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Honey's content analysis technique
This technique has been developed in the consultancy environment. Unlike other content analyses which simply categorise the meaning of a set of constructs, Honey's technique utilises some of the ratings available in the repertory grids from which the pool of constructs being categorised are taken. In this way, it manages to aggregate the meanings shared by a group of people while reflecting some of the individual provenance of their private meanings.

Each individual interviewee is asked to rate all the elements on a single supplied construct, as well as on a set of his or her own constructs elicited in the usual way. This construct relates directly to the topic of the grid, and to the purpose of the overall grid investigation. So, in a study of how a group of local authority employees construe effective supervision by their direct managers, the construct "Overall, a more effective boss versus Overall, a less effective boss" might be used; (here, the elements might be a set of 8 to 10 "managers I have known"). In a course in which undergraduates are asked to reflect on the ways in which they learn, the supplied construct might be "Overall, more conducive to learning versus Overall, less conducive to learning"; (one might use as elements, 8 to 10 occurrences in the individual interviewee's life from which s/he really, but really! learnt something important).

The content analysis proceeds on two assumptions:

(a) that elicited constructs express personal ways by which each respondent understands the supplied construct; they are personal aspects of that construct,
(b) that this personal meaning can be expressed as a matter of degree: some elicited constructs lie closer to the personal meaning of the supplied construct than others.

For each interviewee, the sum of differences between the ratings of the elements on each elicited construct, and the ratings of the elements on the supplied construct, are computed. (As with any pairwise comparison of ratings on constructs, the directionality of constructs has to be taken into account by checking for reversals.) A simple transformation of these sums of differences into percentage matching scores can be done to cater for the situation in which different interviewees might have been working with different numbers of elements. Finally, all the constructs of all interviewees are pooled, and the pool categorised using conventional content analysis techniques (see e.g. Neuendorf, 2002).

The result of this procedure is rather powerful: every construct has attached to it a percentage matching score, which indicates its personal relevance to the topic of the study as defined by each individual interviewee's own definition of "relevance" (the match is between the ratings on each construct and the individual's ratings on the supplied construct, after all).

This becomes particularly valuable when the final step of the content analysis is taken. This usually involves the investigator in choosing a set of constructs to exemplify each category that has been identified in the content analysis. By choosing those constructs whose meaning is the same, and which have the highest percentage matching scores, one is automatically choosing constructs on which there is consensus across the group of interviewees and which represent the individual interviewee’s own understanding of the topic of the grid.

Honey recognises that different interviewees have different construct similarity metrics (a match of 82% between a given elicited construct and the supplied construct may be unremarkable in one interviewee whose construct structure for this topic is somewhat "obsessive", i.e. implicationally tight; while representing a very high degree of agreement when observed in another interviewee whose construct structure is relatively loose with matching scores all of the order of 60% to 70%). He advocates that, when selecting sample constructs in the last stage of the content analysis, as well as choosing ones with similar meaning and with high % matching scores, one should particularly focus attention on constructs with matching scores which are particularly high for the given individual who contributed that particular construct.

As with any content analysis, which represents a process of sociality by which the investigator construes the interviewees' construing, it is very advisable to carry out inter-rater reliability checks between at least two independent investigators on the first content analysis, a mutually agreed redefinition of the categories, and a repetition of the content analysis using these agreed categories, before terminating the analysis. Reliability figures of at least 0.90 for pooled construct sets composed of 200 - 400 constructs can be achieved with a little care in category definition: see e.g. Dick and Jankowicz (2001). Perreault and Leigh (1989) provide an excellent review of relevant reliability measures, in an article which should be better known to many psychologists who are interested in measuring the reliability of their category schemes. The article indicates some of the inadequacies of favourite measures such as % agreement and Cohen’s Kappa, and offers a powerful alternative measure.


  • Dick, P. & Jankowicz A.D. (2001). A social constructionist account of police culture and its influence on the representation and progression of female officers: a repertory grid analysis in a UK police force. Policing, 24, 2, 181-199.
  • Honey, P. (1979). The repertory grid in action. Industrial and Commercial Training,11, 11, 452-459.
  • Perreault, W.D. Jnr. & Leigh, L.E. (1989). Reliability of nominal data based on qualitative judgements. Journal of Marketing Research XXVI, May, 135-148.

Devi Jankowicz

Establ. 2003
Last update: 15 February 2004