a sparse vector of occurrence counts of words). To unify the form of word set, words are stemmed with toolkit in nltk and irrelevant words (stop words e.g.) are removed.