1 / 31

How Much Information is Too Much?: A Comparison of Decompositional and Holistic Strategies

How Much Information is Too Much?: A Comparison of Decompositional and Holistic Strategies. Norma P Fernandez & Osvaldo F Morera University of Texas at El Paso. Making Complex Decisions.

isadora-watson
Télécharger la présentation

How Much Information is Too Much?: A Comparison of Decompositional and Holistic Strategies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How Much Information is Too Much?: A Comparison of Decompositional and Holistic Strategies Norma P Fernandez & Osvaldo F Morera University of Texas at El Paso

  2. Making Complex Decisions • A multiattribute decision must have at least two choices from which to choose, defined on at least two attributes. • Meehl (1954) has shown that statistical decision making typically outperforms clinical expert judgment in the diagnosis of patients of MMPI-profiles • Meehl (1954) has influenced how behavioral decision theorists think about complex decision making

  3. Decompoisitional Decision Making • Decompositional Strategy: These strategies break down complex decisions into smaller parts. These smaller parts are then aggregated to derive an overall course of action. One common decompositional technique is SMARTS (Edwards & Barron, 1994) U(x) = ki u(xij) The aggregation of attribute weights and utility judgments are often made in a linear fashion such that the overall utility of a stimulus can be calculated, where ki represents the attribute weight and u(xij) represents the single-attribute utility judgment.

  4. Holistic Decision Making • Holistic Strategy • An individual makes one general judgment, while simultaneously keeping in mind all the relevant information during the judgment process, to find the best stimulus. • This strategy is analogous to clinical decision making in Meehl (1954)

  5. Assessing Decompositional and Holistic Decisions Temporal stability • In order to measure temporal stability, a participant is given the same stimuli at two different sessions. • The scores of the stimuli from the first session are correlated with the scores of stimuli from the seconds session. • While people’s preferences may change over time, it is assumed that the decision strategy with the highest test-retest correlation is the better strategy.

  6. Assessing Decompositional and Holistic Decisions Convergent validity • We compare two strategies that have something in common. • Convergent validity “is useful to assess the association between decompositional and holistic judgments, and identify factors and circumstances that affect the levels of this association” (Morera & Budescu, 2001).

  7. Decompositional and Holistic Comparisons • As decisions become more complex, holistic temporal stability deteriorates more rapidly than decomposed temporal stability (von Winterfeldt & Edwards, 1986). • Convergent validity is similarly affected by increases in decision complexity (von Winterfeldt & Edwards, 1986).

  8. Present Study • The primary purpose of this project is to investigate the simultaneous effects of attribute complexity and number of stimuli on the temporal consistency and convergent validity of decomposed and holistic judgments.

  9. Present Study

  10. Sample • 430 participants (33 did not complete session two) • Mean age 20.57 years old (SD = 4.22). • 77.8% identified as Hispanics • 58.6% first language was English • 52.3% women

  11. Outcome Measures • Temporal stability outcomes: the correlation between holistic (hh) and decomposed (dd) judgments across days, as well as a measure of distance (smaller distance is indicative of increased stability). • Convergent Validity outcome: the correlation between strategies (hd, dh) across days, as well as a measure of distance.

  12. More on the Outcome Meausures • Fisher r-to-z transformation of the correlations z' = .5[ln(1+r) - ln(1-r)] • Root mean square error (RMS) • Measures distance between two decisions

  13. Temporal Stability(Fisher’s r-to-z Transformed Correlations) • 2(order) X 2(gender) X 2(strategy) X 3 (attributes) X 3 (stimuli) mixed ANOVA • Main effect for complexity in attributes (F(2, 376) = 4.77, p = .009, partial 2= .025). • The three attribute condition (M = 1.05) had higher temporal stability than the six (M = .88) and nine (M = .69) attributes condition.

  14. Temporal Stability(Fisher’s r-to-z Transformed Correlations) • Main effect for complexity in stimuli (F(2, 376) = 3.17, p = .043, partial 2= .017). • The three stimuli condition (M = 1.03) had higher temporal stability than the five (M = .83) and seven (M = .76) stimuli conditions.

