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Flashcards in Data Analysis and Research Communication Deck (30)
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1

Broadly, what types of data analysis are there?

- Quantitative
- Numerical continuous or categorical data that is analysed through statistical techniques

- Qualitative
- Qualitative and textual data that is analysed through content analysis e.g. interview data
- Qualitative data can be converted into categorical numeric data and can be analysed through statistical techniques

2

What are the components of the circle of analysis?

- Description
- Interpretation
- Conclusion
- Theorisation

3

What are the steps to preparing quantitive data?

* 1. Editing
* 2. Data entry and coding
- 3. Data checking and verification
* 4. Handling missing data

4

What is editing when preparing quantitive data?

The process of checking the completeness, consistency and legibility of data and making the data ready for coding

5

What are the components of data entry and coding when preparing quantitive data?

* Coding
* Codes
* Code book

6

What is coding?

The process of assigning a numerical score to other character symbols to previously edited data

7

What are codes?

- Rules for interpreting, classifying, and recording data in the coding process
- The actual numerical or other character symbols assigned to the raw data

8

What is a code book?

Identifies each variable in a study and gives the variable’s description, code name, code for each response and position in the data matrix

9

How can missing data be handled?

- List wise deletion
- The entire record for a respondent that has left a response missing is excluded from use in statistical analysis
- Pure-wise deletion
- Only the actual variables for a respondent that do not contain information are eliminated from use in statistical analysis

- Imputation of values (various means)

10

What is the unit of measurement in quantitative research?

In quantitative analysis the unit of measurement is the variable

11

Why is the variable level important?

The different levels of variables (nominal, ordinal and interval) are important because they determine the kind of analysis that can be carried out on each variable or with each variable

12

What kinds of data analysis statistics are there?

* Descriptive Statistics
* Inferential Statistics

13

What are descriptive statistics?

- Summarising statistics
- Each variable in the data gathered can be described using them
- Each variable can be described in a number of different ways
- Most generally used are frequencies, ranges, means, modes, medians and std.

14

What are inferential statistics?

- Statistical inference uses the data gathered from a sample population to draw conclusions (or inferences) about the population form which the sample was drawn

- When a researcher engages in quantitative analysis using inferential statistics, the sampling method used in selecting participants for the research project becomes critical

- Probability sampling methods must be used. These sampling methods are designed to minimise biases and to ensure that the sample is as representative as possible of the population of the study

15

In hypothesis testing what is the hypothesis?

The hypothesis is an unproven proposition or supposition that tentatively explains certain facts or phenomena - it is a statements of assumption about the nature of the world

16

What is a null hypothesis?

- A null hypothesis is a statement about the status quo that asserts that any change form what has been thought to be true will be due entirely to random sampling error
- The true purpose of setting up the null hypothesis is to provide an opportunity for nullifying it - we can never prove something to be true but we can always prove something to be false

17

What is an alternative hypothesis?

- The alternative hypothsis states the opposite of the null hypothesis
- These are stated in research articles and proposals

18

What are the steps to hypothesis testing?

- Hypotheses are dervied from the research objectives as per the hypothetic-deductive method

- A sample is obtained and the relevant variables are measured

- The measured sample value is compared to the value either stated explicitly or implied in the hypothesis
- If the value is consistent with the hypothesis, the hypothesis is supported

19

What types of hypothesis testing are there?

- Hypothesis about differences
- Examining the differences between two or more variables (multivariate tests of differences)
- Tests of hypotheses involving two variables (bivariate tests of differences)

- Hypotheses about relationships
- Examining the relationship between two or more variables (multivariate tests of association)
- Tests of hypotheses involving two variables (bivariate tests of association)

20

What kinds of analysis are there?

* Univariate analysis
* Bivariate analysis
* Multivariate analysis

21

What is univariate analysis?

- The use of one variables in analysis i.e. analysis conducted on one variable
- e.g. frequencies

22

What is bivariate analysis?

- The use of two variables in analysis i.e. analysis conducted on two variables
- e.g. chi-square tests, one-way ANOVA, t-tests, correlation and simple regression

23

What is multivariate analysis?

- The use of three or more variables in analysis i.e. analysis conducted on more than two variables
- e.g. multiple regression analysis

24

What is most important about qualitative research data analysis?

Analysis is continuous, occurs during data collection

25

What questions are asked in qualitative research data analysis?

- How am I going to make sense of these data?
- Why do people behave as they do
- What else do i need to know?
- What are the patterns from the data collected

26

What is the method to qualitative research data analysis?

- Transcribe data
- Organise data
- Codes to field notes
- Marginal reflections
- Sort and sift
- Define patterns, processes, differences
- Generalise
- Relate to our construct theories

27

What are the three important steps to qualitative research data analysis?

- 1. Familiarity with the data
- Reading
- Memo writing
- 2. Examine the data in depth
- Provide detailed descriptions
- 3. Categorise and code data into themes
- Note repetitive events
- Define criteria for classifying events into categories

28

What are the important aspects about communicating research?

- Each report will have a different purpose which will influence the style
- Some reports are descriptive in nature. The purpose of these is to provide descriptive details on a particular area of interest
- Some reports can be both diagnostic and prescriptive in nature. The purpose of these is to ‘sell and idea’ and identify or determine the nature of the problem and provide the final solution

29

What is a dichotomous variable?

a variable with only two values

30

What is an intervening variable?

the means by which the independent variable affects the dependent