Exam prep week 11 (Mixed methods research clinical governance) - wk 12 (Descriptive data analysis and findings) Flashcards

1
Q

What is mixed methods research?

A

“Research in which the investigator collects and analyses data, integrates the findings and draws inferences using both qualitative and quantitative approaches or methods in a single study”
(Tashakkori & Creswell 2007:265)

Studies that have used two designs, qualitative and quantitative

Offers a different approach

Not limited to constraints of one or the other

Journal Of Mixed Methods Research

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2
Q

Methodological Triangulation (Pluralism)

A

Triangulation : usually applied to qualitative research

Reduces error/increases rigor

Different methods of data collection used in same study

For example: interviews + participant observation + documentation + focus groups etc

Now used to denote single study using combination of research designs/paradigms

Triangulation (more commonly used term)= pluralism = Mixed Methods

Becoming much more common/popular especially in nursing research

Not always labelled as such

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3
Q

Terminology/Rationales Associated with Mixed Methods

A
Triangulation
Completeness
Off-setting weakness & providing stronger inferences
Answering different research questions
Wider explanation of findings
Broader illustration of data
Hypothesis development & testing
Instrument development & testing
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4
Q

REMEMBER!!

A

The appropriate research design is the one that will best answer the research question

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5
Q

Value of Mixed Methods

A

Potential for more complete & comprehensive research opportunity

Can give additional perspectives & insights beyond scope of single design

Weaknesses of one method may be counter-balanced by strengths of another

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6
Q

Limitations of Mixed Methods

A

Complex
Time consuming
Involved
Resource-intensive
Knowledge required of researchers- both qualitative & quantitative knowledge
Understanding & acceptance by research community needed

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7
Q

Action Research

A

Narrows gap between research and implementation of results

Research in the real world situation

Occurs in a spiral/cycle design

May use any type of research methodology

Emphasis on continual improvement

Limitation: may be weak experimental design

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8
Q

Delphi Technique

A

Uses expert opinion on a clinical practice problem

Non-empirical approach (ie no data collection)

Useful when experimental approach not feasible

Limitation: only represents opinion

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9
Q

Clinical Guidelines

A

Provide recommendations on clinical management

Generate national/international consensus on management principles

Allow for application of Research and EBP relating to a specific area of clinical practice

Are not mandatory

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10
Q

Tools to guide clinical decision making

A

Tools to guide clinical decision making eg standards, policies, & procedures, algorithms, clinical pathways, clinical guidelines

Assist to make appropriate decisions about patient care to result in best patient outcomes
Historically developed by individual/groups of experts

No process to determine validity/reliability

Evidence based practice has led to clinical guidelines being systematically developed

Based on best available research evidence

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11
Q

Definitions

A

Algorithms: clinical guidelines on flowchart

Clinical pathways: document essential steps in a clinical process eg COPD pathway

Clinical guidelines: systematically developed statements to assist clinical and patient decisions

Policies: written plans of an organisation’s official position eg Medication Administration Policy

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12
Q

Definitions (cont.)

A

Procedures: series of formal steps for performing specific tasks

Protocols: rigid, prescribed statements

Standards: accepted discipline-based principles for patient care processes

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13
Q

Policy

A

“A document that describes the organisation’s purpose or standard for a given customer process or issue, the expected outcome, guiding principles, roles and responsibilities, definition of terms within the document and references. Compliance with policies is mandatory”

(“Definitions of policy related documents within WA Health,” n.d.)

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14
Q

Procedure

A

A document that generally supports a policy by describing an instruction that clearly prescribes the actions of each step of a process to be taken and by whom.

(“Definitions of policy related documents within WA Health,” n.d.)

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15
Q

Characteristics of Effective Clinical Guidelines

A

No internationally recognised framework but general world-wide agreement

Key qualities for guidelines to be effective

Should be considered when appraising existing national guidelines before adapting to local situation and when developing new guidelines

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16
Q

NH&MRC Nine Guiding Principles for Effective Guideline Development

A
Validity
Reproducibility
Representativeness
Flexibility
Adaptability
Cost-effectiveness
Applicability
Reliability
Usefulness
17
Q

Clinical Governance

A

“A system through which organisations are accountable for continuously improving the quality of their services and safeguarding high standards of care by creating an environment in which excellence in clinical care will flourish”

(Scally & Donaldson 1998:61 in Courtney & McCutcheon 2010:116)

18
Q

What Do Clinical Audits Measure?

A

Monitor use of interventions/care received by patients against agreed standards

Assess effectiveness: does an intervention do what it is intended to do?

