Listed here are a few of the more obvious choices for analysis.
Choices for data analysis are circumscribed by Decision Board 2 selections. For instance, if you chose field research, you will most likely be drawn to narrative analysis. Multiple types of analysis may be required. For instance, if you want to run an involved survey questionnaire with a representative sample, you may want to select test of statistical inference, correlation or regression and descriptive statistics as your types of analyses. Or if you are intending to do a case study on an e-business, you may need descriptive, thematic and narrative analyses.
Test of statistical inference
Descriptive and inferential are the two general types of statistical analyses in quantitative research. Descriptive includes simple calculations of central tendency (mean, median and mode), spread (quartile ranges, standard deviation and variance) and frequency distributions displayed in graphs. Inferential includes more complex calculations of statistical significance usually associated with probability-based analysis. A t-test is a typical example of inferential analysis.
Correlation measures the association between variables, usually as a numeric value signifying the degree to which changes in the values of a dependent variable (Y) increase or decrease in parallel with changes in the values of an independent variable (X). Linear regression analysis can be used to make short-range predictions, but the associations are only as strong as the arguments demonstrating their supposed relationship. Any set of values could be shown to strongly associate with another set of values, regardless of the senseless nature of the association. A typical example of a senseless (spurious) correlation is the strong association between ice cream sales and drowning deaths; another variable, hot temperatures, is actually impacting the association between ice cream sales and drowning deaths.
Descriptive analysis is the chameleon of research analysis: it can take on many forms, from descriptive statistical graphic displays and number summaries to involved interpretive accounts. It is concerned with the “what is” as opposed to the “why” and involves drawing conclusions, discerning patterns and assessing the meaning and implications of the data/information.
After carefully observing a social science-related phenomenon or “text” or “body of knowledge,” a plausible, well-reasoned, descriptive account of the various meanings or interpretations of the data is produced. The analysis often takes into account the context in which the data were produced, who produced it and under what circumstances. Frequent and regular references to information sources typify descriptive analysis.
Thematic analysis is one of the most popular types of qualitative analysis. It is also easy to use. It simply involves the skilled ordering of the findings into descriptive categories or themes around which most or all of the main elements of the data results can be presented. For a qualitative study on dumpster diving, for instance, the following themes could be used as descriptive categories in which to present the rich and detailed data: SUSTAINABLE LIFESTYLE, ANTI-CAPITALIST and COMMUNAL ORIENTATION.
Narrative analysis is a form of inquiry based on a descriptive account of a group of people such as midwives or an extraordinary individual such as Nelson Mandela or the experience of surviving cancer, drawn from a collection of narrative accounts (diaries, letters, photos, poems…).
It values the particular and the subjective, lived experience in a workplace or in an unusual circumstance such as a natural disaster. The researcher analyzes the form, content and contexts within which the story unfolds, structured either chronologically or as critical incidents. The “narrative” emerges as a rich, detailed account that is unique to the subject(s) under analysis and specific to the researcher’s investigative talents.