Decision making theories: Implications for Academic Advising, by Tina Brazil and Jim Levin

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  • 1. DECISION MAKING THEORY Implications for Academic Advising Tina Brazil (TAB291@gmail.com) Jim Levin (JL7@psu.edu)
  • 2. Importance: Curriculum of Academic Advising
    • “Academic Advising …….. This curriculum includes …….. decision-making ……..” (NACADA, 2006).
    • “ … use complex information …. reach decisions … “ (NACADA, 2006).
  • 3. Definition
    • Rational decisions are ones that advances the welfare of the decision maker effectively and logically based on everything the decision maker knows and feels (Brown, 2005, p. 54)
    • Criteria for rational decisions (Hastie & Dawes, 2001, p.18)
      • It is based on the decision maker’s current assets. Assets include not only money, but physiological state, psychological capacities, social relationships, and feelings.
      • It is based on the possible consequences of the choice.
      • When these consequences are uncertain, their likelihood is evaluated according to the basic rules of probability theory.
      • It is a choice that is adaptive within the constraints of those probabilities and the values or satisfactions associated with each of the possible consequences of choice.
  • 4. Problems with Qualitative Decision Making Methods
    • Heuristics: speculative formulation serving as a guide in the investigation or solution of a problem (Keren and Teigen, 2004)
      • Representation bias: decision making by recalling a memory or experience that is similar to the present decision making experience
      • Availability bias: decision making by the primacy and/or by predicting “easily conceivable outcomes”
      • Anchor & adjustment bias: decision making by what is familiar and conceivable
  • 5. Models
    • Goals, Options and Outcomes (GOO)
    • The Personalist Approach
    • Lens Model
    • Simple Utility Equation
    • Additive Linear Multi-Attribute Utility Theory (MAUT)
  • 6. Definition of “Utility”
    • “ The consumption utility of an option is broadly defined here as the benefit the option delivers.” (Hsee, 1999, p. 555)
    • Furthermore, it is assumed that the decision maker should choose the option that delivers the greatest utility or benefit. (Hsee, 1999)
    • When making decisions, we think about what option will derive the highest utility. (Hastie and Dawes, 2001, p. 200)
  • 7. Goals, Options, and Outcomes
    • What do I want? (goals)
    • What can I do? (options)
    • What might happen? (outcomes) (Brown, 2005, p. 9)
    • In Practice
      • Goals & options can be listed
      • Difficult to predict outcomes
        • Quantitative methods can be used (simple probability theory)
          • Research indicates that quantitative methods predict outcomes better than qualitative methods (Hastie & Dawes, 2001)
  • 8. The Personalist Approach (first approximation of quantifiable decision making Fig 1. The pluses and minuses are assigned on an arbitrary scale decided by the decision maker. (Brown, 2005) + + + + + + + Total (algebraic) - - + + Career security + + + + + Academic success + + + + - - Class enjoyment Liberal Arts Science OUTCOMES OPTIONS
  • 9. Lens Model Concept
    • The decision maker is trying to see a “distal” true state of something through a “proximal lens” of cues. These cues represent information or characteristics that the decision maker uses to make a decision (Hastie and Dawes, 2001)
    • An algebraic model of probability that measures and assigns a scaled weight to the importance of each piece of information (cue) available to the decision maker (Hastie and Dawes, 2001)
    • Research: “experts correctly select variables that are important in making predictions, but that a probability model combines these variables in a way that is superior to the global judgments of these very same experts.” (Hastie and Dawes, 2004, p. 58)
    • Probability models have been derived from the Lens Model Concept.
