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Computer Assisted Pretesting of CATI Questionnaires (CAPTIQ)

Frank Faulbaum
p. 5-17


Prétests assistés par ordinateur des questionnaires CATI : Des prétests par observation ou des prétests standards de questionnaires CATI sont problématiques puisque l’enregistrement du comportement observé du répondant doit être fait soit pendant l’entretien, soit après en remplissant des formulaires d’observation. L’enregistrement pendant l’entretien peut être une tâche lourde pour l’intervieweur qui essaie de mener correctement l’entretien. L’enregistrement après l’entretien introduit le problème de la fiabilité. Dans cet article, l’auteur présente une méthode Prétest Assisté par Ordinateur de Questionnaires d’Entretien Téléphonique (Computer-Assisted Pretesting of Telephone Interview Questionnaires ou CAPTIQ) qui permet le codage du comportement du répondant pendant l’entretien sans surcharger l’intervieweur qui peut faire ce travail sans interrompre la continuité de l’entretien. Les données prétests recueillies par CAPTIQ pour chaque question et chaque répondant peuvent être considérées  comme des données longitudinales représentées par un graphique appelé un IPD (Interview Process Graph). Comme un électrocardiogramme, un IPD révèle les zones problématiques lors de l’entretien. Il en résulte des informations recueillies sur les problèmes d’échelles de réponses et sur le processus d’apprentissage initialisé par les répondants lors de la passation du questionnaire. Les problèmes de compréhension associés aux questions apparaissent comme des oscillations dans l’IPG. L’article décrit la méthode CAPTIQ et présente une utilisation d’un IPG dans l’évaluation d’un questionnaire CATI pour une enquête nationale sur le santé et sur l’utilisation des médias.

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Notes de la rédaction

This article is a modified English version of one published in German in ZUMA-Nachrichten (Frank Faulbaum, Marc Deutschmann and Martin Kleudgen, “Computerunterstütztes Pretesting von CATI-Fragebögen: Das CAPTIQ-Verfahren“, May 2003, n. 52, pp. 20-34).

Texte intégral


1Observational or standard pretesting of CATI-Questionnaires is not easily performed since in a strict sense this would mean recording observed respondent behavior during the interview process. In this case, the coding system would have to be designed in such a way that its handling does not influence interviewer-respondent interaction. Otherwise the pretest would no longer constitute a pure field pretest but rather a pretest under particular conditions. In contrast to laboratory pretest methods like cognitive procedures (thinking aloud, paraphrasing, probing, etc.), pure observational pretesting relies exclusively on passive observation of respondents‘ behavior (for an overview of pretest methods see Exposito/Rothgeb 1997; Presser/Blair 1994; Prüfer/Rexroth 1996). Below, we present a method for Computer-Assisted Pretesting of Telephone Interview Questionnaires (CAPTIQ) which allows:
a behavior coding of the question-answer episodes in real time under field conditions (standard pretest), i.e. during the interview at the time when the episode actually takes place without interrupting the natural flow of the interview;
the reliable identification of certain types of problems occurring during the interview;
the assessment of respondent and interviewer specific influences on data quality on the basis of pretest data;
the immediate transfer of codes to a data file while the interview process is going on;
the using of large random samples to reduce the sampling error of pretest results and to do more complex statistical analyses already in the pretest stage of questionnaire development (see Kleudgen/Faulbaum/Deutschmann 2001; Deutschmann/Faulbaum/Kleudgen 2003; Faulbaum/Deutschmann/Kleudgen 2003).

2The approach is considered a first attempt to integrate coding of response behavior as part of a normal CATI interview. Associated with the pretest procedure is a specific graphical presentation of pretest results which is called an IPG (Interview Process Graph). The IPG, like an electrocardiogram, reveals the problem zones in the complete interview. With this method of presentation, it is possible to identify response scale problems and possible learning processes initiated by the respondents going through item batteries. Problems of understanding and weaknesses in question wording manifest themselves as oscillations of the IPG.

3In its traditional form, behavior coding of respondent behavior, which basically constitutes a variant of standard pretesting methodology, tries to classify response behavior along the dimension adequate vs. inadequate. The coding is done with respect to each question in the questionnaire. In principle, this could either be done by categorizing the responses after the interview or during the interview. The first variant has the disadvantage of requiring automatic recording of the whole interview which, in turn, at least in Germany, requires the consent of the respondents. Since this might disturb the pure field character of pretesting and might introduce a bias in response behavior, a decision was made to use the second variant: coding the response behavior during the interview. While behavior coding of tape-recorded responses after the interview has the apparent advantage that it could be done by the researcher himself, coding during the interview requires that it be done by trained interviewers. This, however, is not easy to deal with because of the greater time pressure in the case of telephone interviews. The interviewer has to code and interview at the same time without interrupting or delaying interaction with the respondent, a task which might constitute a heavy burden on the interviewer. This kind of multi-tasking demanded of the interviewer could be circumvented by having the coding done not by the interviewer but by specifically trained personnel equipped with separate computers and headsets who do the coding in parallel with the interview. This strategy, however, would also require the consent of the respondent. Furthermore, for larage samples, it requires costly equipment.

