PSY 310 week 3 notes
PSY 310 week 3 notes psy 310-03
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This 3 page Class Notes was uploaded by Jessica Poland on Monday February 1, 2016. The Class Notes belongs to psy 310-03 at Grand Valley State University taught by Dr. Cornelius in Summer 2015. Since its upload, it has received 24 views. For similar materials see Behavior Modification in Psychlogy at Grand Valley State University.
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Date Created: 02/01/16
PSY 310 Week 3 notes▯ Time sampling: Another method of tracking behavior. Scored as occurring or not occurring during a very brief observation time, which are separated by large periods of time of no recording. Ex: Classroom setting-Check if Billy is on task once an hour.▯ ▯ Potential sources of error in data:▯ ▯ Deﬁnition of target behavior: not speciﬁc enough▯ ▯ Observational situation: View obscured, something distracting in environment▯ ▯ Training of observers: observers not trained well enough▯ ▯ Data Sheets▯ To identify sources of error, behavior modiﬁcation therapists will often have more than one observer, and then compare their results.▯ ▯ Interobserver reliability/agreement: Between the two observers, calculate their agreement on the data. This gives us an idea of accuracy.▯ ▯ Overall Agreement: Count total number of behaviors and divide them, then multiply by 100 to get a percentage. Ex: Teacher says “um” how many times? Amanda says she said “um” 56 times, and Nicole says it was 74 times. 56/74 * 100 = 75.7% overall agreement. This does not tell you if they agreed each/what time, just totals!!▯ The only way to tell if you agree on certain instances of the behavior is if you had a time-stamp on it.▯ ▯ Point-by-point agreement: Looks at each behavior and calculates agreement. Look at each interval for agreements or disagreements. We can calculate for occurrences and non occurrences or just occurrences. ▯ ▯ Occurrences and non-occurences: Look at each box on an interval table. What did the ▯ ▯ ﬁrst person have written and did the other agree? Count if they agree that behavior did ▯ ▯ happen and if they agree that the behavior did not happen. Count any disagreements. ▯ ▯ ▯ Occurrences only: Look at each box on the interval table. If at least one person claims ▯ ▯ that the behavior did happen at that interval, compare to the other person and count that ▯ ▯ as either an agreement or a disagreement. If both people claim that the behavior did not ▯ ▯ happen during that interval, do not count it at all. ▯ ▯ ▯ Equation: A/A+D * 100 = %▯ ▯ ▯ The percentages for Occurrences and non-occurrences should be higher than just ▯ ▯ occurrences because you get to add the absence of the behavior that you ignored in ▯ ▯ only occurrences. This raises the amount of agreements. ▯ ▯ Another method: Cohen’s Kappa: Measures reliability of the data. Controls by chance of agreement… Closer to 1=more agreement. Similar to Correlation coefﬁcient. ▯ ▯ Topography: The form of the behavior; what the behavior looks like.▯ ▯ Function: What is the function of the behavior? Incentive for doing it? Why is this behavior being engaged in?▯ ▯ Understanding the function makes it easier to correct the behavior. Ex: Child throws ▯ ▯ tantrum because they are not being given attention. We can ﬁx this by only giving them ▯ ▯ attention when they are behaving well. ▯ ▯ How to ﬁgure out the function:▯ ▯ -Look at the Antecedents and consequences. “A-B-C” “Antecedents-Behavior-▯ ▯ ▯ Consequences”▯ ▯ -Questionnaires/indirect▯ -Direct observation▯ -experimental functional analysis - manipulate environment to demonstrate a relationship between A and C. Would not want to do this when behavior is harmful. Sometimes this is not feasible/ethical. ▯ ▯ Ex: Engages in behavior-give them attention. Next time, give them a tangible reinforcer. ▯ ▯ Next time, give them a consumable reinforcer. Which one increases the behavior the ▯ ▯ most?? That one is probably serving as the function of the behavior. ▯ ▯ We assume that behavior is purposeful. ▯ ▯ Big Four Functions:▯ ▯ -Attention (Social reinforcement): eye contact, other people present▯ ▯ -Self-stimulation, physiological experience: nail-biting▯ ▯ -Non-social external environment: knocking over blocks▯ ▯ -Escape from demands: Ex, watching netﬂix allows me to escape from studying ▯ ▯ Designs for treatment; experimental design goals: want to be able to say that the behavior changed due to the treatment. (internal validity)▯ ▯ ▯ -Treatment= independent (X) variable▯ ▯ -Behavior= dependent (Y) variable▯ ▯ -Desired to prove that change in X causes change in Y▯ ▯ ABAB design: A=Baseline B=Treatment. Must establish a baseline of data-what is normal behavior before treatment? Then, once you have a steady baseline, impose a treatment. See if behavior changes from baseline. Then, we must return back to baseline and then re-introduce the treatment in order to prove that the relationship is cause and effect, because the ﬁrst change from baseline could be due to other, outlying factors. ▯ ▯ How long does your baseline have to be? … it depends on the behavior and what your data are. Collect enough to show stability or at least a trend in the opposite direction of what is desired. Do not introduce treatment if the behavior is trending in the desired direction because it could be due to something else, we wont be able to tell if the desired behavior is occurring due to the treatment or not. If the behavior is dangerous, switch to treatment as soon as possible.▯ ▯ Problem with ABAB: Sometimes it is unethical to do a reversal. Ex: Headbanging treatment reduces the frequency of this behavior.. Do not take away the treatment! That is unethical. Another problem is that sometimes it is not possible to take away the treatment. For example, learning how to ride a bike. You cannot take away what has already been taught. ▯ Multiple baseline design: Does not require a return to baseline. Internal validity is assumes if dimensions change sequentially with the implementation of treatment. Usually, compare two variables and introduce the treatment, and see if both behaviors change. These behaviors must be relatively independent. ▯ ▯ -Multiple baseline across behaviors: Same person, different topography▯ ▯ -Multiple baseline across situations: Same behavior, different settings▯ ▯ -Multiple baseline across individuals: Same behavior, different people▯ ▯ Changing Criterion design: Changing the reinforcement of a certain behavior. No return to baseline. If behavior changes with the criterion, we an assume cause and effect. Example: Ashley wants to eat less fast food. She eats 15 fast food meals one week, she gets to take a nap. The next week, she must eat 12 or less the next week in order to get to take a nap, and she must eat 10 or less the next week to get a nap, etc. ▯ ▯ ▯
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