  • 10. Simple Utility Equation
    • Decision tree in which each option represents a major branch, and from each branch stems the possible outcomes. Each of these outcomes is assigned a specific quantitative probability so that the sum of the outcomes stemming from one choice adds up to 1, or 100%. The probability for each outcome is multiplied by an assigned number that represents how the decision maker would feel about that outcome (Hastie and Dawes, 2001)
    • Utility = Σ (probability outcome x value outcome )
  • 11. Simple Utility Equation study play pass .2 fail .8 pass .7 fail .3 Value prob x value utility -100 -.3 .4 +100 .7 -100 -.8 -.6 +100 .2 Figure 2. Here, the two options are study and play. In this case, the utility of option “study” has a much higher utility value to the decision maker than does play. Utility = Σ (probability outcome x value outcome )
  • 12. Additive Linear Multi-Attribute Utility Theory (MAUT)
    • MAUT weighs all of the attributes and scales the attributes by importance to the decision maker. Each option is considered by assigning a scaled value to that option-attribute, according to its importance, and then adding up all of the scaled option-attributes to obtain the utility value for that option (Hastie and Dawes, 2001)
  • 13. Example of MAUT
    • To predict the probability of a student graduating from ENGR, Dr. James Levin constructed a probability model (logic regressions) that used several quantitative and qualitative (which were quantified) student criteria as inputs, assigned a scaled value to each criterion, and produced an output that gave the probability of that student graduating from ENGR. (Levin & Wyckoff, 1998)
    • Similar models are in progress for SC (Levin, 2007)
  • 14. Additive Linear Multi-Attribute Utility Theory (MAUT): ENGINEERING
    • Grad Engr/Not Grad Engr = -3.8 -.02 x 10 (nspts) -.01 x 520(satv) +.69 x 3.00(hsgpa) +.08 x 25(alg) +.07 x 12(chem-s) + .28 x 1(reas-g) +.20 x 1(gen) = .236
    • Odds of Grad Engr/Not Grad Engr = e .236 = 2.72 .236 = 1.27/1
    • Probability = 1.27/2.27 = 56%
  • 15. MAUT Example: SC   PHYS_ATT -0.0776     0.1678 .1818 (.0429) MATH_ATT -0.0819     0.1555   CHEM_ATT (<0.0493) (<.0001) -0.0161   -0.0173 -.0136 (<.0001) NONSCIpts       -0176 (.0231) ENGL score -0.0013 (<.0001) 0.1282   0.0798 .0759 (<.0001) CHEM score -0.055   0.3735     M40_score         M140_score PHYSICAL MATH LIFE ALL  
  • 16. MAUT Example cont: SC         BASIC MATH SCORE -0.0179 (<.0001) -1.3727   -1.1434 -1.2080 (<.0001) REMEDIAL_MATH         SAT_MATH -0.0565 0.0033     .0022 (.0111) SAT_VERB -0.0111 (<.0001)   2.1597 0.9413 .8618 (<.0001) HS_GPA         Gender         Study_psu -0.0469 0.0288     .0142 (.0138) Study_hs -0.0104   1.7525   .2727 (.0171) COMPLETELY CERTAIN       -.2241 (.0929) CERTAIN       .8433 (.0138) KNOW_SOME         KNOW_ALOT -0.0358   -0.7194     BIOL_ATT
  • 17. References
    • Brown, Rex, Rational Choice and Judgment: Decision Analysis for the Decider, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005.
    • Hastie, Reid and Robyn M. Dawes, Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making, Sage Publications, Thousand Oaks, California, 2001.
    • Hsee, Christopher K. (1999). Value Seeking and Prediction-Decision Inconsistency: Why Don’t People Take What They Predict They’ll Like the Most? Psychonomic Bulletin & Review, 6 (4), 555-561.
    • Keren, Gideon and Karl H. Teigen, “Yet Another Look at the Heuristics and Biases Approach.” Blackwell Handbook of Judgment and Decision Making, Blackwell Publishing Ltd., Malden, MA, 2004.
    • Levin, James and Wykoff, Jack,(1998). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Engineering. Engineering Education, 75(11), 178-182.
    • Levin, James, (2007). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Science in progress.
    • NACADA: National Academic Advising Association. (2006). NACADA concept of academic advising. Retrieved 5/2/07 from http://www.nacada.ksu.edu/Clearinghouse/AdvisingIssues/Concept-Advising.htm