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Coding and observation (Reliability (inter-observer reliability …

- - - - Steps (Martin & Bateson, 2007)
        Observational stream
        Experimental stream
        
        Observational stream
        Ask question
        Observe informally (ad libitum sampling)
        choose measures
        choose recording method
        collect and analyse data
        
        As Question
        Usually more you know in a published area, the more sophisticade the question
        BUT simple questions are necessary
        
        Observe informally
        Get to know population
        understand what's typical
        immerse
        
        Choose measures
        don't code behaviour not related to question
        what you want vs. what you can
        decide what to measure
        recording methods - how to sample behaviour
        
        Operational definitions
        specify physical requirements for coding
        (e.g. button press, finger through cage, etc.)
        Ostensive definitions
        diagrams, descriptions of behaviour
        (solitary vs. coordinated play)
        Classify measures as events or states
        events - occurnces as points in time
        States - long duration events, eg. sleep or play
        
        Ethogram
        operationalised list of behaviours and how to code
        enable replication
        
        Types of measures
        Latency - how long subject takes to respond to simulus
        Frequeny - number of occurences during observation interval (always use per unit of time, except when equal across conditions)
        rate - frequency per unit time
        Duration - total time for single occurance during observation interval
        Proportion - 30 minutes out of 45. Note .67 is a proportion not a percentage (also mean proportion)
        
        Scales of measurement
        non-parametric
        nominal - categorical - is/isn't, presence/absence
        ordinal - ranking - relational mathematical concepts (greater than, 1st 2nd 3rd)
        parametric
        Interval (0 is arbitrary) - 0 degrees is not zero heat. 60 degrees is not twice 30, but it is 30 more.
        ratio (continuous) - 0 is real. Can apply multiply and divide 60 is twice 30
        
        Sampling
        
        ad libitum (at leasure) - preferred method for initial study, miss rare events, short durations, underestimate effect of smaller events
        Focal sampling - specific indivudual, dyad etc. chosen. Large bias if individual does or doesn't do something others do, or does in private
        scan sampling - group is sampled, same biases for ad libitum
        behaviour sampling - all occurrences sampling. every time soething happens. possible to over estimate small events.
        
        Recording methods
        Time sampling (periodically sample)
        instantaneous sampling
        one-zero sampling - is it happening now yes/no
        continuous recording e.g. skinner box
        high fidelity records means less coding can be done
        
        Experimental stream
        Hypothesise
        predict
        design
        experiment
        analyse
        Interpret
- - - - Crying yes/no.
        misses intensity of crying (could code quiet 0, whimper 1, scream 2)
        misses duration
        misses relative intensity to contextual events (could subdivide into stranger/caregiver)
- - - - Consensus
        Percent agreement (doesn't account for agreement by chance)
        Cohen's kappa (does take random into account)
        Consistency
        correlation coefficient (doesn't take into account variance between coders)
        Cronbach's alpha (corrects for variance, can asses more than 2 coders)
      - Kappa
        PA = observed agreements
        PC = expected agreements by chance (products of yes percentages multiplied, added to product of no percentages)
        Kappa = (Pa - Pc) / (1-Pc)
        
        No absolute ranges for what's good. Landis & Koch quote a lot
        
        Bias: “tendency of a measurement process to over- or under-estimate the value of a population parameter”
        
        Marginal homogeneity
        more conservative as the marginal homogeneity increases
        where marginals are homogenous (Yes/no totals at side) are nearly the same.
        percentage agreement vs lower kappa
        
        Trait prevalance
        more conservative as the prevalence of a trait becomes higher or lower
        Single square (yes/no agreement) is notably more than others