Need evidence to determine if intended outcomes were achieved

Audits show success/failure of intended outcomes

Identify departure from “best practice”

19
Q

Descriptive vs Inferential Statistics

A

Descriptive statistics:
Allow researchers to describe, organise and summarise raw data

Inferential statistics:
Allow researchers to estimate how reliably they can make predictions and generalise their findings based on the data

20
Q

Results Section of Research Papers

A

Summarise findings with two major goals:

  • To describe/explain phenomenon of interest
  • To predict aspects related to that phenomenon
21
Q

Data Analysis in Qualitative Research

A

Research question always kept in mind

Data collected are formally interpreted

Data analysis is:
deliberate
considered
systematic

22
Q

Qualitative Data Analysis

A

When is it performed?

After data collection complete
Simultaneously with data collection
(Constant comparative data analysis)
Data collection & analysis “staged

Two major styles:

Fracturing, grouping, gluing (bits)

Circling and parking (whole)

23
Q

Fracturing, Grouping, Gluing

A

Most common style of qualitative analysis

Major analysis strategy is that of coding:
ie data are:

  • fractured (divided into labelled bits=codes)
  • categorised (codes are grouped into tentative categories & labelled)
  • integrated (linked/glued together)
24
Q

Circling & Parking

A

Data set treated as mass of information

Not fractured into small bits/sections

Aim: understanding overall themes

‘Circle’ data set

‘Park’ for closer scrutiny

‘Circle’, ‘Park’, ‘Circle’ etc until understanding complete

25
Q

Other Styles of Analysing Qualitative Data

A

“Magnifying glass” style: data that are focus of study subjected to minute scrutiny

Layering & Comparing: eg in Ethnography; data analysed in layers

26
Q

Quantitative Descriptive Statistics

A

Statistical procedures: give organisation & meaning to numerical data

Descriptive statistics: describe, organise, summarise raw data

of Inferential Statistics: make predictions & generalise findings

27
Q

Two important functions of descriptive statistics

A

Organisation of data into figures eg piecharts, histograms, scatter plots, tables, line graphs
-Graphical & numerical techniques for organising & interpreting data

So enable trends & differences to be noted & calculation of simple statistics

Condense/reduce large quantities of numerical information into meaningful units

-Can be condensed & summarised using statistics eg measures of central tendency & measures of variability

28
Q

Statistics:

Levels of Measurement

A

Nominal

Ordinal

Interval

Ratio

29
Q

Statistics:

Ordinal Measures

A

Used to show relative ranking of events/objects

Conveys more information than nominal data

eg “Please rate the quality of nursing care received in this hospital”
Very poor, poor, average, good, very good, excellent

30
Q

Statistics:

Frequency Distribution

A

The most basic way of organising data

The number of times each event occurs is counted
Often expressed as percentages (%)
May be numbers of cases (N or n)
	N=number in the whole sample
	n=number in subgroup of the sample
31
Q
Statistics:
Interval Measures (Continuous)
A

Differences between scores/measures can be treated as equal
There is a specific numerical distance between each of the levels
No absolute zero

eg Temperature (°C);
	  blood pressure (mmHg)
32
Q

Statistics:

Ratio Measures

A

Also continuous
Show ranking of events/objects with equal intervals
Do have absolute zero: makes the ratio of scale values meaningful
Highest level of measurement

eg height, weight, distance, pulse, blood pressure

33
Q

Statistics:

Measures of Central Tendency

A

Single central score- enables summarising distribution of data set

Describe the centre of a distribution of scores

Three measures most common:
Mode
Median
Mean

34
Q

Statistics:

Normal Distribution

A

Symmetrical & bell-shaped

Mean is at the centre

Most common score (mode) at the centre

Middle score (median) at the centre

Shows normal distribution- few values low or high- most in middle.

35
Q

Statistics:

Skewness, Symmetry and Kurtosis

A

Skewness: measure of the asymmetry of the distribution of scores

Can have =ve/-ve skew

Symmetry: 2 halves of a distribution are mirror image of each other

Kurtosis: Related to the ‘peakness’ or flatness of a distribution

36
Q

Statistics:

Measures of Variability/Dispersion

A

Concerned with the spread of data

Variability answers:

- Is the sample homogeneous or heterogeneous?
- Are the samples similar or different?

Measures of variation describe extent to which individuals/scores in sample vary

Most common measures are:

		- range
		- variance
		- standard deviation
37
Q

Statistics:

Range

A

Simplest & most unstable measure of variability

The difference between the highest & lowest scores

Disadvantage: depends on the 2 extreme scores only (outliers)

Can use difference between other scores e.g. semi-quartile range