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Analytic Comparative Research
A very large data set (e.g., contains thousands of cases) that is accessible in computer-readable form and is used to reveal patterns, trends, and associations between variables with new computer technology
Research comparing data from more than one time period and/or more than one nation
A research method for systematically analyzing and making inferences from text, visual images, or other sources
Geographical mapping strategies used to visualize a number of things including location, distance, and patterns of crime and their correlates
Research that seeks to understand the structure, nature, or scope of a nation’s or nations’ criminal justice systems or rates of crime
Descriptive Comparative Research
This federal law stipulates that all persons have a right to access all federal agency records unless the records are specifically exempted
Freedom of Information Act (FOIA)
The software tool that has made crime mapping increasingly available to researchers since the 1990s
Geographic Information System (GIS)
A method proposed by John Stuart Mill for establishing a causal relation in which the values of cases that agree on an outcome variable also agree on the value of the variable hypothesized to have a causal effect, whereas they differ in terms of other variables
Frequency graphs produced by Google’s database of all words printed in more than one third of the world’s books over time (with coverage still expanding)
Uses data from several sources to predict the probability of crime occurring in the future using the underlying factors of the environment that are associated with illegal behavior
Risk-Terrain Modeling (RTM)
Analysis of data collected by someone other than the researcher or the researcher’s assistant
Explores how cultures and nations deal with crime that transcends their borders
Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text. DescriptionSources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further. Three different definitions of content analysis are provided below.
Uses of Content Analysis
Types of Content Analysis There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings. Conceptual Analysis Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both. To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question. General steps for conducting a conceptual content analysis: 1. Decide the level of analysis: word, word sense, phrase, sentence, themes 2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.
3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.
4. Decide on how you will distinguish among concepts:
5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity. 6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding? 7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding. 8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified. Relational Analysis Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts. To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results. There are three subcategories of relational analysis to choose from prior to going on to the general steps.
General steps for conducting a relational content analysis: 1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:
4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models. Reliability and Validity Reliability: Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:
Validity: Three criteria comprise the validity of a content analysis:
Advantages of Content Analysis
Disadvantages of Content Analysis
ReadingsTextbooks & Chapters
Methodological Articles
Application Articles
Software
Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue. Websites
CoursesAt Columbia University’s Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods. |