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Sarah E. Stegall, Darrin L. Rogers, Emanuel Cervantes.

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Presentation on theme: "Sarah E. Stegall, Darrin L. Rogers, Emanuel Cervantes."— Presentation transcript:

1 Sarah E. Stegall, Darrin L. Rogers, Emanuel Cervantes

2 The Semantic Inconsistency Scale (SIS), a no- cost tool for measuring random responding in questionnaire research, was developed and validated in two independent samples. It shows strong initial evidence of validity, able to not only detect computer-generated random responses but also invalid responding caused by more realistic conditions.

3 Invalid responding can threaten validity and interpretation (Huang, 2012; however, see Costa & McCrae, 1997 for alternative views). Random responding (RR; Archer & Smith, 2008) scales measure participants consistency of to pairs of items with similaror oppositemeanings (e.g., MMPI2 scale VRIN; Butcher et al., 2001; PAI scale INC; Morey, 2007).

4 Methods previously used to develop and evaluate RR scales include: Comparing responses from participants instructed to answer questionnaires randomly with subjects given standard instructions (Berry et al., 1991; Cramer, 1995; Galen & Berry, 1996) and Comparing real responses with computer-generated random responses (Charter & Lopez, 2003) Real-world More ecologically valid manipulations more externally valid Not been used so far

5 Commercially-marketed assessments only Semantic inconsistency scale (SIS) Public-domain measure of RR for use with questionnaires

6 Participants: 482 undergraduate students 75% female, 25% male 95% Hispanic Data Collection Phases Phase 1 (February-July, 2012): N=286, 75% female. Phase 2 (August-December, 2012): N=196, 81% female.

7 Procedures & Materials: Anonymous online survey Big Five Inventory (BFI; John & Srivastava, 1999) SIS item pool I see myself as someone who…

8 30 pairs of items From International Personality Item Pool (Goldberg et al., 2006)

9 Judged to be semantically related Very similar in meaning Apparently opposite in meaning

10 Degree of inconsistency in responses RR Needs a push to get started Finds it difficult to get down to work. Spends time thinking about past mistakes Doesnt worry about things that have already happened. I see myself as someone who… Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree *Note: reverse coded* Difference of 1 Difference of 3 Similar items Opposite items

11 Experimental Manipulation: Quick condition (Q or quick) Subtly encouraged to complete the task quickly In-test messages emphasized importance of students time Control condition (A or accurate) Instructed to complete the survey accurately In-test messages emphasized accuracy

12 Phase 1 Selection and validation of final item pairs Maximized correlations Resulting 22-item (11-pair) SIS Phase 2 SIS scale assessed using responses SIS score = mean discrepancy in SIS pairs (possible range: 0-4)

13 Q vs. A comparison Survey completion time Attention to survey content Real vs. random responses All results calculated on Phase 2 sample only (unless otherwise specified)

14 Median SIS scores Q > A (Wilcoxon test z=2.179, p<.05). Figure 1. Trimmed (20%) means for SIS scores in condition A (accurate) versus Q (quick).

15 Correlation SIS scores Time to complete the full survey Spearmans rho = -.13 (p =.06)

16 Multiple choice questions content of survey items they had just seen & responded to Number of questions answered incorrectly Prediction: positive correlation with SIS No association Spearmans rho =.04 (p >.05)

17 SIS discrimination between 100% random responding (computer-generated) Actual participant responses Compare Phase 2 responses to 100,000 records of randomly-generated responses. Score SIS on everything Real scores < Random-response scores (t=31.56, p<.001; Figure 2).

18 Figure 2. Distribution of true Phase 2 SIS scores (blue) versus randomly-generated profiles (red).

19 SIS sensitivity of discrimination between True Phase 2 records Equal number of randomly-generated records Receiver-Operator Characteristic (ROC) analysis Area under the curve (AUC) discrimination ability of the test AUC =.95 (excellent discrimination ability)

20 Figure 3. ROC analysis for Phase 2 responses vs. (100%) randomly- generated response records.

21 1. Dataset split in half randomly Control group: original (real) responses Random group: X% of responses replaced with random Randomly-selected X% of responses X goes from 1% to 100% (i.e., do this process 100 times) Control Group Original (real) Responses Random Group X% replaced with random 1% < X < 100%

22 2. SIS scored & AUC calculated SIS discrimination between Control & Random groups

23 3. Result: SIS discrimination between real and partial (from 1 to 100%) random responding 4. We repeated this entire process 100 times, to even out random selection

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29 Each run: AUCs comparing real responses to real + partial random 0% to 100% random Mean of 100 AUCs at each point

30 Figure 4. AUCs for 100 runs of SIS discrimination between original profiles and partially (1% through 100%) random profiles. Light blue lines are AUCs for 100 individual runs; dark blue line indicates mean AUC at each point.

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32 Semantic Inconsistency Scale (SIS) Phase 1: Scale development (22-item/11-pair) Phase 2: Validated Identification of random responding Excellent with 100% random responses Fair performance even with protocols having less than 20% random responding. Discriminate between Quick & Accurate: Participants primed and instructed to answer hastily Participants given regular instructions

33 Perform as well as (if not better than) comparable tests Easily inserted into a variety of psychological and personality tests Modification of item stems or formats may allow use with an even wider range.

34 Limitations and Future Directions: Not appropriate for all test varieties Very short research Clinical protocols Random responding is not always a problem Depends on clinical/research situation SIS might help you know whether it is

35 SIS = Robust and valid measure of random responding FREE: Creative Commons licensed

36 Archer, R. P., & Smith, S. R. (2008). Personality assessment. CRC Press. Berry, D. R., Wetter, M. W., Baer, R. A., Widiger, T. A., Sumpter, J. C., Reynolds, S. K., & Hallam, R. A. (1991). Detection of random responding on the MMPI-2: Utility of F, back F, and VRIN scales. Psychological Assessment: A Journal Of Consulting And Clinical Psychology, 3(3), doi: / Butcher, J. N., Graham, J. R., Ben-Porath,Y. S., Tellegen, A., Dahlstrom,W. G.,&Kaemmer, B. (2001). MMPI-2 (Minnesota Multiphasic PersonalityInventory-2): Manual for administration and scoring (2nd ed.). Minneapolis, MN: University of Minnesota Press. Charter, R. A., & Lopez, M. N. (2003). MMPI 2: Confidence intervals for random responding to the F, F Back, and VRIN scales. Journal of clinical psychology, 59(9), Costa Jr., P. T., & McCrae, R. R. (1997). Stability and Change in Personality Assessment: The Revised NEO Personality Inventory in the Year Journal Of Personality Assessment, 68(1), 86. Cramer, K. M. (1995). Comparing three new MMPI-2 randomness indices in a novel procedure for random profile derivation. Journal of personality assessment, 65(3), Gallen, R. T., & Berry, D. R. (1996). Detection of random responding in MMPI-2 protocols. Assessment, 3(2), Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40(1), Huang, J. L., Curran, P. G., Keeney, J., Poposki, E. M., & DeShon, R. P. (2012). Detecting and deterring insufficient effort responding to surveys. Journal of Business and Psychology, 27(1), John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2, Morey, L. C. (2007). Personality assessment inventory (PAI).

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