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Analyzing and Presenting Qualitative and Quantitative Data

This chapter provides an overview of dealing with qualitative and quantitative data in research. It covers the process of creating a data matrix, coding data, selecting appropriate tables and diagrams, and choosing the most suitable statistics for analysis. The chapter also explores data preparation, types of data, data layout, coding techniques, and methods to check data for errors. Additionally, it discusses exploring and presenting data, comparing variables, describing data using statistics, examining relationships and trends, and assessing the strength of relationships. The chapter concludes with an overview of qualitative analysis, including categorization, data unitization, recognizing relationships, developing hypotheses, and the analytical aids used in the process.

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Analyzing and Presenting Qualitative and Quantitative Data

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  1. Chapter 11-13 Dealing with data qualitative data The main report

  2. Objectives • To recognize different types of data and understand the implications of data type for subsequent analyses. • To create a data matrix and to code data for analysis by computer. • to select the most appropriate tables and diagrams to explore and illustrate different aspects of your data.

  3. Cont. • To select the most appropriate statistics to describe individual variables • To examine relationships between variables and trends in your data. • To interpret the tables, diagrams and statistics that you use correctly

  4. General process • Preparing your data for analysis by computer • Choosing the most appropriate tables and diagrams to explore and present your data • Choosing the most appropriate statistics to describe your data • Choosing the most appropriate statistics to examine relationships and trends in your data

  5. Preparing data for analysis • Type of data (level of numerical measurement) • Format in which your data will be input to the analysis software • Impact of data coding on subsequent analyses (for different data types) • Need to weight cases • Methods you intend to use to check data for errors

  6. Data type • Categorical data refer to data whose values cannot be measured numerically but can be either classified into sets (categories). • Descriptive data • Ranked data

  7. Data type • Quantifiable data are those whose values you actually measure numerically as quantities. • Continuous data • Discrete data

  8. Data layout • Virtually all analysis software will accept your data if they are entered in table format. This table is called a data matrix. • A table must has a survey form identifier

  9. Coding • Coding quantifiable data • Coding categorical data •Coding at data collection •Coding after data collection • Coding missing data

  10. Exploring and presenting data • Exploring and presenting individual variables • To show specific values • To show highest and lowest values • To show the trend • To show proportions • To show the distribution of values

  11. Comparing variables • To show specific values and interdependence • To compare highest and lowest values • To compare proportions • To compare trends and conjunctions • To compare totals • To compare proportions and totals • To compare the distribution of values • To show the relationship between cases for variables

  12. Describe data using statistics Describing the central tendency • To represent the value that occurs most frequently • To represent the middle value • To include all data values

  13. Describe data using statistics Describing the dispersion • To state the difference between values • To describe and compare the extent by which values differ from the mean

  14. Examining relationships, differences and the trends • Testing for significant relationships and differences • Type I and Type II errors • To test whether two variables are associated • To test whether two groups are different •Categorical data •Quantifiable data • To test whether three or more groups are different

  15. Cont. Assessing the strength of relationship • To assess the strength of relationship between pairs of variables • To assess the strength of a cause-and-effect relationship between variables • To predict the value of a variable from one or more other variables

  16. Cont. • Examining trends • To compare trends • To determine the trend and forecasting

  17. Chapter 12 An overview of qualitative analysis • Understanding the characteristics of language • Discovering regularities • Comprehending the meaning of text or action • Reflection

  18. An overview of qualitative analysis • Categorisation • “Unitising” data • Recognizing relationships and developing categories • Developing and testing hypotheses • The interactive nature of the process • Analytical aids 􀂾Summaries 􀂾Self-memos 􀂾Researcher’s diary

  19. Strategies for qualitative analysis • Using a theoretical or descriptive framework • Exploring without a predetermined theoretical or descriptive framework

  20. Deductive based analytical strategies and procedures • Pattern matching • Explanation building • Impact of a deductive approach on the analysis process

  21. Chapter 13 The main report

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