Psychological science has shown a growing interest in the study of everyday life. New methodologies have been proposed for the within-person study of real-time biopsychosocial aspects in their natural settings (Bolger et al., 2003; Conner and Lehman, 2013; Reis, 2013; Portell et al., 2015b,c). New technologies have made it possible to record spontaneous behavior—that is, behavior that is not elicited by a researcher but forms part of the subject's behavioral repertoire in his or her natural context (see e.g., Mehl et al., 2001). Compared with elicited behavior, spontaneous behavior has the advantage of occurring in a natural context and natural situation, so it is not influenced by extraneous variables such as a non-natural context or social desirability based on researchers' expectations. However, this area of study remains highly complex, particularly when it comes to obtaining quantitative indicators that make it possible to reconstruct the “who,” “what,” “how,” and “when” of events of interest and position these events in the individuals' ecological niche. The difficult task of obtaining quantitative indicators of spontaneous behavior in everyday contexts has been further complicated by the long-standing clash between the qualitative and quantitative paradigms in psychology. Mixed methods research (Johnson et al., 2007) has provided valuable resources for combining qualitative data derived from non-spontaneous behavior (e.g., questionnaire responses) and narrative data derived from natural settings (e.g., a life story). Nevertheless, the merging of qualitative and quantitative perspectives in cases where only spontaneous behavior is of interest has been little explored. In this article, we describe a mixed methods approach grounded in observational methodology (Anguera, 2003) that we believe fills this gap. The proposed approach combines the strengths and offsets the weaknesses of the qualitative and quantitative perspectives.

We present a methodological framework for studying everyday behavior using a rigorous scientific approach based on indirect observation that involves “liquefying” transcribed verbal material or texts from original settings. The process involves the quantification of qualitative data using techniques that are based on order or sequence of events rather than on traditional frequency measures. The approach proposed is perfectly compatible with any guiding theoretical framework whatsoever; this method is not linked to any concrete theoretical model, it offers numerous methodological opportunities, and it has the potential to lead to significant developments in the field of studying everyday behaviors. This approach differs from previous work in this area (Sandelowski, 2001; Sandelowski et al., 2009; Seltzer-Kelly et al., 2012; Bell et al., 2016) in that it analyzes the order and sequence of events. The parameters of frequency (which only indicates the number of occurrences), order (which also provides information about sequence), and duration (which, in addition to the aforementioned information, also indicates the time in conventional units) provide a higher degree of data consistency (Bakeman, 1978). The use of the order parameter, with the introduction of sequentiality, entails added value of extraordinary importance (Sackett, 1980, 1987; Bakeman and Gottman, 1987; Magnusson, 1996, 2000, 2005, 2016; Sánchez-Algarra and Anguera, 2013; Portell et al., 2015a).

The presented liquefying method enables the systematic analysis of minor details that arise in a multitude of situations involving text (e.g., conversations, speeches, diary, or blog entries) with a level of granularity (Schegloff, 2000) that enables these “natural texts” to be analyzed in combination with other contextual data. The approach is applicable to both conventional and new forms of communication (e.g., WhatsApp messages), regardless of format or source. The source may be verbal behavior (informal conversations, focus group discussions, etc.) or documentary material (diaries, narratives, etc.), including in some cases graphic material, such as photographs and drawings.

Most of the solutions proposed to date for transforming text into quantitative data are either qualitative (e.g., ethnographic methods) or quantitative. Our proposal, however, takes a mixed methods approach in which spontaneously generated qualitative material is transformed into quantifiable code matrices.

In this article, we discuss key aspects of our proposed system. We analyze the concepts and meaning of systematic observation and one of its two branches, indirect observation, alongside key concepts of mixed methods research. We also look at types of qualitative data used in indirect observation and describe a methodological framework for building ad hoc observation instruments, creating matrices of codes for the data collected, and analyzing data and checking their reliability. Finally, we present a protocol specifically designed for indirect observation with examples from each of the stages in the process.

