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RM Reference Notes Cheat Sheet by

3 page sheet + matrix for exam (double sided)

Types of Data

Catego­ric­al/­Nominal
Do not hold numerical meaning (arbit­rary)
Ordinal
Rank ordering, differ­ences not equal
Interval
Intervals between points on a scale are equal and the same, zero is arbitrary
Ratio
Zero is NOT arbitrary (an absence)

Experi­mental Designs

Balanced
each cell (each combin­ation of factors) contain the same number of replic­ations (how many measur­ements
Complete
every level of one factor combined with every level of the other factor(s)
Incomplete
Lots of factors or many measur­ements (neste­d/block design best)
Single subjec­t/r­epeated measures
Subject acts as their own control
Ceiling effects: Test is too easy (100%)
Floor effects: Test is too hard (0%)
Learning effects: subjects improve with more trials
Order effects: test order may have effect on outcome

Charac­ter­istics of Data Sets

Data Shape
Frequency distri­butions are a common way to describe data shape (range of scores)
Location
finding central tendency or middle of data
Spread
Variance -> range, SD and IQR
Outliers
Clustering
e.g. bimodal distri­bution
Granul­arity
Data only takes on certain values (e.g. discrete data + rounded contin­uous)
 
(e.g. discrete data + rounded contin­uous)

Types of Sampling

Random
Increased ability to generalise to population
Systematic
Choosing subjects from a population at a regular interval (choosing every second item)
Cluster
Randomly select a few schools in your sample and have all students as partic­ipants
Conven­ience
Sample used because it is accessible rather than repres­ent­ative of a population
 

Central Limit Theorem

• draw a large enough sample from the population and plot all of those sample means, our sampling distri­bution will approach normal
• Sampling distri­bution uses sample means
• Population mean: mean of all sample means

Standard Error
- SD of sampling distri­bution
95% CI = sample mean +- 1.96 x SE

Pearson's Correl­ation (r)

Strength
Positive
Negative
Strong
.8 to 1
-.8 to -1
Moderate
.5 to .7
-.5 to -.7
Weak
0 to .4
0 to -.4

ANOVA Variance

 
DF
Sum of Squares
Mean Sqaure
Between Groups
no. groups -1
How much data varies between different groups (variance)
Average variance between groups
Within Groups
no. data points - no. of groups
How much data varies within each group (variance)
Average variance within groups
Total
no. data points - 1

Types of ANOVAs

One-way
1 factor­/in­dep­endent variable (categ­orical)
Two-way
2+ factor­s/IVs (categ­ori­cal), intera­ctions
Repeated Measures
Measure the same outcome variable on the same population twice
 
Each subject is now a random factor (rather than fixed factors)

T-test Types

Test
Descri­ption
DF
1-sample (single)
Compares your experi­mental group with a hypoth­esised or known value
n-1
2-sample (indep­endent)
Compares the means for two indepe­ndent samples
(n1-1) + (n2-1)
Paired
measuring something for the same group of people
n-1
One tailed: Direct­ionless -> one group is different from the other group (in pos or neg direction)
Two tailed: Direct­ional -> one group if larger or smaller than the other

Linear Regression

Beta
degree of change in the outcome variable for every 1 unit of change in the predictor variable
R-Sqaured
Fit of the model and represents how much variance in the DV can be accounted for by the IV
Analysis of Variance
Adj SS (adjusted sum of squares) -> total variance of data
 
- The error SS is what is left over -> variance that cannot be explained by other factors or variance in the model
Predicting
CI: If we repeated our experiment many times an degene­rated a confidence interval each time, 95% of those confidence interval will contain the true population value
Prediction Interval: Predicting future observ­ations from the regression equation
 

Assump­tions of Parametric Tests

1. Normally distri­buted data
2. Homoge­neity of variance
3. Interv­al/­ratio data
4. Indepe­ndence

This means that you may have to use non-pa­ram­etric tests when...
• your data is better repres­ented by the median (e.g. skewed data like salary or house prices), or
• you have a very small sample size, or
• you have ordinal (e.g. rating scales, some questi­onnaire results) or catego­rical data

Parametric and Non-Pa­ram­etric Equivalent Tests

Parametric
Non-Pa­ram­etric
1-sample AND paired t-test
Sign test or Wilcoxon signed­-rank test
2-sample t-test
Mann-W­hitney test
One-way ANOVA
Kruska­l-W­allis test
Multif­actor ANOVA (two-way + repeated measures)
N/A
N/A
Chi-square test

Types of Qualit­ative Data

Transc­ripts (e.g. interview)
Allows the researcher to ask about specific things and probe deeply
Observ­ation
ethnog­raphic studies
Pictures
Pictures could be photos that the researcher has taken (drawings, rooms etc)
Documents
Many types (e.g. progress notes)
Web content
Publicly available (e.g. social media)

Sampling for a Qualit­ative Study

Typical Case
Average case
Extreme case
Unusual, unique or distinct case
Maximum Variation
Looking for the biggest range of perspe­ctives
Homogenous Group
Minimum variation sampling + Focus on in-depth area of interest
Stratified Purpose
Selected cases from identified subgroups (e.g. 5 people from 4 age groups)
Theore­tical
Start data collection -> analyse results -> form therapy -> continue sampling
Snow Ball
One respondent is asked to suggest others.
Convin­ience
Recruiting anyone who is at hand

Qualit­ative Evidence

Tangibly (concrete)
Intangibly
Guidel­ines, protocols
unders­tanding what clients want from their clinicians
practice recomm­end­ations based on qual research
broaden knowledge and change behaviours

Setting up a Qualit­ative Analysis

Deductive (top-down)
Inductive (botto­m-up)
coding will be influenced by the framework you're using
coding will be purely based on what the partic­ipant has said, without trying to fit it into a framework.
 

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