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Consolidation Week

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Amanda Lambros

on 28 May 2017

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Transcript of Consolidation Week

Consolidation of Concepts for CMHL1001
External Validity
Research Designs
Where to get GREAT Evidence-Based Articles?
Joanna Briggs Institute (JBI) - joannabriggs.org
The Basics
Types of Questions
Descriptive - Describe what is going on
Relational - Relationship between two variables
Causal - Determine if one of more variables
cause or affect one or more outcome variables
Independent & Dependent
Hypothesis & Null Hypothesis - Difference? 'No'
Example: Ho: When children have food colouring, there will be no significant difference in hyperactivity levels of those children vs. Ha: When children have food colouring, there will be a significant difference in hyperactivity levels of those children
Wiley Chapters
Descriptive Statistics - Quantitatively describing a sample
Data Analysis - Part I
Data Analysis - Part II
Cochrane Collaboration - cochrane.org
What level of evidence are you aiming to find?
Internal Validity
'Sample' - Who is in your study
Sampling Frame - Accessible population to draw your sample from
Study Population - The larger population that you will draw your sample from
Theoretical population - The population you want to generalise to
Sampling Error
Systematic vs. Random Errors
Systematic: Biases in measurement (scales not calibrated) - IF identified it can be resolved
Random: Human error - reduced by re-measuring
Probability Sampling
Simple random sampling, stratified random sampling, cluster sampling, combined (a.k.a. multi-stage sampling)
Non-Probability Sampling
Convenience, purposive (quota, expert, snowball)
The extent that the results can be generalised - Does the same thing happen in other settings
Extent that a study minimises systematic errors i.e. How well a study is conducted
Consistency and repeatability - i.e. Same results over and over and over and over again :)
Type I & Type II errors
Type I: False positive - You've rejected your null hypothesis when its actually true! EEK i.e. You observe a difference when there is none
You might not notice as p<0.05

Type II: False negative - You haven't rejected your null hypothesis when you should have - You do not see a difference but there is one - Solution: Increase sample size...this may rectify TII
You might notice as p>0.05
Types: Nominal, Ordinal, Interval/Ratio
Peer Reviewed Journals
- act in the best interest of the patient
- do no harm
- the right to refuse or choose their treatment
- a balanced decision of who gets what, especially with regard to treatment
- the patient and the practitioner have the right to dignity
- truthfulness and respect for the concept of informed consent
Standard deviation

Measures of Central Tendency..know how to do these!
Inferential Statistics
>0.05 Accept Null
<0.05 Reject Null
Confidence Intervals
1- Strength - from weak to strong
2- p-value - Either significant or not significant
3- Direction - Either positive or negative
Range from -1 to +1 and 0 being no correlation at all
Typically 95% - This is a measure of reliability of an estimate
Odds Ratio
A measure of association between an exposure and an outcome.
OR=1 Exposure does not affect odds of outcome
OR>1 Exposure associated with higher odds of outcome
OR<1 Exposure associated with lower odds of outcome
Evidence Based Practice Model
Whether the construct you are using really measures what you are using it to measure

All of them have been covered this semester....so read them all as they will feature heavily on the exam!
Declaration of .....? Why is this important?
There is a control group

Causation possible? YES!
Causation possible? NO!
Difference between a research question and a Research statement?
Full transcript