Contents

122 PART II: Descriptive Methods(Whitlock, Powers, & Eckenrode, 2006). They derived their codes from interviewswith self-injurers and from observations of messages posted on the Internet.They then examined 3,219 Internet postings from 10 Internet messageboards over a 2-month period and coded, or categorized, the content into differentthemes, such as motivation for self-injury and methods of concealing theirbehavior. Similar to analysis of narrative records, data reduction using codingallows researchers to determine relationships between specific types of behaviorand the events that are antecedents of these behaviors. Whitlock and hercolleagues, for instance, identified “triggers” of self-injury behaviors in theircoding and were able to identify the proportion of messages that described eachtrigger. Based on their coding, they observed that “conflict with important others”was the most frequent trigger (34.8%) of self-injury. By counting the occurrenceof these triggers, these investigators moved from qualitative coding of thedata to quantitative data analysis.Quantitative Data Analysis• Data are summarized using descriptive statistics such as frequency counts,means, and standard deviations.• Interobserver reliability refers to the extent to which independent observersagree in their observations.• Interobserver reliability is increased by providing clear definitions aboutbehaviors and events to be recorded, by training observers, and byproviding feedback about the accuracy of observations.• High interobserver reliability increases researchers’ confidence thatobservations about behavior are accurate (valid).• Interobserver reliability is assessed by calculating percentage agreement orcorrelations, depending on how the behaviors were measured and recorded.The goal of quantitative data analysis is to provide a numerical, or quantitative,summary of observations in a study. An important step is to calculate descriptivestatistics that summarize the observational data, such as relative frequency,means, and standard deviations. Another important aspect of analyzingobservational data is assessing the reliability of the observations. Unless theobservations are reliable, they are unlikely to tell us anything meaningful aboutbehavior. We will describe each of these aspects of quantitative data analysisin turn.Descriptive Statistics The type of descriptive statistics used to summarizeobservational data depends on the scale of measurement used to record thedata. As we saw, a nominal scale of measurement is used when behaviors andevents are classified into mutually exclusive categories. Because a frequentlyused nominal measurement is whether a behavior is present or absent, the mostcommon descriptive statistic for the nominal scale is relative frequency. To calculatea relative frequency the number of times a behavior or event occurs is talliedand then divided by the total number of observations. Relative frequencymeasures are expressed as either a proportion or a percentage (by multiplyingthe proportion by 100). We mentioned earlier that Whitlock and her colleagues