Psychologists work in a wide range of fields and subfields that correspond to everyday life situations. To name just a few examples, they are involved in health education programs in nurseries and nursing homes, prosocial programs in primary schools, exercise programs for the elderly, social support programs in neighborhoods, or communities with families of multiple nationalities, AIDS prevention programs for adolescents, support programs for families with a history of child abuse or negligence or families of young car crash victims, relaxation programs for athletes, and social programs in prisons or juvenile correctional institutions. Systematic observation can make important contributions to the study of spontaneous behavior in a vast range of everyday contexts.

Observation is a useful method for collecting, processing, and analyzing information that cannot be studied in the artificial setting of a laboratory. It enables a largely unbiased analysis of everyday behaviors and interactions that occur naturally (Anguera, 2010). Although systematic observation dates back to the 1970s, it has taken on an identity of its own in the last two decades (Anguera, 1979, 2003; Anguera and Izquierdo, 2006; Sánchez-Algarra and Anguera, 2013). It offers both flexibility and rigor as it is built on sound scientific principles, and this combination makes it ideal for use in many fields (Portell et al., 2015b).

Systematic observation differs from ethnography in that its purpose is not to obtain a narrative account of subjective experiences in a process that requires the participation of the researcher or person being studied. Ethnographic studies require a qualitative approach, but unlike systematic observation, they do not require quantitative analysis and rigorous data quality control. Systematic observation, by contrast, is characterized by highly systematic data collection and analysis, stringent data quality controls, and the merging of qualitative and quantitative methods.

Systematic observation follows the four fundamental stages of scientific research: formulation of a research question, collection of data, analysis of data, and interpretation of results. The wealth of data collected in an observational study provides researchers with the opportunity to capture valuable chunks or snippets of everyday realities, without having to specifically ask for the information (there are no interviews, questionnaires, or psychological tests). In addition, it allows the researcher to study spontaneous behavior in a natural, uncontrolled environment.

Everyday activity in context is the cornerstone of observational studies. It is the source of a rich fabric of information that the psychologist/researcher needs to tap into in order to extract relevant information that is subsequently processed systematically to produce a set of “net” data that can be analyzed both qualitatively and quantitatively.

The study of everyday activity provides insights into the diverse behaviors and events that occur throughout a person's life. It provides thus a privileged vantage point from which to observe changes, but everyday life is a highly complex, dynamic process replete with information that is often not even known to exist (Anguera, 2001). Its study requires the examination of diverse phenomena at different levels of a pyramid-like structure. At the top of the pyramid, psychologists analyze how individuals go about their lives and gradually become familiar with what has shaped their life course. As they move down the pyramid, they discover everyday realities at different levels (family, career, social relationships, hobbies, etc.) and come to understand how these are influenced by interacting factors, such as health, satisfaction of needs, and conflicts.

According to Mucchielli (1974) observation equation, O = P+I+Pk−B, observation equals perception plus interpretation plus previous knowledge minus bias. Observation thus is not possible unless what is being observed is perceivable. Perceptibility is a key concept when it comes to differentiating between direct and indirect observation (Anguera, 1979, 2003). In indirect observation, it is always incomplete, and Mucchielli's equation is only partially fulfilled.

In direct observation, perceptibility is considered to be complete when what is being observed (whether in situ or through video or audio recordings) can be captured by visual or auditory senses. In anthropology, for example, the subfield concerned with the study of visual representations is known as visual rather than observational anthropology. Modern-day technology permits maximum levels of precision in visual and auditory perception (Escalera et al., 2009; Bautista et al., 2015) and minimizes the need for interpretation.

Although everyday contexts can take countless shapes and forms, the levels of response (or criteria or dimensions) that can be directly observed are similar. Facial expressions, for example, can be analyzed by software such as Face Reader, which can distinguish between facial and emotional mimicry. Gestures, in turn, which also have an important role in human communication (Holle et al., 2012; Mashal et al., 2012), even in children (Lederer and Battaglia, 2015) can be effectively analyzed using programs such as NEUROGES+ELAN (Lausberg and Sloetjes, 2009, 2016). Finally, vocal behavior (Russ et al., 2008) can be analyzed using sound analysis software. Non-verbal manifestations, or “expressiveness,” are interesting external indicators of a person's emotional state (Rodriguez et al., 2014), although adequate quality control is needed to reduce bias.

