Observation Methods (As opposed to experimental) - Describe behavior of a particular sample of people/animals - Takes systematic notes/codes on interesting behaviors - May look for frequency of a particular behavior, variations in interactions, range of behaviors, etc. - Data can be qualitative or quantitative Types of Observation - Naturalistic Observation: Studies in which a passive, outside observer makes systematic observations about a phenomenon in its natural environment - Contrived Observation: Observations that occur in a lab or otherwise atypical setting (one-way mirrors and hidden cameras are often used - Participant Observation: Same as naturalistic observation, except the observer is a participant within the group Naturalistic Observation - Goal: Observe study participants behaving as they usually would in every day environments - Observation can be instructed, where there is no attempt made by the observer to evoke behaviors of interest - Ex: Putting up cameras in a preschool and leaving them there for an hour - More structured interactions create situations that encourage a particular behavior - Ex: Asking a parent to read a book to their child Participant Observation - Goal: Observe participants as they would in their natural environment, while the researcher participants as well (POV notes) - Ideal for “closed” groups or activities - AA, fraternities/sororities, secret societies, etc - Can get additional detail from being a participant that’s you can’t get as a third-party observer - “Undercover” vs “stake-out” Participant vs Observer - Must strike balance between being a participant and being on observer - Too much observation can lead to reactive effects - Too much participants can lead to very biased observations, purpose may be forgotten - The balance between these depends on the research project Observation Challenges - Reactivity: The idea that participants might behave differently because they know their being observed/watched (Hawthorne effect) - Solutions: Concealed observer, video record, habituation - Lack of Control: Conditions are not equal across observations (very little can be held constant, unlike an experiment) - Conclusions must be drawn carefully - no causation! - Observer Bias: Observational research is interpreted through the lens of the observer (and thus, through their perspective of the world) - Behaviors are often ambiguous, and thus may be interpreted differently by different observers’ perceived notions - This includes hypotheses! - reduces validity and reliability Overcoming Bias - Use checklists to operationalize and standardize behavior - Use one or more observers who are unaware of the study’s hypothesis - Not knowing the hypothesis can keep people from jumping to conclusions or assuming behaviors, etc - Use multiple observers and measure agreement between them (inter-raptor reliability) - Use a sampling method Sampling Method - Time Sampling: Defining a specific time interval, and coding observations within that time period (ex: first 10 minutes of every hour) - Event Sampling: A particular event interval is selected for observation (ex: every 5 behaviors) Ethics - Often no informed consent if covert observations - Rare ability to opt out of the study - Privacy concerns in certain environments - IRB will approve covert observation studies if you can demonstrate that the value of it is sufficiently high Small N or Single Subject Studies - N < 10 (1-9 subjects) - Studies with only a single study is also called a case study - Most often used in clinical studies Why? - Goal: Most often, to develop an intervention or to intervene with an existing problem - Individualized Care - Appropriate for rare conditions and comorbidities(people with more than one disorder) - For basic science: Unusual cases can provide a better understanding of the brain Four Components of Small-N - DV is measured repeatedly: Only one subject, and repeated testing helps ensure the results aren’t because of noise or confounds - Baseline Phase - Treatment Phase - Analysis Repeated Measurement - DV must be measured multiple times both before and after the implementation of an intervention - Else: Potential regression to the mean, maturation, and other validity concerns - Sometimes not feasible/ethical to have a baseline period - Retrospective data may be appropriate: Case files, self-report from file intervention Baseline Phase - Time period in which the intervention is withheld from the participant(s) - Typically abbreviated ‘A’ - Allows for accurate assessment of DV before intervention - Assume that if we don’t intervene, the baseline would continue Treatment Phase - Time period in which the treatment is administered - Abbreviated ‘B’ - Repeat same process as in baseline - ’B’ measurements are compared to ‘A’ measurements to determine change DV Measurement Methods: - Interval: How long between occurrence? - Like duration,need definitive start and end points - likely not meaningful if behavior is frequent - Magnitude: What is the intensity of behavior? - Need a clearly defined scale, concerns about reliability DV measurement - Baseline Phase: Enough measurements for a predictable pattern to occur (At least 3 occasions) - Keep measuring until you have a predictable pattern - Treatment Phase: At least as many as the baseline phase Types of Designs - Basic Design: A-B (baseline - treatment) - Simplest small N design 1. Measure baseline phase 2. Administer treatment 3. Measure treatment phase - Least internal validity, any extraneous event that follows treatment could explain any change you see - Withdrawal Design: - Controls possible confounds in A-B - Logic: If the treatment changes behavior, behavior should revert to baseline when treatment is removed - A-B-A: Baseline, Treatment, Baseline - A-B-A-B: Baseline, Treatment, Baseline, Treatment - Concerns: Often not ideal and potentially unethical to remove treatment that was helping the participant, Some treatments may have lasting effects as well, so we shouldn’t always see a return to baseline
