Research Methods for Studying Narrative Identity

Abstract

This article provides a primer for researchers seeking an introduction to quantitative narrative research methods. It represents a consensus document of most common practices used by the coauthors. Key elements of conducting narrative research (e.g., asking narrative questions, designing narrative prompts, collecting narratives, coding narratives) are discussed along with limitations to this approach and future directions.

Keywords

narrative identity narrative methods personality development personality structure self/identity

Research in wide-ranging subfields of psychology has capitalized on the power of narratives as a methodological tool, examining diverse topics such as psychotherapy (Adler, 2012), alcoholism (Dunlop & Tracy, 2013), gender (Grysman, Merrill, & Fivush, 2016), mid-life development (Pals, 2006), personality change (Lodi-Smith, Geise, Roberts, & Robins, 2009), generativity (McAdams, 2006a), family processes (McLean, 2015), emotion regulation (Pasupathi et al., 2015), ethnic identity development (Syed & Azmitia, 2010), meaning-making (McLean & Thorne, 2003), adult attachment (Waters, Brockmeyer, & Crowell, 2013), physical and mental health (Adler et al., 2015), political orientation (McAdams et al., 2008), and self-regulation (McAdams, Hanek, & Dadabo, 2013). Personality psychologists have made especially strong contributions to this methodological approach in work on narrative identity, the story of the self that weaves together the reconstructed past, the perceived present, and the imagined future, providing the individual with a sense of unity and meaning (e.g., McAdams, 1995; McAdams & McLean, 2013; McAdams & Pals, 2006; Singer, 2004).

Despite this widespread use of narrative, social and personality psychologists still have many questions about what narrative is and how to use it as a tool. Indeed, it seems a critical barrier to researchers’ use of narrative methods is not lack of interest but lack of information. Thus, we draw on our collective knowledge as narrative researchers to describe common approaches to quantitative narrative research as a primer for scholars new to the field. We focus on four key aspects of narrative methods: asking narrative questions, designing narrative prompts, collecting narratives, and coding narratives (see Figure 1 for an overview). Although we primarily employ examples of narrative methods in the study of narrative identity, we also interweave examples from other domains to highlight the broad applicability of narrative. Importantly, this primer should be seen as a starting point for those interested in beginning narrative research and neither an exhaustive tutorial nor a detailing of advanced topics.

Figure 1.

Overview of the narrative research process.

Theoretical Frame

The theoretical rationale for using narrative methods has been elucidated elsewhere (e.g., Adler, Lodi-Smith, Philippe, & Houle, 2016; Habermas & Reese, 2015; McAdams & McLean, 2013; McLean & Syed, 2015); here, we address two common critiques of this approach: that the stories people tell about their lives are not necessarily true or accurate and that narrative methods are merely labor-intensive self-report instruments. Both criticisms miss the essential point about narratives.

First, like all autobiographical memories, narratives of personal experiences are dynamically reconstructed representations of events. Each time a memory is recalled, the retrieval process is a complex interaction between the internal neural context and external sociocultural context, modulated by the functions that remembering serves in that moment. Thus, narratives are deeply idiographic, dynamic reflections of how individuals recall their experiences and serve context-specific functions. The purpose of narrative research is to understand these functions and their relationships with other correlates and outcomes, not the veracity of the memories. Narratives are subjective constructions that have objective impacts. For example, individual differences in autobiographical narratives are associated with the successful maintenance of sobriety among alcoholics (Dunlop & Tracy, 2013) and the desistence from crime among ex-convicts (Maruna, 2001), and individual differences in the trajectory of narrative development precede changes in mental health among psychotherapy clients (Adler, 2012).

Second, narratives are not simply converging measures of identity; narratives are constitutive of identity in that how we make sense of our experiences and who we perceive ourselves to be are reciprocally related across development (McAdams & Pals, 2006; McLean, Pasupathi, & Pals, 2007). Moreover, because narratives are deeply embedded in sociocultural interactions across the life course, how individuals story their lives reflects both explicit efforts at meaning-making and implicit modes of being in the world (Fivush & Merrill, 2016). Thus, narrative approaches to identity are not reducible to self-report measures but rather represent a different level of analysis that assesses subjectivity in unique ways (Adler et al., 2016).

