Cheatography

# RM Reference Notes Cheat Sheet by Tash23

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.