4Observation and categorization of response behavior during the interview process requires a quite simple coding system which could easily be managed by interviewers. Nonetheless, the simultaneous task of interviewing and coding puts a burden on the interviewers who have to be trained extensively. Only the most competent and experienced interviewers should be selected for the pretest phase.

Coding System And Coding Procedure

5The coding principles used are derived from behavior coding systems described elsewhere (see Morton-Williams 1979; Oksenberg/Cannell/Kalton 1991; Prüfer/Rexroth 1985, 1996) and adapted to the telephone mode. In contrast to PAPI (paper and pencil interviewing), to which most coding procedures originally refer, computer assistance allows the integration of the coding system into the CATI software (and, in principle, the CAPI software) by reserving certain keys for particular types of respondent behavior.

6The basic idea of coding respondent behavior can be illustrated by what Zouwen/Dijkstra/ Ongena (2000) called a “paradigmatic question-answer sequence”. In a paradigmatic, ideal and unproblematic sequence, the interviewer poses each question correctly and the respondent gives an answer which the interviewer is able to assign to one of the response categories. This, in fact, means that the respondent gives only adequate responses. Thus, the central aim of behavior coding and its underlying coding system is to classify, for each question occurring in the interview, the adequacy or inadequacy of the respondents’ answer and to identify certain types of inadequacy. Since no coding of the interviewer behavior is done no real interaction coding is involved we cannot decide whether or not an inadequate respondent behavior was caused by inadequate interviewer behavior. The latter possibility can only be ruled out by extensive interviewer training. Moreover, if a sufficiently large number of respondents are pretested and many interviewers are involved, the problem is less serious since systematic interviewer influences can be accounted for in the statistical analysis.

7The coding system is described systematically in figure 1. The basic types of behavior categories upon which the coding system is based are:
Spontaneous answer to the question: The respondent in his first reaction tries to give a direct answer to the question or refuses the question.
Non-spontaneous answer to the question: The respondent in his first reaction wants a further clarification by the interviewer before she/he gives an answer, refuses or says “don‘t know”. Thus, this class of responses collects all those which cannot be counted as direct attempts to select a response category.

Figure 1: The Online Process

Figure 1: The Online Process

8To each of these classes corresponds a number of behavior subcategories leading to a specific code. The codes are entered into the computer by the use of function keys to allow rapid input.

9The behavior subcategories of the above basic category types are:

10Subcategories for “Spontaneous answer to the question”:
Answer corresponds correctly to the response categories (response scale) and can be assigned to the response categories, including the categories “refuse” or “don‘t know” without any problem (interviewer presses function key F1 to indicate that the answer was assignable without difficulty);
Answer does not exactly meet the response categories, but the response can be assigned to the response categories without further probes by the interviewer (press function key F2);
Answer is assignable after further probes: Respondent answers directly but must be asked to which response category his answer should be assigned (press F3);
Anticipated answer: Respondent answers already while the question is read by the interviewer (press F4).

11Subcategories for “Non-spontaneous answers to the question”:
Question understanding/acoustics/language: Respondent does not clearly understand the question for acoustic reasons, does not know the language well enough, or the telephone connection is bad and there is noise on the line (press F5);
Concept meaning: The meaning of a concept is not understood. The respondent doesn‘t know the concept or the word(press F6);
Question comprehension: Respondent doesn‘t understand the meaning (sense) of the question. He doesn‘t understand why the question was asked (press F7);
Response categories: Respondent forgot the response categories, response scale too complicated (pressF8).

12Of course, various subcategories can be rearranged according to certain properties and reorganized in new specific response classes like “adequate” or “inadequate”.

Analysis Of Pretest Results

Structure Of Pretest Data File And Types Of Analyses

13The pretest data file for each case contains the following information:
Characteristics of respondent’s interviewer (demographic variables, etc.);
For each question, the response category including refusal information;
For each question and each coding category, the classification code;
Further information about the interview (interview length, interviewer’s impression of respondent’s behavior, such as cooperative attitude, etc.).

14These data permit different types of analyses: the analysis of interviewer statistical differences in code use (frequencies, percentages) across questions and respondents; the analysis of differences in code parameters between types of respondents (male/female, etc.) across interviewers and questions, and the analysis of differences between questions or questions types in code parameters across interviewers and respondents. Examples of these types of analyses are given below. Of course, specific analyses for one interviewer, respondent or question can be done. A prerequisite for these analyses is a sufficient number of respondents and also questions. With a sufficient number of respondents, more complex statistical analyses like factor analysis, regression modeling or cluster analysis could also be done.