Finally, narratives allow researchers to ethically and meaningfully understand lived experiences in context (Fivush, 2010). Appreciating the voices and experiences of participants can be done by examining the dynamic and contextualized nature of stories, often accomplished via qualitative inquiry (e.g., Hammack, 2008; Josselson, 2009) as well as in nomothetic, hypothesis testing or exploratory quantitative approaches. We focus on the quantitative approach in this primer because we most frequently receive questions about that approach.

Asking Narrative Questions

Studies that use narrative data ought to be designed to ask questions that narratives are especially well suited to address. At its core, research using narrative methods is concerned with meaning-making. The empirical study of narrative is therefore a science of subjectivity, one that employs the tools of science for examining issues of personal meaning. Narrative questions are concerned with the explicit content of stories (e.g., emotional language), the implicit content of stories (e.g., themes), and/or the structural aspects of stories (e.g., coherence). Narrative methods are particularly well situated to examine meaning-making processes that concern the self. Other approaches for examining meaning and identity may tap the extent to which participants feel their lives are meaningful, but in crafting personal narratives, participants demonstrate it. Narrative methods therefore allow for the enactment of meaning rather than a report of the perception of meaningfulness.

The diversity and breadth of the field is certainly beneficial, but it has also led to some diffusion and redundancy. Recently, narrative scholars have begun to work toward providing some structure to the range of narrative variables that have been examined in relation to personality. McAdams and McLean (2013) identified six commonly examined narrative constructs, highlighting those variables that have received substantial attention. Adler, Lodi-Smith, Philippe, and Houle (2016) developed an organizational framework for narrative variables related to psychological well-being and classified them into four categories: motivational themes, affective themes, themes of integrative meaning, and structural aspects of narratives. Table 1 provides an overview of these overlapping systems.

Table 1.

Commonly Assessed Narrative Variables (a Representative Set).

Narrative Category (Adler, Lodi-Smith, Philippe, & Houle, 2016)	Narrative Variable	Definition
Motivational themes	Agency^a	The degree to which the protagonist can initiate changes on his or her own, achieve some degree of control over the course of his or her experiences, and affect his or her own life. This is often exemplified by aspects of self-mastery and control, status striving, achievement, and power.
	Communion^a	The degree to which the protagonist aims to have a sense of togetherness and harmony with other people or their environment, to share, help, connect to, and care for others.
	Growth goals	The degree to which the protagonist makes intentional efforts to guide his or her self-development in a personally meaningful direction, oriented toward personal growth.
Affective themes	Contamination^a	The protagonist describes an event that moves from a good, affectively positive scene to a bad, affectively negative scene, in which good things turn into bad outcomes.
	Redemption^a	The protagonist describes an event that moves from a bad, affectively negative scene to a subsequent good, affectively positive scene. The bad is redeemed, salvaged, mitigated, or made better in light of the ensuing good.
	Positive resolution^a	The protagonist is able to come to peace with, or let go of, a challenging event.
Themes of integrative meaning	Accommodative processing	The degree to which the protagonist has been forced to change, centrally and qualitatively, his or her views of the self and world, by actively experiencing a paradigmatic shift that requires a revision of structures and/or important changes in response to the environment.
	Exploratory processing^a	The degree to which the protagonist makes an active and engaged effort to explore, reflect on, or analyze a difficult experience with openness to learning from it and incorporating a sense of change into the life story.
	Meaning-making^a	The degree to which the protagonist learns something or gleans a message from an event (e.g., no meaning, vague meaning, learning concrete lessons, gaining deep insights).
Structural elements of narratives	Coherence	The degree to which the narrator situates the characters of his story and their actions in a specific context, the story follows a temporal sequence of goal-oriented actions that are culturally recognized, emotions are clearly expressed in support of the point of the narrative, and narrative is integrated into larger life themes and meanings.
Structural elements of narratives	Complexity	The degree of engagement in the narrative processing, as shown by depth of thought and nuance, such as seeing a variety of perspectives or emotions.

Source. This table was adapted from Table 2 in Adler, Lodi-Smith, Philippe, and Houle (2016) and from Table 1 in McAdams and McLean (2013).

^aThe variable appeared in both sources.