While aspects of human communication such as facial expressions, gestures, posture, and voice tone are fully perceivable through visual or auditory channels, they are frequently accompanied by verbal behavior, which has very different characteristics in terms of perceptibility. Indirect observation is an appropriate method for studying both verbal behavior and textual material, whether in the form of transcripts or original material produced by the participants in a study.

Verbal behavior transmits messages and both these and the channels through which they are transmitted can take many shapes and forms. Messages are analyzed differently depending on whether they are spoken or written. Written forms of expression (e.g., self-reports, diaries, biographies) are largely considered to be narratives. Narrative studies have been used in qualitative methodology for many years and have both strengths and shortcomings. One of their main strengths is their adaptability to very different situations and contexts. Narrative studies provide insights into a person's true nature and help to understand their experiences and needs (Riva et al., 2006). They have been used, for example, in a wide range of settings, such as secondary schools (García-Fariña, 2015; García-Fariña et al., 2016), high schools (Tronchoni et al., 2018), family gatherings (Gimeno et al., 2006), support groups for patients (Roustan et al., 2013), therapeutic interaction (Blanchet et al., 2005), and group therapy for adolescents (Arias-Pujol and Anguera, 2017). One of their shortcomings is that perceptibility is limited by the documentary nature of the texts, and it is not uncommon for different researchers to draw different conclusions from the same text.

Human communication does not simply refer to the transmission of information. It involves numerous aspects that vary according to content, the people transmitting or receiving the message, their relationship (hierarchy, previous interactions, etc.), the flow of data or metadata, and the interpretative context. In addition, changing lifestyle habits and new technologies have led to new forms of human communication (Bavelas and Chovil, 2000), such as WhatsApp messages and blog posts, extending the traditional dichotomy between verbal and non-verbal behavior established by the classical sociologist Weick (1968, 1985). In a recent study, for example, Radzikowski et al. (2016) analyzed Twitter messages in a quantitative study on the rubella vaccine, and as stated by Hardley (2014, p. 34), “Over many decades, surveillance methods (often termed “indicator based” methods) have been developed and refined to provide disciplined, standardized approaches to acquiring and recording important information. More recently, ubiquitous and unstandardized data collected from the Internet have been used to gain insight into emerging disease events.”

Indirect observation can be considered a valid scientific method (Webb et al., 1966; Anguera, 1991, 2017, in press; Behar, 1993; Morales-Ortiz, 1999; Morales-Sánchez et al., 2014). It uses similar techniques to systematic observation, and as a procedure, it is structurally identical, although there are important differences dictated by the nature of the source data (verbal behavior and text).

Indirect observation involves the analysis of 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). The addition of seemingly unobtrusive objects can also provide important 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 (Morales-Ortiz, 1999; Morales-Sánchez et al., 2014).

Narratives are an excellent vehicle for studying everyday life through indirect observation, and one option for studying them is to apply a procedure for systematizing and structuring the information through quantitization. This approach makes it possible to integrate qualitative and quantitative elements.

The data used in indirect observation invariably start out as qualitative and the source material varies according to the level of participation of the person being observed and the nature of the source (textual or non-textual).

Common sources of material used in indirect observation studies include:

Recordings of verbal behavior as it occurs (normally in mp3 files). There may be single or multiple dialogues and it is essential to clearly distinguish between the different “voices” recorded.

The above sources of information give rise to a varied set of data that provides empirical evidence and can position specific events and everyday behaviors along a continuum of time. Finally, the information available becomes progressively richer as one gains access to several sources of documentary material.

