1. Data
    1. Defn.
      1. A series of observations, measurements, or facts that can be analysed
    2. Variable
      1. Has a possible range of values
    3. Analysis
      1. Gathering, modelling and transforming data with the goal of highlighting useful information, suggesting conclusions and supporting decision making.
    4. Types
      1. Nominal
        1. Categorys
          1. No relationships
        2. Least powerful
      2. Ordinal
        1. Rank
        2. Has a relationship (1st, 2nd etc.
        3. Non mathematical relationship
      3. Interval
        1. No real 'ZERO'
          1. eg. temperature
        2. Has a mathematical relationship
      4. Ratio
        1. Has a tue 'ZERO'
        2. Eg. distance, height.
        3. Most powerful
  2. Research Methods
    1. Which method?
      1. Depends on the question
    2. Quantitative
      1. Experiments
      2. Surveys
      3. RCTs
      4. Numbers
        1. Tells you what happened
    3. Qualitative
      1. Focus groups
      2. Interviews
      3. Case studies
      4. Tells you why it happened
    4. Validity
      1. Internal
        1. to do with study design
          1. Is it ok?
          2. Are we measuring the right thing
          3. Eg measuring height for as a measure of intelligence is wrong.
      2. External
        1. Can it be applied outside?
          1. Can the results be generalised?
    5. Replicability
      1. Can it be done again?
    6. Reliability
      1. If experiment done again will the same results be given?
      2. Easier for lab based work
    7. Objective
    8. Unbiased
    9. Variables
      1. Must be operational
        1. Be explicitly stated
    10. Constructs
      1. Defined by theoretical definitions
  3. Variables
    1. Quasi-independent
      1. Characteristics that cant be randomly assigned
        1. Eg sex, age
    2. True experimental variables
      1. Can control these in a true experiment
      2. Can be randomly assigned
        1. eg Give Drug A or Drug B
    3. Independent variables
      1. The ones we control
        1. To bring about change in DV
      2. Levels
        1. At least TWO
          1. eg. gender- male/female
      3. AKA 'condition'
    4. Dependent variables
      1. The ones we measure
      2. The ones that depend on the IV
  4. Research design
    1. Experimental design
      1. True experiment design
        1. Design where researchers can randomly assign participants to experimental condition.
        2. Eg randomly assign normal participants to consume different amounts of alcohol
        3. Randomised
      2. Quasi experimental design
        1. Design where researcher cant randomly assign participants to groups
        2. eG Compare heavy vs light drinkers, as you are either in one group or the other
    2. Randomisation
      1. Reduces confounding variables
        1. When groups to be compared differ in ways other than what the researcher has manipulated
        2. As they are distributed equally among the groups
      2. Prevents (un)intentional bias.
      3. Ensures participant is equally likely to be assigned to either group
      4. Enables use of powerful statistics.
    3. Subjects design
      1. Independent groups design
        1. Comparing BETWEEN groups
        2. Potential problems
          1. Confounding factors
          2. Solutions
          3. Randomisation
          4. Matched groups
        3. Matched groups
          1. Make sure subjects in both groups are matched as closely as possible on potential confounding factors
      2. Repeated measures design
        1. Testing WITHIN groups
        2. Advantages
          1. Fewer participant
          2. Each participant is their own control
          3. Removes some confounding factors
        3. Disadvantages
          1. Order effects
          2. Cant return ppnt to original state
          3. Practice effect
          4. better performance due to practice
          5. Fatigue effect
          6. SOLUTION
          7. Counterbalancing
          8. Randomly assigning order to group
          9. Therefore we can know whether the order has made any difference
    4. Causation
      1. How correct is our claim of A being the cause of B?
      2. SOLUTION
        1. Have a comparision group
          1. Eg treatment vs placebo
        2. Could do O-X-O
          1. Eg. test, give alcohol, test.
          2. Therefore we know if alcohol is the cause
  5. Forms of validity
    1. Face
      1. Does it measure what it says it does?
    2. Criterion
      1. Concurrent
        1. Comparison of new test with established test
      2. Predictive
        1. Does the test have predictive value?
          1. Eg Does blood pressure value now predict heart attack in 5 years?
      3. Does the measured results agree with other measures of same thing?
    3. Construct
      1. How well does the design tap into the underlying construct
    4. Ecological
      1. Does study reflect naturally occuring behaviour?
        1. Eg does mouse in box reflect its behaviour in wild?
    5. Population
      1. Is our sample adequate for the claims we make about the population?
      2. What population are we interested in?
  6. Sampling
    1. A sample is a selection or subset of individuals from the population
    2. Why sample?
      1. Time
      2. Money
      3. Sufficiency
        1. Maybe we dont need that much data as we feel that the sample gives an accurate data
      4. Access
    3. How
      1. Random sample
        1. No pattern
      2. Systematic
        1. Drawn from the population at fixed intervals
      3. Stratified
        1. Specified groups appear in numbers proportional to their size in population
      4. Opportunity/Convenience
        1. People who are easily available
        2. Leads to bia
      5. Snowball
        1. Get current participants to recruit more for the research
        2. Useful if you want to recruit very specific population
          1. eg drug users might know other drug users
  7. Descriptive stats
    1. Central tendency
      1. Mean
        1. Average
        2. Less useful if there is a big outlier
        3. Best for continuous, symmetrical data
      2. Median
        1. Rank then find the middle value
        2. Best for ordinal data or interval/ratio data that is highly skewed
      3. Mode
        1. Most common
        2. Misleading if frequency is only just more than other values
        3. Best for nominal data
      4. For skewed data
        1. Positively skewed data, the mean has a higher value than the median, and the median has a higher value than the mode.
        2. Negatively skewed data, the mean has a lower value than the median, and the median has a lower value than the mode.
    2. Spread of data
      1. Range
        1. Max - Min
      2. Variance/Standard deviation
        1. Measure of mean deviation from mean
      3. Interquartile range
        1. 3rd quartile(75th qrtle)-1st quartile(25th qrtle)
        2. Useful when median is used as measure
        3. Value
          1. Large
          2. = data squashed
          3. Small
          4. = data spread out
        4. More stable than range as extreme values arent included
      4. Cumulative frequencies
        1. Each score and the number that attained that score and below
        2. eg if scores are 1-5, the we can say 7 people got 4 or less
      5. Percentiles
        1. Scores split into percentile
        2. A method of expressing a persons score relative to those of others
        3. Therefore if ur score is in 90th percentile you have done better than 90% of people
        4. 50th percentile=median
      6. Shapes of distribution
        1. Normal
          1. Ideal
          2. Symmetrical
          3. Mean/mode/median
          4. same
        2. Skewed
          1. Topic
        3. Kurtosis
          1. Steepness/flatness
          2. Steep
          3. Leptokurtic
          4. Flat
          5. Platykurtic
          6. +ve value= steep
          7. -ve value = flat
          8. 0=middling