Week 70 (17.8.08)

Learn

Chapter 12 - Analysing Quantitative Data

• Data for quantative analysis can be collected and subsequently coded at different levels of numerical measurement.
• Data are entered for computer analysis as a data matrix in which each column usually represents a variable and each row a case.
• All data should, with few exceptions, be recorded using numerical codes to facilitate analyses.
• Where possible, you should use existing coding schemes to enable comparisons.
• For primary data you should include pre-set codes on the data collection form to minimise coding after collection. For variables where responses are not known, you will need to develop a codebook after data have been collected for the first 50 to 100 cases.
• You should enter codes for all data values, including missing data.
• Your data matrix must be checked for errors.
• Your initial analysis should explore data using both tables and diagrams. Your choice of table or diagram will be influenced by your research question(s) and objectives, the aspects of the data you wish to emphasise, and the level of measurement at which the data were recorded. This may involve using:

1. tables to show specific values
2. bar charts, multiple bar charts, histograms and, occasionally, pictograms to show highest and lowest values
3. line graphs to show trends
4. pie charts and percentage component bar charts to show proportions
5. box plots to show distributions
6. scatter graphs to show relationships between variables

• Subsequent analyses will involve describing your data and exploring relationships using statistics. As before, your choice of statistics will be influenced by your research question(s) and objectives and the level of measurement at which the data were recorded. Your analysis may involve using statistics such as:

1. the mean, median and mode to describe the central tendency
2. the inter-quartile range and the standard deviation to describe the dispersion
3. chi square, Cramer's V and phi to test whether two variables are significantly associated
4. Kolmogorov-Smirnov to test whether the values differ significantly from a specified population
5. t-tests and ANOVA to test whether groups are significantly different
6. correlation and regression to assess the strength of relationships between variables
7. regression analysis to predict values

Unlearn

NIL. Mostly new learning

Relearn

As for the BAP assignment, the survey i undertook are only quantitative questionnaires to understand the perceptions of SEs on leadership across supervisory levels in Pfizer. With these results, i am going to analyse these quantitative data using tables and diagrams (bar charts) to show specific values in answering my research question(s) and objectives. As this research is not looking at any relationship, therefore, no correlation or regresssion analysis is required.