As mentioned, the material used to collect data in indirect observation is only partly perceivable (Anguera, 1991) and any conclusions made need to be inferred by a researcher drawing from a theoretical framework or taking a position. This is the main challenge in indirect observation. In the system we propose, rigorous application of a carefully designed observation instrument by duly trained observers offers the necessary guarantees of data reliability. Although direct and indirect observation may vary in terms of source material, level of interpretation, and level of participation, the two methods share a scientific procedure that when properly applied can provide quantitative indicators of the processes underlying everyday behavior.

Mixed methods research has been increasingly embraced by the scientific community over the past 15 years (Creswell et al., 2003; Johnson et al., 2007; Tashakkori and Teddlie, 2010; Onwuegbuzie and Hitchcock, 2015). The mixed methods approach involves the collection, analysis, and interpretation of qualitative and quantitative data for the same purpose and within the framework of the same study; some authors have even raised the approach to the rank of paradigm. Molina-Azorín and Cameron (2015) acknowledge that mixed methods research is not easy to conduct and requires considerable time and resources. Nonetheless, it is a movement that is gradually gaining supporters. As stated by Leech and Onwuegbuzie (2009) and Onwuegbuzie (2003), mixed methods research lies on a continuum between single-method and fully mixed studies, although the scientific community has yet to agree on which position it holds along this continuum. That said, it is generally agreed that the position will depend on the research objective and the nature of the data, analyses, and level of inference.

Overall, mixed research is largely understood as “a synthesis that includes ideas from qualitative and quantitative research” (Johnson et al., 2007, p. 113). However, this is a very broad framework in which many gaps need to be filled. In the case of indirect observation, the methodological approach must be extremely rigorous as we are dealing with situations in which substantive areas merge with the multiple realities of everyday life.

The exponential growth of mixed methods research in recent decades has generated certain inconsistencies in terms of terminology and definitions. We therefore believe that it is first necessary to clarify the meaning of method/methodology and to discuss the multiple meanings attached to the term “mixed method” before we present our methodological framework for indirect observation.

Greene (2006, p. 93) proposed a broad description of the term “methodology,” understood as an inquiry logic that admits different forms of data collection (questionnaires, interviews, observational datasets, etc.), methods of research (experimental, ethnographic, etc.), and related philosophical issues (ontology, epistemology, axiology, etc.). Greene also refers to specific guidelines for practice, which distinguish between methods that obviously vary in terms of design, sampling, data gathering, analysis, etc. We consider that systematic observation fits with Greene's definition of methodology (Anguera, 2003), although we have not always used the term. We also agree with the following statement by Johnson et al. (2007, p. 118): “It is important to keep in one's mind, however, that the word methods should be viewed broadly.” Accordingly, in the approach we describe in this article, we also consider indirect observation to be a method in the broad sense of the word.

Johnson et al. (2007, p. 123) defined mixed methods research as “the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al., 2007, p. 123). They formulated this definition after asking 19 renowned researchers in the field (Pat Bazeley, Valerie Caracelli, Huey Chen, John Creswell, Steve Currall, Marvin Formosa, Jennifer Greene, Al Hunter, Burke Johnsson and Anthony Onwuegbuzie, Udo Kelle, Donna Mertens, Steven Miller, Janice Morse, Isadore Newman, Michael Q. Patton, Hallie Preskill, Margarete Sandelowski, Lyn Shulha, Abbas Tashakkori, and Charles Teddlie) to send in their definition of the term “mixed methods” by e-mail.

We fully agree with the definition proposed by Johnson et al. (2007) and it provided us with the necessary elements to draw up our methodological framework for indirect observation. The success of any mixed methods approach depends on the adequate mixing or integration of qualitative and quantitative elements. Numerous authors have analyzed the term “mixing” in an attempt to provide guidance on the processes required to achieve a seamless result (Bazeley, 2009; O'Cathain et al., 2010; Fetters and Freshwater, 2015). Qualitative and quantitative data can be mixed in three different ways, aptly summed up by Creswell and Plano Clark (2007, p. 7): “There are three ways in which mixing occurs: merging or converging the two datasets by actually bringing them together, connecting the two datasets by having one build on the other, or embedding one data set within the other so that one type of data provides a supportive role for the other data set.” For our proposal, we chose the second form: connecting two databases by having one build on the other. According to Sandelowski et al. (2009), this connection can be achieved through transformation, i.e., by quantitizing qualitative data or qualitizing quantitative data. In our indirect observation framework, we transform non-systematic qualitative data into a format suitable for quantitative analysis.

