Chapter 14 Analysing Quantitative Data

The analytical framework is presented in:
1. The conceptual framework.
2. The literature review.
3. The methodological framework.
4. The data analysis chapter or the thesis/the data analysis section of the report of the research.
The work of data analysis is a substantial project, what goes into the thesis or the report of the research is:
1. A synopsis of all of the analysed data.
2. A complete account of all of the analysed data.
3. A partial account of the analysed data.
4. A biased account of the analysed data.
The key findings, the key data and the key interpretations of the data are:
1. Not presented in the data analysis chapter or in the data analysis section of the report of the research.
2. Presented in the data analysis chapter or in the data analysis section of the report of the research.
3. Lost in all of the detail of the data analysis that is presented in the data analysis chapter or in the data analysis section of the report of the research.
4. Presented in the research methodology chapter of the thesis, in the research methodology section of the report of the research project.
The researcher uses the coding key to:
1. Understand the theory.
2. Understand the data.
3. Understand the research methodology.
4. Understand the data gathering methods.
Sometimes respondents don’t answer all the questions in a questionnaire, these non responses become:
1. Critical data.
2. Outlying data.
3. Missing data.
4. Significant data.
A computer software package, as well as helping analyse the data, also helps with:
1. The loss of data.
2. The location of data.
3. The management of data.
4. The complication of data.
When data has been loaded into a software package it has to be:
1. Covered.
2. Cleaned.
3. Closed.
4. Compiled.
Experimental design is:
1. The methodology used in survey research.
2. The methodology used in case study research.
3. The methodology used in ethnographic research.
4. The methodology used when conducting experiments.
Statistics are used in quantitative data analysis for two purposes:
1. Addition and subtraction.
2. Description and prediction.
3. Multiplication and division.
4. The calculation of means and modes.
Percentages:
1. Are the same as ratios.
2. Can be used to calculate the standard deviation.
3. Are a good approximation of the interquartile range.
4. A particular kind of scale with measures of 1 to 100.
Proportions:
1. Are a particular kind of scale with measure of 1 to 100.
2. Are used in calculating tests.
3. Are a type of ratio in which the denominator is the total number of cases.
4. Can be used to calculate the standard deviation.
Graphing data, like tabulating data:
1. Is helpful in the construction of a theoretical framework.
2. Is helpful in the calculation of the mean, mode and median.
3. Allows for the communication of the range and the interquartile range.
4. Allows for the communication of large quantities of data in a very succinct manner.
Visually displaying data can:
1. Make trends and patterns in the data apparent.
2. Serve to hide trends and patterns in the data.
3. Serve the development of the theoretical framework.
4. Serve the development of the methodological framework.
Line charts, bar charts and scattergrams are all examples of:
1. Graphs used in meteorology.
2. Graphs used in oceanography.
3. Graphs used in social science research.
4. Graphs used in Google map research.
A good way of learning how to present data is to:
1. Examine how other researchers present data, e.g. in journal articles.
2. Practice writing theoretical frameworks.
3. Read about the different research methodologies.
4. Study the different ways of gathering data.