week 6

Question 1

thematic coding

Answer 1

• Begin with as few pre-determined ideas as possible
• Coding is shaped by interpretation of the data (what does this mean?)
• Characterised by different types and levels of codes
• Categories of data are created and refined through constant comparison to develop themes
• Analysis and data collection are iterative

Question 2

memo writing

Answer 2

Make field notes/memos throughout the entire data collection:
i Process of data collection - reflect on what worked or didn’t work

Question 3

analysis considerations

Answer 3

chronology
key events
various settings
people 
process
issues

Question 4

developing themes

Answer 4

code
category
theme

Question 5

Name and describe the three steps in grounded theory data analysis.**

Answer 5

1. Initial/Open coding: a literal line-by-line reading andi nterpretation of salient categories
2. Axial Coding: grouping of the first order, or open codes into coherent categories and sub-categories
   This involves synthesising the most significant/frequently used words/experiences into formal codes to create categories/ conceptual similarities.
3. Selective Coding: selecting and validating major categories that outline relationships and interactions between the codes

Question 6

constant comparison

Answer 6

• different people views, actions etc
• data from the same individual at different points in time
• incident by incident,
• data categories,
• category with other categories.

Question 7

consensus coding

Answer 7

Measure of reliability of coding
 Two coders independently code data in same way
 Way of assessing validity or accuracy in data
 Improves consistency and quality of analysis
 Have two coders or team code same few interviews  Come to consensus on codes/revise definitions

Question 8

Name and describe content analysis**

Answer 8

The objective of qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results (summative/quantitative
Content analysis is the procedure for the categorisation of verbal or behavioural data for the purpose of classification, summarization and tabulation and abstraction

Question 9

inductive content analysis steps

Answer 9

1. open coding: creating heads while reading text
2. creating categories: grouping headings into higher order headings
3. abstract themes

Question 10

deductive content analysis

Answer 10

• Often used in cases where the researcher wishes to retest existing data in a new context
•  The researcher typically begins the analysis, using the pre-existing categories (analysis matrix) imposed by the theory or previous research findings, which is clearly the instance of deduction.

Question 11

challenges with qualitative analysis

Answer 11

unexpecting challenging

time consuming

Question 12

What is the difference between a ‘target population’ and an ‘accessible population’?*

Answer 12

Target population – the population to which the researcher ideally wants to generalise study results to
Accessible population – the population to which the researcher has access to (e.g., participants from a specific region or patients from one clinical site)

Question 13

2. In the context of quantitative sampling, what does ‘representation’ refer to?**

Answer 13

the extent to which a sample or subgroup is representative of the population
Demographic characteristics: age, gender, ethnicity
• Personal characteristics
• Specific traits
• Diagnosis / clinical presentation
• Accessibility to participants
• Ethical issues (vulnerable group? Children?)

Question 14

whats sample error

Answer 14

• The change occurance that a randomly selected sample is not representative of the population due to errors inherent in the sampling techniquw
• Random nature of errors
• Controlled by selecting large samples that are representative of the population (eg using government census data to ensure representation)

Question 15

whats sample bias

Answer 15

• Some aspect of the researchs sampling design creates bias in the ata
• Non random nature of errors
• Controlled by being aware of sources of sampling bias and avoiding them
• Example; surveying only students who attend additional tutorial session in a specific tasks.

Question 16

general rules for sample size

Answer 16

• Include/ recruitment many participants as possible
• 30 participants per group for correlation, casual comparative and true experiment design are required
• Should strive for 10% to 20% of the population for descriptive designs

Question 17

whats random sampling

Answer 17

Selecting participants so that all members of a population have an equal & independent chance of being selected

Question 18

whats advantages and disadvantages of random sampling

Answer 18

Advantages:
- Easy to conduct
- High probability of achieving a representative sample
- Meets assumptions of many statistical procedures
Disadvantages
- Identification of all members of the population can be difficult/challenging/not feasible/expensive
- Contacting all members of the sample can be difficult.

Question 19

whats stratified random sampling

Answer 19

• Selecting participants so that relevant subgroups in the population are guaranteed represnetaiton
• Examples of strata variables: gender, education level, occupation

Question 20

difference between proportional and non proportional stratified random sampling

Answer 20

• Proportional- same proportion of subgroups in the sample as in the population eg if a population.
• Non proportional- different, often equal proportions of subgroups. Selecting the same number of children from each five grade in school even though there are different numbers of children in each grade

Question 21

advantaged and disadvantages of stratified random sampling

Answer 21

Advantages:
- More precise sample
- Can be used for both proportional and non proportional smaples
- Helps to ensure representation of subgroups in the sample
Disadvantages;
- Identification of all members of the population can be difficult
- Identifying members of all subgroups can be difficult

Question 22

whats cluster sampling

Answer 22

• Selecting participants by using groups that have similar characteristics and in which participants can be found
• clusters are locations within which an intact group of members of the population can be found
• examples: neighbours

Question 23

advantages and disadvantages of cluster sampling

Answer 23

Advantages
• Very useful when popualations are large and spread over a large geographic region (e.g., state of Tasmania)
• Convenient & expedient
• Do not need the names of everyone in the population
Disadvantages:
- Representation is likely to become an issue
- Assumptions of some statistical procedures can be violated therefore will limit what data analyses can be completed

Question 24

whats systematic sampling

Answer 24

• Selecting every 5th or 10th participant from a list of the members of the population

Question 25

whats advantages and disadvantage of systematic sampling

Answer 25

Advantage:
- Very easily done
Disadvantage:
- Susceptible to systematic exclusion of some subgorups
- Some members of the population do not have an equal change of being inlucdede

Question 26

three techniques for non random sampling (non probability sampling)

Answer 26

convenience smapling
purposive sampling
quota sampling

Question 27

whats purposive sampling

Answer 27

selection based on the researches experience and knowledge of the individual being sample. Need for clear criteria for describing and justifying the sample used. Concerns related to represnetaiotn and gernalisabilty are considered to be limitation of this approach.

Question 28

whats quota sampling **

Answer 28

selection based on the exact characteristics and quotas of participatns in the sample when it is impossible to list all members of the population. Concerns with accessibility, representation and gerneralisabilty are present.