Mixed methods research is marked by a persistent scientific gap that requires powerful solutions rooted in two key challenges in the field of indirect observation. These two challenges, discussed in this article, are (a) how to rigorously transform qualitative textual material derived largely from everyday human communication into matrices of codes, and (b) how to subsequently analyze these codes using quantitative methods suited to the categorical nature of the data in order to uncover the underlying structure. The proposed transformation system breaks away from the classical theoretical framework of mixed methods, which simply involves integrating qualitative and quantitative elements. The key difference is that it contemplates systematic observation, and hence indirect observation, to be a mixed method in itself (Anguera and Hernández-Mendo, 2016; Anguera et al., 2017a).

Integration of qualitative and quantitative elements is the key to any mixed methods approach (Creswell and Plano Clark, 2007; Bazeley, 2009; O'Cathain et al., 2010; Maxwell et al., 2015). Our approach adds another element: the liquefaction of verbal behavior and texts. This process consists of schematically transforming “solid” textual material into “liquid” matrices of codes apt for quantitative analysis (Anguera et al., 2017b; Anguera, in press). The quantitative processing of originally qualitative data with the aim of detecting hidden behavioral patterns or underlying structures, for example, adds an element of robustness to the integration of qualitative and quantitative data, particularly in the case of everyday life events and behaviors.

Talkativeness and text, for example, can now be analyzed within the framework of mixed methods research using frequency counts (Poitras et al., 2015) thanks to the development of reliable—and extremely useful—measures of verbal productivity and the multiple opportunities offered by modern-day technology (Bazeley, 2003, 2006, 2009). Frequency counts, however, are weak and insufficient measures. Considering that “methodological plenitude” (Love, 2006, p. 455) is not always attainable in applied research, the mixed method framework offers new and interesting possibilities for indirect observation.

The combined use of qualitative and quantitative approaches has been tried and tested in multiple studies and has also been analyzed in several systematic reviews (Elvish et al., 2013). In the following sections, we show that it is necessary to start with qualitative inputs and to then quantify these in a process that ensures reliability throughout the various stages.

The empirical process in indirect observation starts with the collection of qualitative data. While the characteristics and standards that guarantee quality are perfectly outlined in the literature on quantitative methodology, the same cannot be said of qualitative methodology. Qualitative methodology offers enormous flexibility, but interpretations on content and form vary and are not free of controversy. Content provides personal and interpersonal information, which stems from experiences that are temporally unstable and highly influenced by the context and versatility of the moment. As for form, the tools used to support indirect observation (narratives, biographies, self-reports, life stories, in-depth interviews, etc.) cause doubt and distrust in many researchers, who, in the absence of standardized tools, question their stability and consistency.

Much has been written about the forms used to structure narratives (e.g., Hurwitz et al., 2004; De Fina and Georgakopoulo, 2015; Riessman, 2015), and qualitative data can be gathered using many tools, including interviews (e.g., Riera et al., 2015), biographies (e.g., Lindqvist et al., 2014), children's vignettes (e.g., Jackson et al., 2015), focus group vignettes (e.g., Brondani et al., 2008), telephone interviews (e.g., Björk et al., 2014), self-reports (e.g., Coutinho et al., 2014), focus group recordings (e.g., McLean et al., 2011), and participant observation (e.g., Caddick et al., 2015). In our case we are specifically interested in qualitative datasets within the framework of indirect observation. Although systematic observation dates back to the 1970s, it has taken on an identity of its own in the last two decades (Anguera, 2003; Anguera and Izquierdo, 2006; Sánchez-Algarra and Anguera, 2013; Anguera et al., 2017a). Indirect observation shares many of the characteristics previously described for systematic observation, namely, highly systematic data collection and analysis, strict data quality controls, and an approach that requires the merging of qualitative and quantitative techniques.