  15. Temporal Stability(Fisher’s r-to-z Transformed Correlations) • Main effect for strategy (F(1, 376) = 4.50, p = .035 partial 2 = .012). • However unexpectedly, the holistic strategies (M = .98) were more stable over time than decomposition strategies (M = .76). • Strategy X order X attribute interaction (F(2, 376) = 3.06, p = .048, partial 2= .016)

  16. Temporal Stability:3-Way Interaction(Fisher’s r-to-z Transformed Correlations)

  17. Temporal Stability:3-Way Interaction(Fisher’s r-to-z Transformed Correlations)

  18. Temporal Stability(RMS Main Effects) • 2(order) X 2(gender) X 2(strategy) X 3 (attributes) X 3 (stimuli) mixed ANOVA • Main effect for complexity in stimuli (F(2, 376) = 4.83, p = .008, partial 2= .025). The three (M = 14.20) stimuli condition was statistically significant from the seven (M = 16.42) stimuli condition. Furthermore, the five (M = 14.02) stimuli condition was statistically different from the seven stimuli condition. • Main effect for strategy (F(1, 376) = 130.24, p = .000 partial 2 = .257). Decompositional strategies (M = 8.96) seemed to have smaller RMS distance values, indicating increased temporal stability than the holistic strategies (M = 20.80).

  19. Temporal Stability(RMS Strategy X Stimuli Interaction) Strategy X stimuli interaction (F(2, 376) = 3.06, p = .048, partial 2= .016). A t-test indicated that in the decompositional strategy there was not a statistical difference between the three stimuli condition (M = 9.17, SD = 6.73) and the seven stimuli condition (M = 9.39, SD = 7.31; t(260) = .252, p = .801). However, in the holistic strategy there was a statistical difference between the three stimuli condition (M = 19.42, SD = 13.13) and the seven stimuli condition (M = 23.43, SD = 10.92; t(260) = -2.68, p = .008).

  20. RMS 2-Way Interaction:Strategy X Stimuli

  21. Temporal Stability(RMS Strategy X Attribute Interaction) There was also a Strategy X attribute interaction: F(2, 376) = 8.15, p = .000, partial 2= .042. A t-test indicated in the holistic strategy no statistical differences Between the three attribute condition (M = 20.44, SD = 12.81) and the nine attribute condition (M = 22.28, SD = 12.40; t(254) = 1.17, p = .245). However, in the decompositional strategy there was a statistical difference between the three attribute condition (M = 11.18, SD 8.27) and the nine attribute condition (M = 6.69, SD = 3.72; t(254) = 5.38, p = 000).

  22. RMS 2-Way Interaction:Strategy X AttributesTemporal Stability

  23. Temporal Stability(RMS Strategy X Order Interaction) There was a strategy X order interaction (F(1, 376) = 10.58, p = .001, partial 2= .027). A t-test indicated in the decompositional strategy a non-statistically significant difference between order one (hd, dh; M = 8.40, SC = 6.13) and order two (dh, hd; M = 9.67, SD = 7.16; t(260) = -1.89, p = .060). However, in the holistic strategy there was a statistically significant difference between order one (hd, dh; M = 22.08, SD = 11.80) and order two (dh, hd; M = 19.27, SD = 11.65; t(393) = 2.37, p = .018).

  24. RMS 2-Way Interaction: Order X Strategy Temporal Stability

  25. Convergent Validity (Fisher r-to-z Transformed Correlations) • 2 (order) X 2 (gender) X 2 (session) X 3 (attributes) X 3 (stimuli) mixed ANOVA • Main effect for complexity for the stimuli conditions (F(2, 376) = 7.29, p = .001, partial 2= .037). The three stimuli condition (M = .71) had higher convergent validity than the five (M = .38) and seven (M = .34) stimuli condition.

  26. Convergent Validity (RMS) • 2 (order) X 2 (gender) X 2 (session) X 3 (attributes) X 3 (stimuli) mixed ANOVA • Main effect for complexity for the attribute conditions (F(2, 376) = 10.06, p = .000, partial 2= .051). The three attribute condition (M = 23.74) was different than the six attribute condition (M = 20.51) and the nine attribute condition (M = 21.01), indicating that increase in complexity leads to less distance. • Session X attribute X order (F(2, 376) = 3.48, p = .032, partial 2= .018).

  27. RMS 3-Way InteractionConvergent Validity

  28. RMS 3-Way InteractionConvergent Validity

  29. Comparison of RMS and Correlations

  30. Subjective Evaluations of Preferences

  31. Future Directions • Order effects may suggest that a replication of this study should be performed where only one strategy is performed per occasion (Morera & Budescu, 1998). • Discrepant findings with RMS and correlations is worthy of future investigation

More Related