Visualization Of Pretest Results: The Interview Process Graph (Ipg)

15For each question, statistics of the different types of coding (like frequencies, percentages, etc.) of refusals, of don’t knows, of inadequate spontaneous responses, of comprehension problems, etc., can be plotted in various types of graphs we call interview process graphs (IPGs). The horizontal axis of an IPG consists of the question numbers appearing in the same order as in the interview. The vertical axis refers to the statistics of certain types of coding. Thus, we can, for example, consider an IPG for the percentage of inadequate spontaneous responses, an IPG for total numbers of inadequate responses, an IPG for the percentages of meaning problems, etc.

16IPGs allow the identification of possible problem zones in an interview and for the analysis of question/item problems in the context of neighboring questions/items which is especially important in the case of large item batteries. They also permit the visualization of learning and adaptation processes occurring during the interview. One could, for example, visualize how fast the respondents learn to handle a certain type of response scale.

17Figure 2 shows an example of an IPG. It is based on a CAPTIQ pretest in a Health & Media Survey which dealt with media use and medical information seeking behaviors. The sample size was 2,000. The questionnaire consisted of 124 questions of different types: simple yes/no questions about diseases and health problems, questions using various kinds of response scales for assessing the time dimension of health related behavior, item batteries for the identification of attitudes concerning different health topics using agreement scales, and questions about knowledge of different diseases and the extent of media use in seeking medical information.

18The size of the pretest sample was 100. The IPG in Figure 2 integrates different types of pretest information for all questions/items of the questionnaire: percentages of spontaneously given adequate and nearly adequate responses, percentages of spontaneously given inadequate responses and percentages of non-spontaneous answer due to a problem. The codes defining these response classes are indicated in the figure. The items indicated by a double star have been presented in a randomized fashion. We see that, for some questions, the percentages of adequate or nearly adequate responses were nearly 100 percent. An example are the thirteen questions named FR5_1 to FR5_13. The high percentages reflect the simplicity of the questions. The respondents were asked whether or not they already suffered from certain diseases. They had only to answer yes or no.

19However, other items tell a completely different story. The item battery FR37_1 – FR37_10, introduced by the phrase: “How well do you feel you’re informed about...” followed by a list of different diseases like cancer/tumor, venereal diseases/Aids, heart condition, diabetes, etc., apparently seems to be more problematic.Respondents had to make a decision on a verbal scale with respect to each disease. The scale values were (in Englisch translation) “very well informed”, “well informed”, “somewhat informed”, “barely informed”, “not informed at all”. In 14% of all cases, the interviewer could elicit an adequate answer only after further probes (spontaneous inadequate answer: Code F3).

20A further example for weaknesses in an item battery is given by the six items labeled FR18_1 to FR18_6. The initial question was: “In the following, I give you some statements people sometimes make with respect to their health. Please tell me if you totally agree, almost agree, almost disagree or totally disagree. Examples of items were:
My health is principally a matter of constitution and luck.
My health is first of all dependent on what I personally do.
My health is determined by physicians.

21On average, in 39% of the cases, the respondents had to modify their spontaneous answers after probing by the interviewers in order to admit an assignment of the answer to an admissible response category. In addition, in 7% of the cases, respondents apparently had problems and asked for clarification which may be seen as an indication of greater task complexity and a higher potential for response error.

22There is still another interesting finding which can well be illustrated with this item battery, but which also occurs in other batteries. Items occurring earlier in the item list showed worse response behavior than items occurring later. This may either indicate the positive effect of further clarification in that respondents are becoming better in coping with the task due to a learning process or because they rely on constant response tendencies.

23The presentation of the first item, FR18_1, causes problems for 17% of the respondents. The problems mainly concern understanding the item or question (7%) and problems with the response categories (6%). In 4% of the cases, the items only needed to be repeated by the interviewers. At the same time, we observed an increase in the proportion of spontaneous adequate or nearly-adequate answers (from 40.4% to 61,7%). The successive items were causing significantly less problems. The relevant percentages of the IPG are summarized once more in table 1.

Table 1: Proportions of Adequate and Inadequate Answers

spontaneous adequate or nearly adequate answer
(F1, F2, F4)

spontaneous inadequate answer (F3)

non-spontaneous answer
due to a problem
(F5, F6, F7, F8)


























Respondent And Interviewer-Specific Analyses

Respondent-Specific Analysis

24The preceding section concentrated on item-specific analyses of pretest data; this is, on the quality of the instrument. The advantage of the CAPTIQ method is that it can handle larger sample sizes which also admit respondent- and interviewer-specific analyses. Thus, questions like “Are there specific subgroups of respondents who have more problems with respect to certain types of questions than other subgroups?” or “Which properties of respondents have the most influence on response behavior?” can, in principle, be investigated.

