Connaway and Powell Chapter 9
Statistical Analysis: a form of analysis that deals with developing and applying methods and techniques for the organization and analysis of data that is typically quantitative so that conclusions that are developed from this data can be evaluated in an objective matter.
Categories: Categories are necessary to organize the data so that it may be analyzed. The categories should be established before data collection actually occurs.
Wildemuth Chapter 29
Content Analysis: is a systematic and quantitative analysis of information. It should always follow the scientific method.
Latent Content: Latent content is difficult to quantitate. It is conceptual in nature and is not directly observable in analysis. It can often relate to emotions or other things that can’t be counted or observed quantitatively.
Wildemuth Chapter 30
Qualitative Content Analysis: A form of content analysis that focuses predominantly on speech, texts and their contexts. As it’s terminology insinuates, it is analysis that is of a qualitative nature rather than the quantitative nature that content analysis typically employs.
Conventional Qualitative Content Analysis: Qualitative content analysis where coding categories are created directly and inductively from raw data.
Wildemuth Chapter 31
Discourse Analysis: The analysis of many forms of discourse, including all spoken discourse, whether it be formal or not, and all written text of any kind. In ILS this could apply specifically to reference interviews as an example.
Hermeneutics: In hermeneutics a researcher has preunderstandings of a concept of interest and focuses on relationships between texts. This method of analysis is reflexive.
Wildemuth Chapter 32
Deductive Reasoning: A way in which one uses logic to come to conclusions that are true when using a broader group of tenets that are also (assumed) true.
Induction: The opposite of induction, Induction is when one observes specific facts and logically comes to a more general, broader conclusion.
Wildemuth Chapter 33
Measures of Central Tendency: These measures are most concerned with identifying a single number that can summarize an entire data set.
Measures of Dispersion: Quite the opposite of the measures of central tendency, the measures of dispersion show the outliers of your data set.
Wildemuth Chapter 34
Frequency Distribution: Frequency distribution is when the counts of how many cases there are in a category of a variable are organized into a table. This is done when analyzing categorical, or nominal, data.
Chi-Square Statistic: The Chi-square statistic is when you measure the difference between observation and what might be expected of a population in general.
Wildemuth Chapter 35
Codes: labels that are structured in syntax that are typically linked to data elements or chunks. Could also be defined as aggregates of data elements that the researcher or analyst views as coherent.
Optimal Matching Approach: An approach that involves direct comparison of the similarity (or lack thereof) of two sequences in completion.
Wildemuth Chapter 36
Correlation: A method of statistical analysis in which you examine the relationship that exists between two variables. When two variables are “perfectly correlated” then the variability of one variables is able to explain all the variability of the other variable.
Direction of the Relationship: The direction of the relationship between variables is indicated by the correlation statistic and it’s sign. When the sign is positive it means that as a variable increases the other variable will consequently also increase. If the sign is negative then as one statistic increases the other will decrease.
Wildemuth Chapter 37
Power: The ability to reject a hypothesis that is null at a significance level. It means that one can intuit a difference when one actually exists. Lack of power is a huge problem in social scientific research design.
Repeated Measures Design: When each participant is assigned to multiple conditions instead of just one.
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