This page provides extra material to the recent<i> Issues in Forensic Psychology</i> paper submitted by Paul Taylor, Karen Jacques, <a href=””>Ellen Giebels</a>, <a href=””>Mark Levine</a>, Rachel Best, <a href=””>Jan Winter</a>, and <a href=”″>Gina Rossi</a> titled “Analysing Forensic Processes: Taking Time into Account”. A pdf of this paper can be <a href=””>downloaded</a>.

In this paper, we suggest that the forensic psychology field had neglected studying processes, and that it really should study processes because temporality and change are critical to most of the field’s core subjects. On this page, we provide some evidence to support our view and some resources for (wise!) researchers who are thinking of incorporating time into their analyses.

<b>Prevalence in Journals</b>

We examined the top 10 forensic psychology journals for articles that examined data consisting of at least 3 times points examined in a way that preserved the temporal order amongst the points. An Endnote file of the articles that we identified as meeting this criteria can be <a href=””>downloaded</a> (needs unzipping first). Of course it is more than likely that we ay have missed a relevant article. If you know of an article that should have been included, please <a href=””>email</a> and let us know so that we can begin to put together a reference list for those interested in using these methods.

<b>Software for Analysing Processes</b>

We have developed software that conducts all of the analyses described in the IFP paper, and all of those described in the section below. At the moment this software is still in a prototype form (i.e., it’s not slick and seemless), but if you would like to use it, and it is free for use in a good home, please contact <a href=””>Paul</a>.

<b>Further Reading</b>

As well as the methods described in the papers included in the Endnote file above, there are a number of other methods and papers that provide good examples of sequence methods as applied to an issue that is (at least potentially) related to forensic psychology. These include (and other suggestions are welcome):

<b>   Examining Contingencies</b>

The co-occurrences of behaviors within a sequence, often refered to as contingencies or lag-1 relationships, provide important information about the way in which a sequence is constructed. Arguably, these contingencies should provide the “descriptive” backbone to any analysis of forensic process.

Bakeman, R., & Gottman, J. M. (1997). <i>Observing interaction: An introduction to sequential analysis </i>(2nd ed.). New York: Cambridge University Press.

Bakeman, R., Deckner, D. F., & Quera, V. (2005). Analysis of Behavioral Streams. In D. M. Teti (Ed.), <i>Handbook of research methods in developmental psychology</i> (pp. 394-420). Oxford, UK: Blackwell Publishers. <a href=””>View PDF</a>

<b>   State-transition diagrams</b>

These graphs consider “what follows what” (i.e., lag-1 conditionals). They are useful for identifying “pathways” through a sequence (e.g., a life history), and for finding critical features within the average sequence (e.g., turning points). They do not, however, take no account of longer dependencies (i.e. lag-2 upwards), and alternative methods are required to capture more complex relationships (e.g., proximity coefficient, see below).

Fossi, J., Clarke, D. D., & Lawrence, C. (2005). Bedroom rape: Sequences of sexual behavior in stranger assaults. <i>Journal of Interpersonal Violence, 20,</i> 1444-1466.

Jacques, K., & Taylor P. J. (September, 2007). Pathways to suicide terrorism: Do women follow in men’s footsteps? Poster presented at the BPS Division of Social Psychology conference. Kent, UK. <a href=””>View poster</a>

<b>   Proximity Analysis</b>

This form of analysis measures the interrelationships among codes wtihin a sequence using a general coefficient. The coefficient avoids the arithmetic manipulations and extrinsic assumptions made by many existing techniques, and it uses data efficiently which allows comparisons across speakers, among transcripts, and across different sections of the same sequence.

Taylor, P. J., & Donald, I. J. (2007). Testing the relationship between local cue-response patterns and global dimensions of communication behavior. <i>British Journal of Social Psychology</i>, <i>46,</i> 273-298. <a href=””>View PDF</a>

Taylor, P. J. (2006). Proximity coefficients as a measure of interrelationships in sequences of behavior. <i>Behavioral Research Methods</i>, 38, 42-50. <a href=””>View PDF</a>

<b>   Phase analysis</b>

Explores consistency in interaction by identifying coherent periods, or phases, of behaviour. It uses sequence data where events may reoccur or not, whose entities or behaviours are coded into discrete events.

Fisher, B.A. (1970). Decision emergence: Phases in group decision making. <i>Speech Monographs, 37,</i> 53-66.

Poole, M.S., & Holmes, M.E. (1995). Decision development in computer-supported groups. <i>Human Communication Research, 22,</i> 90-127.

<b>   Optimal matching analysis</b>

An extension of phase analysis, this method calculates the overall similarity of two or more sequences, or the similarity of these sequences to a prototype (Holmes, 1997). Similarity is based on the number of changes (known as Indels [Insertions and Deletions]) that it is necessary to make before one sequence becomes the exact replica of the other.

Holmes, M.E., & Sykes, R.E. (1993). A test of the fit of Gulliver’s phase model to hostage negotiations. <i>Communication Studies, 44,</i> 38-55.

<b>   Motif analysis</b>

This method seeks to identify sub-sequences (motifs) that are common to most sequences. The method relies on something called Gibbs sampling, which iterates over the data many times until motif candidates (of various lengths) that occur more than might be expected by chance emerge. It is useful in any scenario where the researcher wants to identify the fundimental or core sequence of behaviours that is common to most cases.

Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald, A. F., & Wootton, J. C. (1993). Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment. <i>Science, 262, </i>208-214.