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Consolidation of Concepts for EIHP_2015

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Amanda Lambros

on 24 May 2015

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Transcript of Consolidation of Concepts for EIHP_2015

Consolidation of Concepts for EIHP100/130
SAMPLING
External Validity
Measurements
Reliability
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
Variables
Independent & Dependent
Hypothesis & Null Hypothesis
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
Types of Data
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
WHY?!?!
Validity
Peer Reviewed Journals
Ethics
Beneficence
- act in the best interest of the patient
Non-maleficence
- do no harm
Autonomy
- the right to refuse or choose their treatment
Justice
- a balanced decision of who gets what, especially with regard to treatment
Dignity
- the patient and the practitioner have the right to dignity
Honesty
- truthfulness and respect for the concept of informed consent
Mean
Median
Mode
Standard deviation
Minimum
Maximum
Skewness
Kurtosis (not previously mentioned)

Measures of Central Tendency
Inferential Statistics
Correlation:
p-values:
>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
DOES NOT EQUAL CAUSATION
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
Full transcript