Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T14:07:25.415Z Has data issue: false hasContentIssue false
  • English
  • Français

Event-based categorical sequential analyses of the medical interview: a review

Published online by Cambridge University Press:  11 October 2011

Maria Angela Mazzi ,
Lidia Del Piccolo  and
Christa Zimmermann
Maria Angela Mazzi*
Affiliation:
1Department of Medicine and Public Health, Service of Medical Psychology, University of Verona, Verona, Italy
Lidia Del Piccolo
Affiliation:
1Department of Medicine and Public Health, Service of Medical Psychology, University of Verona, Verona, Italy
Christa Zimmermann
Affiliation:
1Department of Medicine and Public Health, Service of Medical Psychology, University of Verona, Verona, Italy
*
Address for correspondence: Dr. M.A. Mazzi, Department of Medicine and Public Health, Section of Psychiatry, Service of Medical Psychology, University of Verona, Policlinico G.B. Rossi, Piazzale L.A. Scuro 10, 37134 Verona (Italy). Fax: +39-045-585871 E-mail: mariangela.mazzi@univr.it
Get access Rights & Permissions [Opens in a new window]

Summary

When the doctor-patient interaction is viewed as a series of utterances, the temporal position of utterances becomes a central information in understanding the nature of interaction. Important concepts are interdependence and serial dependence which account for the fact that two partners influence each other in their talk and that each partner influences him/herself. Lag sequential analysis studies the associations between doctor and patient utterances in a two-way contingency table (lag one sequences) and is used for exploratory purposes. Log-linear modelling, based on multi-way contingency tables, is used as an extension of lag-sequential analysis to study longer sequences.

Markov chains test sequences in terms of processes with the aim to find predictive models and require a theory driven approach. Pattern recognition aims to discover regularities in the temporal evolution of the utterance sequences. Theory driven applications analyse manifest patterns in terms of their conditional probability distribution while empirically driven applications are used to detect “hidden” patterns. These different approaches to sequential data can be regarded as complementary tools to describe the doctor patient consultations at various levels of complexity.

Declaration of Interest: none.

