RELIABILITY AND VALIDITY OF DATA COLLECTION

RELIABILITY OF MEASUREMENT • Measurement is reliable when it yields the same values

across repeated measures of the same event• Relates to repeatability• Not the same as accuracy

• Low reliability signals suspect data

THREATS TO RELIABILITY

1. Human error

• Miss recording a data point

• Usually result from poorly designed measurement systems

• Cumbersome or difficult to use

• To complex

• Can reduce by using technology – Cameras

2. INADEQUATE OBSERVER TRAINING

• Training must be explicit and systematic

• Careful selection of observers

• Must clearly define the target behavior

• Train to competency standard

• Have on-going training to minimize observer drift

• Have back up observers observe the primary observers

3. UNINTENDED INFLUENCES ON OBSERVERS

• Causes all sorts of problems

• Expectations of what the data should look like

• Observer reactivity when she/he is aware that others are evaluating the data

• Measurement bias

• Feedback to observers about how their data relates to the goals of intervention

SOLUTIONS TO RELIABILITY ISSUES

1. Design a good measurement system

• Take your time on the front end

2. Train observers carefully

3. Evaluate extent to which data are accurate and reliable

4. Measure the measurement system

ACCURACY OF MEASUREMENT

• Observed values match the true values of an event

• Issue: Do not want to base research conclusions or treatment decisions on faulty data

PURPOSES OF ACCURACY ASSESSMENT:

• Determine if data are good enough to make decisions

• Discover and correct measurement errors

• Reveal consistent patterns of measurement error

• Assure consumers that data are accurate

OBSERVED VALUES MUST MATCH TRUE VALUES

• Determined by calculating correspondence of each data point with its true value

• Accuracy assessment should be reported in research

INTER- OBSERVER AGREEMENT (IOA) OR RELIABILITY (IOR)

• Is the degree to which two or more independent observers report the same values for the same events

• Used to:

• Determine competency of observers

• Detect observer drift

• Judge clarity of definitions and system

• Increase validity of the data

REQUIREMENTS FOR IOA / IOR

• Observers must:

• Use the same observation code and measurement system

• Observe and measure the same participants and events

• Observe and record independently of one another

METHODS TO CALCULATE IOA / IOR

• (Smaller Freq. / Larger Freq.) * 100 = percentage

• Can be done with intervals as well

• Agreements / Agreements + Disagreements X 100

• Methods can compare:

• Total count recorded by each observer

• Mean count-per-interval

• Exact count-per-interval

• Trial-by-trial

TIMING RECORDING METHODS:

• Total duration IOA

• Mean duration-per-occurrence IOA

• Latency-per-response

• Mean IRT-per-response

INTERVAL RECORDING AND TIME SAMPLING:

• Interval-by-interval IOA (Point by point)

• Scored-interval IOA

• Unscored-interval IOA

CONSIDERATIONS IN IOA

• During each condition and phase of a study

• Distributed across days of the week, time of day, settings, observers

• Minimum of 20% of sessions, preferably 25-30%

• More frequent with complex systems

CONSIDERATIONS IN IOA

• Obtain and report IOA at the same levels at which researchers will report and discuss it within the results

• For each behavior

• For each participant

• In each phase of intervention or baseline

OTHER CONSIDERATIONS

• More conservative methods should be used• Methods that will overestimate actual agreement should

be avoided• If in doubt, report more than one calculation• 80% agreement usually the benchmark

• Higher the better• Depends upon the complexity of the measurement

system

REPORTING IOA

• Can use

• Narrative

• Tables

• Graphs

• Report how, when, and how often IOA was assessed

VALIDITY• Many types

• Are you measuring what you believe you are measuring

• Ensures the data are representative

• In ABA, usually measure:

• a socially significant behavior

• dimension of the behavior relevant to the question

THREATS TO VALIDITY

• Measuring a behavior other than the behavior of interest• Measuring a dimension that is irrelevant or ill suited to

the reason for measuring behavior• Measurement artifacts

• Must provide evidence that the behavior measured is directly related to behavior of interest

EXAMPLES

• Discontinuous measurement

• Poorly scheduled observations

• Insensitive or limiting measurement scales

CONCLUSIONS

• Reliabiltiy and validity of data collection are important

• Impacts the client,

• Impacts your reputation for good work