Indirect Observation in Everyday Contexts: Concepts and Methodological Guidelines within a Mixed Methods Framework

Abstract

Indirect observation is a recent concept in systematic observation. It largely involves analyzing textual material generated either indirectly from transcriptions of audio recordings of verbal behavior in natural settings (e.g., conversation, group discussions) or directly from narratives (e.g., letters of complaint, tweets, forum posts). It may also feature seemingly unobtrusive objects that can provide relevant insights into daily routines. All these materials constitute an extremely rich source of information for studying everyday life, and they are continuously growing with the burgeoning of new technologies for data recording, dissemination, and storage. Narratives are an excellent vehicle for studying everyday life, and quantitization is proposed as a means of integrating qualitative and quantitative elements. However, this analysis requires a structured system that enables researchers to analyze varying forms and sources of information objectively. In this paper, we present a methodological framework detailing the steps and decisions required to quantitatively analyze a set of data that was originally qualitative. We provide guidelines on study dimensions, text segmentation criteria, ad hoc observation instruments, data quality controls, and coding and preparation of text for quantitative analysis. The quality control stage is essential to ensure that the code matrices generated from the qualitative data are reliable. We provide examples of how an indirect observation study can produce data for quantitative analysis and also describe the different software tools available for the various stages of the process. The proposed method is framed within a specific mixed methods approach that involves collecting qualitative data and subsequently transforming these into matrices of codes (not frequencies) for quantitative analysis to detect underlying structures and behavioral patterns. The data collection and quality control procedures fully meet the requirement of flexibility and provide new perspectives on data integration in the study of biopsychosocial aspects in everyday contexts.

Keywords: indirect observation; mixed methods; quantitizing; systematic observation; textual materials; verbal behavior.

Figure 1

(A–D) The lags are shown on the X-axis and the probabilities on the Y-axis. Based on the results from Table 5b, the values corresponding to the unconditional probabilities (first row) are indicated by the horizontal line parallel to the X-axis (e.g., 0.35 for category A). Also shown are the values for each of the conditional probabilities for each category and lag. These values are linked by a (generally uneven) line for each category. The horizontal line parallel to the X-axis represents the upper limit for the effect of chance. Accordingly, any conditional probabilities in the subsequent lags that are higher than the unconditional probability for the corresponding category are significant and hence form part of the behavioral pattern.

Figure 2

Behavioral pattern extracted after assigning significant conditional behaviors (behaviors with a conditional probability greater than the unconditional probability) to each lag. The behavior pattern extracted from the presented illustration exhibits a regularity consisting of expressions of sorrow or sadness being followed by expressions of self-perceived improvement and these expressions, in turn, being followed by joy at having overcome the problem. From there, the pattern bifurcates, leading either to the initial situation of sorrow and sadness or to expressions of self-perceived worsening.

Figure 3

Optimized corrected behavioral pattern following construction of a confidence interval around the unconditional probabilities. The corrected pattern reveals the typical alternation seen in patients with endogenous depression.

Figure 4

Polar coordinate map showing the vectors for the categories A (focal category), B, C, and D. As indicated in the legend of Table 8, A is the focal behavior and expressions of sorrow or sadness activate expressions of self-perceived improvement (Quadrant IV) and joy at having overcome the problem (Quadrant 1). The focal behavior is not self-generating (Quadrant III). Additionally, expressions of sorrow or sadness do not generate self-perceived worsening (Quadrant II), although self-perceived worsening does generate the focal behavior.

Figure 5

First of the 13 T-patterns detected in the data from Table 3a (p < 0.05).