Cheatography
https://cheatography.com
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
Oneway 
1 factor/independent variable (categorical) 
Twoway 
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) 
Ttest Types
Test 
Description 
DF 
1sample (single) 
Compares your experimental group with a hypothesised or known value 
n1 
2sample (independent) 
Compares the means for two independent samples 
(n11) + (n21) 
Paired 
measuring something for the same group of people 
n1 
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 
RSqaured 
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 nonparametric 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 NonParametric Equivalent Tests
Parametric 
NonParametric 
1sample AND paired ttest 
Sign test or Wilcoxon signedrank test 
2sample ttest 
MannWhitney test 
Oneway ANOVA 
KruskalWallis test 
Multifactor ANOVA (twoway + repeated measures) 
N/A 
N/A 
Chisquare 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 indepth 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 (topdown) 
Inductive (bottomup) 
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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