In these next sections, we are going to describe, and illustrate with examples, the stages and sub-stages involved in an indirect observation study. We will focus largely on the extraction and transformation of information from textual material produced using conventional or newer channels of communication in a variety of formats (handwritten letters, reports, transcriptions of group meetings, and interviews, etc.), irrespective of origin (e.g., informal conversations or focus group discussions or documentary material).

Extracting information on human behavior from text and transforming it into suitably systematized and organized categorical data, without loss of key information, is a major challenge in the Behavioral Sciences. In addition, the process must offer sufficient scientific and ethical guarantees and produce results in a format that can be rigorously processed using any of a range of quantitative techniques available for analyzing categorical data.

Our text-liquefying process consists of six stages: (1) specification of study dimensions, (2) establishment of segmentation criteria to divide the text into meaningful units, (3) building of a purpose-designed observation instrument, (4) coding of information, (5) data quality control, and (6) quantitative analysis of data. Table 9 presents detailed steps and guidelines for the “liquefication” of indirect observations. Each of the steps will be explicated within the following sections.

We have presented a structured procedure detailing the successive stages of the method we propose for studying verbal behavior and/or textual material in an indirect observation study (Table 9). Our aim was not to offer a general approach to systematic observation from the perspective of indirect observation, as guidelines already exist for the reporting of systematic studies within observational methodology (Portell et al., 2015a). Our aim rather was to introduce the reader to the key concepts of indirect observation studies and provide step-by-step guidance on how to perform such a study. The procedure we propose is summarized in Table 9 and has already been applied in studies from different fields (Vaimberg, 2010; García-Fariña et al., 2016; Arias-Pujol and Anguera, 2017).

Within the broad framework of mixed methods, we have presented indirect observation as a structured method consisting of different steps designed to guarantee scientific rigor. The method consists of the quantitization of qualitative data derived from verbal or textual material to produce code matrices which, following appropriate organization and rigorous quality control procedures, can be analyzed using robust, rigorous, and objective techniques. In a sense, we liquefy the text into a form suitable for quantitative analysis.

Although the materials that support direct and indirect observation are different, the methodological proposal described in this paper shows that both forms of observation share a systematic procedure in which adequately trained observers apply a robust, reliable purpose-designed observation instrument to produce quantitative indicators of the many processes underlying everyday behavior. The main strengths of our approach are that it enables the merging of data from different sources and offers the possibility of taking advantage of the continuous advances in information and communication technologies to study aspects of biopsychosocial behavior in everyday contexts. There are two main limitations. On the one hand, the dimensions in an indirect observation study depend largely on a theoretical framework and a conceptual framework, and these may be lacking. On the other hand, observation instruments comprising category systems, either alone or combined with a field format, also require a theoretical framework. However, the proposed approach has the advantage of allowing all data obtained from narratives to be included in the study, even those which do not fit with the theoretical framework or are contradictory. In fact, the validation of the coding process entails, among other things, checking that no new information has been added, that no information has been eliminated, and that the meaning of the information has not been altered. In this way, there is no omission of information that could lead to bias. This information can be included using bottom-up or top-down processes (Anguera, 1991; Anguera et al., 2007), in other words, the narratives are categorized on the basis of the chosen theoretical framework (top-down) and the theoretical framework is adapted on the basis of the narratives given (bottom-up). An exclusively quantitative study would entail the loss of sensitive and relevant information about the spontaneous behavior, as it would require excluding all variables not envisaged in the chosen theoretical framework. Hence our insistence on the enormous potential of mixed methods research, which suitably integrates both qualitative and quantitative elements.

This work presented a novel approach, based on sequence of occurrence, for transforming qualitative data into quantitative data that can be analyzed using robust quantitative techniques. Additionally, it is important to note that it is possible, at any time during the analysis, to return from the quantitative data to the narrative data. As a result, this approach presents advantages of both qualitative and quantitative methods, at the same time it covers weaknesses of both methods.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.