Keywords

doctor patient conversationsequential dyadic interactionlag-sequential analysiscategorical data series.
Type
Sequence Analysis of Patient-Provider Interaction
Information
Epidemiology and Psychiatric Sciences , Volume 12 , Special Issue 2: Sequence Analysis of Patient-Provider Interaction , June 2003 , pp. 81 - 85
Copyright
Copyright © Cambridge University Press 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agresti, A. 1996. An Introduction to Categorical Data Analysis. John Wiley & Sons: New York.Google Scholar
Allison, P.D. & Liker, J.K. 1982. Analyzing sequential categorical data on dyadic interaction: A comment on Gottman. Psychological Bulletin 91, 393–403.CrossRefGoogle Scholar
Altham, P.M. 1979. Detecting relationships between categorical variables observed over time: a problem of deflating a Chi-squared statistic. Applied Statistics 28, 115–125.CrossRefGoogle Scholar
Avery, P.J. 2002. Fitting interconnected Markov chain models: DNA sequences and test cricket matches. Statistician 51, 267–278.CrossRefGoogle Scholar
Avery, P.J. & Henderson, D.A. 1999. Fitting Markov chain models to discrete state series such as DNA sequences. Applied Statistics 48, 153–161.Google Scholar
Bakeman, R. & Gottman, J.M. 1986. Observing Interaction: an Introduction to Sequential Analysis. Cambridge University Press: Cambridge.Google Scholar
Bakeman, R. & Gottman, J.M. 1997. Observing Interaction: an Introduction to Sequential Analysis, 2nd ed.Cambridge University Press: Cambridge.CrossRefGoogle Scholar
Bakeman, R. & Quera, V. 1992. SDIS: A sequential data interchange standard. Behavioral Research. Methods, Instruments and Computers 24, 554–559.CrossRefGoogle Scholar
Bakeman, R. & Quera, V. (1995a). Log-linear approaches to lag-sequential analysis when consecutive codes may and cannot repeat. Psychological Bulletin 118, 272–284.CrossRefGoogle Scholar
Bakeman, R. & Quera, V. (1995b). Analysing Interaction. Sequential Analysis with SDIS and GSEQ. Cambridge University Press: Cambridge.Google Scholar
Bakeman, R., McArthur, D. & Quera, V. (1996a). Detecting group differences in sequential association using sampled permutations: Log odds, kappa, and phi compared. Behavioral Research Metliods, Instruments and Computers 28, 446–457.CrossRefGoogle Scholar
Bakeman, R., Robinson, B.F. & Quera, V. (1996b). Testing sequential association: Estimating exact p values using sampled permutations. Psychological Metliods 1, 14–15.Google Scholar
Budescu, D.V. 1984. Tests of lagged dominance in sequential dyadic interaction. Psychological Bulletin 96, 402–414.CrossRefGoogle Scholar
Dumas, J.E. 1986. Controlling for autocorrelation in social interaction analysis. Psychological Bulletin 100, 125–127.CrossRefGoogle Scholar
Eide, H., Quera, V. & Finset, A. 2003. Exploring rare patient behaviour with sequential analysis: an illustration. Epidemiologia e Psichiatria Sociale 12, 109–114.CrossRefGoogle ScholarPubMed
Faraone, S.V. & Dorfman, D.D. 1987. Lag sequential analysis: Robust statistical methods. Psychological Bulletin 101, 312–323.Google Scholar
Gottman, J.M. & Backeman, R. 1979. The sequential analysis of observational data. In Social interaction analysis: Metliodological issues (ed. Lamb, M.E., Soumi, S.J. and Stephenson, G.R.). University of Wiscounsin Press: Madison.Google Scholar
Gottman, J.M. & Roy, A.K. 1990. Sequential Analysis: a Guide for Behavioral Researchers Cambridge University Press: Cambridge.CrossRefGoogle Scholar
Lange, N. 1998. Pattern Recognition. In Encyclopedia of Biostatistics (ed. Armitage, P. and Colton, T.), pp. 3298–3304. John Wiley & Sons: West Sussex.Google Scholar
Lehoczky, J. 1998. Markov Chains. In Encyclopedia of Biostatistics (ed. Armitage, P. and Colton, T.), pp. 2423–2428. John Wiley & Sons: West Sussex.Google Scholar
MacDonald, I.L. & Zucchini, W. 1997. Hidden Markov and Other Models for Discrete-valued Time Series. Chapman & Hall: London.Google Scholar
Magnusson, M.S. 2000. Discovering hidden time patterns in behavior: T-patterns and their detection. Behavioral Research. Methods, Instruments and Computers 32, 93–110.CrossRefGoogle ScholarPubMed
Magnusson, M.S. 2002. T-patterns in behavior and DNA: detection and analysis with Theme and GeneTheme. In Measuring Beliavior 2002, 4th International Conference on Methods and Techniques in Behavioral Research, 27–30 August 2002, Amsterdam, The Netherlands.Google Scholar
Moran, G., Dumas, J.E. & Symons, D.K. 1992. Approaches to sequential analysis and the description of contingency in behavioral interaction. Behavioral Assessment 14, 65–92.Google Scholar
Pentland, A. & Liu, A. 1999. Modelling and prediction of human behavior. Neural Computation 11, 229–242.CrossRefGoogle ScholarPubMed
Sackett, G.P. 1979. The lag sequential analysis of contingency and cyclicity in behavioral interaction research. In Handbook of Infant Development (ed. Osofsky, J.D.). Wiley: New York.Google Scholar
Stiles, W.B., Honos-Webb, L. & Surko, M. 1998. Responsiveness in psychotherapy. Clinical Psychology: Science and Practice 5, 439–458.Google Scholar
Tavaré, S. & Altham, P.M. 1983. Serial dependence of observations leading to contingency tables, and corrections to chi-squared statistics. Biometrika 70, 139–144.CrossRefGoogle Scholar
Van-Beek, Y., de-Roos, B., Hoeksma, J.B. & Hopkins, B. 1992. Sequential analysis of nominal data in mother-infant communication: Quantifying dominance and bidirectionality. Beliaviour 122, 306–328.Google Scholar
Wickens, T.D. 1989. Multiway Contingency Tables Analysis for tlie Social Science. Lawrence Erlbaum Associates Inc.: Hillsdale.Google Scholar
Wickens, T.D. 1993. Analysis of contingency tables with betweensubjects variability. Psychological Bulletin 113, 191–204.CrossRefGoogle Scholar
Zimmermann, C., Del Piccolo, L. & Mazzi, M.A. 2003. Patient cues and medical interviewing in general practice: examples of the application of sequential analysis. Epidemiologia e Psichiatria Sociale 12, 115–123.CrossRefGoogle ScholarPubMed