Legal Research Methodology Sample

[Alarico Boyett]

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Alternatively, lawyers may need legal research to provide clients with accurate legal guidance. In the case of law students, they often use legal research to complete memos and briefs for class. But these are just a few situations in which legal research is necessary.

1. Identifying the legal issue is not so straightforward. Legal research involves interpreting many legal precedents and theories to justify your questions. Finding the right issue takes time and patience.

2. There's too much to research. Attorneys now face a great deal of case law and statutory material. The sheer volume forces the researcher to be efficient by following a methodology based on a solid foundation of legal knowledge and principles.

3. The law is a fluid doctrine. It changes with time, and staying updated with the latest legal codes, precedents, and statutes means the most resourceful lawyer needs to assess the relevance and importance of new decisions.

You will never know what to research if you don't know what your legal issue is. Does your client need help collecting money from an insurance company following a car accident involving a negligent driver? How about a criminal case involving excluding evidence found during an alleged illegal stop?

Don't cast your net too wide regarding legal research; you should focus on the relevant jurisdiction. For example, does your case deal with federal or state law? If it is state law, which state? You may find a case in California state court that is precisely on point, but it won't be beneficial if your legal project involves New York law.

When it comes to online research, some people start with free legal research options, including search engines like Google or Bing. But to ensure your legal research is comprehensive, you will want to use an online research service designed specifically for the law, such as Westlaw. Not only do online solutions like Westlaw have all the legal sources you need, but they also include artificial intelligence research features that help make quick work of your research

Now that you have gathered the facts and know your legal issue, the next step is knowing what to look for. After all, you will need the law to support your legal argument, whether providing guidance to a client or writing an internal memo, brief, or some other legal document.

Why? Because secondary sources provide a thorough overview of legal topics, meaning you don't have to start your research from scratch. After secondary sources, you can move on to primary sources of law.

Once you find a helpful case, you can use it to find others. For example, in Westlaw, most cases contain headnotes that summarize each of the case's important legal issues. These headnotes are also assigned a Key Number based on the topic associated with that legal issue. So, once you find a good case, you can use the headnotes and Key Numbers within it to quickly find more relevant case law.

Keep in mind, though, that legal research isn't always a linear process. You may start out going from source to source as outlined above and then find yourself needing to go back to secondary sources once you have a better grasp of the legal issue. In other instances, you may even find the answer you are looking for in a source not listed above, like a sample brief filed with the court by another attorney. Ultimately, you need to go where the information takes you.

The simplest way to find out if something is still good law is to use a legal tool known as a citator, which will show you subsequent cases that have cited your source as well as any negative history, including if it has been overruled, reversed, questioned, or merely differentiated.

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.

Definition 2: An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

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.

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.

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.

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.

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 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.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

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.

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