25As an example, let us consider the relationship between the demographic respondent variables “Gender”, “Age” and “Education” and the response behavior. Table 2 gives an overview of the proportions of various types of adequate and inadequate answers. The proportions are based on a summation of codes over items and interviewers. The table shows significant differences between males and females. Females apparently give more spontaneous inadequate answers and more non-spontaneous answers due to a problem than males. The proportion of spontaneous inadequate answers also increases with age and decreases with education.

Table 2: Respondetn-specific Analyses – Demographic Variables and Response

Table 2: Respondetn-specific Analyses – Demographic Variables and Response

26Though these results are far from surprising, they underline the potential of the method. Similar results have been obtained by Prüfer/Rexroth(1985) in their work on interaction coding and by Reuband(1998).

Interviewer-Specific Analysis

27Already with large pretest sample sizes, simple statistical description can reveal interviewer differences with respect to the classification of behavior types. In the pretest example from the Health and Media Survey, the respondents were randomly selected for the pretest sample and randomly assigned to interviewers so that differences in proportions were not considered confused with other background variables. Table 3 shows, for each interviewer, the proportions of respondents who gave spontaneous adequate or inadequate answers, and non-spontaneous answers due to a problem.

Table 3: Example of Interviewer-specific Analysis – Comparison of Interviewers

Table 3: Example of Interviewer-specific Analysis – Comparison of Interviewers

28One can easily recognize that there are important differences between the interviewers. For example, while interviewer “BM” coded non-spontaneous answers due to a problem in 6% of the cases, interviewers “GA” and “ZI” assigned these codes in only 1,6%. of the cases. Interviewer “ZI” had the highest proportion of the behavior category “spontaneous inadequate answer”. The results indicate that the intense interviewer training did not lead to a full standardization of coding behavior.


29The CAPTIQ method was specifically designed for evaluating CATI instruments with comparatively large pretest samples. The device is far from ideal. In fact, it has to rely on rather robust and coarse coding principles. However, this does not mean that further refinements and modifications could not be done. In this respect, the work presented here only represents a first step. What is needed, in any case, are studies of intercoder reliability.

30It is precisely the coarseness of the method which guarantees its applicability to large pretest samples which, in turn, allows for the application of more sophisticated statistical methods in the analysis of pretest data. Above, only the results of elementary inspections of the IPGs have been reported. More sophisticated analyses could involve factor analyses and clustering of inadequate responses for the identification of problem types, methods of serial statistical analysis, subgroup analyses taking into account age, gender and other socioeconomic variables, etc.

31The use of CAPTIQ is not limited to classical pretest applications which mainly concentrate on question quality. In addition, the method may also be used for the identification of interviewer-related and respondent-related questions of quality. Thus, response behavior is conceived to be decomposable into a respondent part, an interviewer part and a question wording part.

32As a kind of observational pretest method, CAPTIQ should ideally constitute the last link in a chain of pretesting stages all dealing with the improvement of the same instrument. It is clear that, at first, the standard rules for designing good questions should be followed (see Fowler 2001; Fowler/Mangione 1990), although, in most research, this is not the case. Also evaluation systems for questionnaires could be used (see Willis/Lessler 1999) during the first stage. The number of inadequate responses can be expected to be substantially reduced if cognitive pretests are done beforehand. In any case, the procedure serves diagnostic purposes. Though it is not able, in every case, to put into concrete terms what exactly has to be changed in the questions, the procedure can give hints of where to look. It can also indicate problems not due to question wording but rather to respondent or interviewer-related properties.

33CAPTIQ may also be useful if no extensive pretesting can be done. In most surveys, which are not devoted to academic or government research and are done by commercial companies, no extensive pretesting usually takes place because of costs. Questionnaires are designed and then immediately employed in the field. In these cases, the method presented here could offer a quite, cheap and routinely applicable method for the identification of severe questionnaire problems by just inspecting the Interview Process Graph.

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Table des illustrations

Titre Figure 1: The Online Process
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Titre Table 2: Respondetn-specific Analyses – Demographic Variables and Response
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Titre Table 3: Example of Interviewer-specific Analysis – Comparison of Interviewers
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Frank Faulbaum, « Computer Assisted Pretesting of CATI Questionnaires (CAPTIQ) », Bulletin de méthodologie sociologique, 83 | 2004, 5-17.

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Frank Faulbaum, « Computer Assisted Pretesting of CATI Questionnaires (CAPTIQ) », Bulletin de méthodologie sociologique [En ligne], 83 | 2004, mis en ligne le 08 février 2008, consulté le 24 mars 2017. URL :

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Frank Faulbaum

(Sozialwissenschaftliches Umfragezentrum, Universität Duisburg-Essen, Lotharstrasse 65, D 47048 Duisburg;

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