In the service of building a cumulative science of narrative identity, we recommend that researchers explicitly situate their investigations in the context of these organizational systems and the narrative variables listed in Table 1, when appropriate. That said, these proposed organizational structures are not a final taxonomy, and there is still much landscape to be filled in. For example, the focus on associations with well-being may have narrowed the landscape of variables examined. While there is no single compendium of existing coding systems, many of the most commonly used systems are presented in the reviews by McAdams and McLean (2013) and Adler and colleagues (2016) and are also summarized in published works.

The substantial work that has been invested in identifying core narrative elements provides a strong foundation for generating confirmatory research hypotheses. At the same time, many gaps in our knowledge require that exploratory research be conducted as well. And, of course, the two can work together in productive ways. For example, McAdams (2006a) used a series of studies on the life stories of highly generative American adults to derive a core set of themes that define what he termed “the redemptive self.” Building on this descriptive work, McAdams and Guo (2015) empirically tested the association between these core themes and psychosocial outcomes, demonstrating how individuals use different configurations of narrative identity as resources for meaning. Thus, a key element in narrative study design is developing exploratory questions or hypotheses appropriate to narrative data.

It is also possible to conduct research on narrative identity on data that have not specifically been collected with narrative questions in mind. Many studies include free-response qualitative data, which may lend itself to narrative investigation. For example, Silver, Holman, McIntosh, Poulin, and Gil-Rivas (2002) collected a data set designed to examine psychological adaptations following the September 11, 2001, terrorist attacks in the United States. Participants were provided three prompts asking them to write about their experiences. Adler and Poulin (2009) used these responses to look at the emotional sequencing of narratives and their association with psychological well-being. The key question to ask about such existing data is whether it is sufficiently narrative. In other words, were participants telling stories or were they simply reporting thoughts, feelings, ideas, and so on? There is no commonly used definition of “narrative,” but if the majority of participants are not telling stories—that is, experiences of specific life events that unfold over time—the data do not lend themselves to productive narrative inquiry.

Along those lines, there is a related and essential distinction between narrative and linguistic methods (e.g., Pennebaker, Mehl, & Neiderhoffer, 2003). Although narratives are comprised of words, their meaning is an emergent property that cannot be reduced to word choice, despite the fact that word choice does influence a narrative’s meaning. Indeed, studies that have empirically compared narrative and linguistic approaches within the same data set have failed to identify a linguistic substrate of common narrative themes (Weston et al., 2015), and word count is rarely meaningfully correlated with many narrative themes (Adler et al., 2016). Linguistic methods provide data that may complement those derived from narrative approaches (such as emotional tone, which is often readily apparent in word choices) but are not interchangeable (Weston et al., 2015).

Designing Narrative Prompts

Obtaining narrative data is dependent on the crafting of narrative prompts, which ought to always be driven primarily by the research questions. One commonly used instrument for collecting extensive narrative data in the personality literature is the Life Story Interview (LSI; McAdams, 2008). This semistructured interview protocol has been used in dozens of studies and has proven to be an effective vehicle for collecting personal stories. It facilitates self-narration by tapping the major components of autobiographical memory, lifetime periods (chapters), and more specific episodes (scenes) while also projecting the interviewee’s life into the future and providing conceptualizing information on personal values and ideology (e.g., Conway & Pleydell-Pearce, 2000).

The LSI is a battery consisting of multiple narrative prompts that range from specific domains of individuals’ lives such as particular challenges to “Key Scenes,” the important life memories that serve as the core of the protocol. Three prompts that have received extensive attention in research on narrative identity are stories about a life high point, a low point, and a turning point (e.g., Cox & McAdams, 2014; McLean & Pratt, 2006). The complete LSI can take 1–2 hr to collect and produce a massive amount of text. Studies seeking to include narrative data, but that do not strive to capture the complete life story, would be well served by including these three scenes if they are appropriately geared toward the research questions.

Some researchers may be interested in more specific life experiences rather than general categories (e.g., turning points). In such cases, the LSI provides a good template for writing prompts. For example, in studies of topics as diverse as personal transgressions (Mansfield, Pasupathi, & McLean, 2015), identity content (e.g., politics, gender, family; McLean, Syed, & Shucard, 2017), the transition to parenthood (Dunlop, Walker, Hanley, & Harake, 2016), and career changes and religious conversions (Bauer & McAdams, 2004), the narrative prompts mirrored those in the LSI. A template for such a prompt is:

Please describe a scene, episode, or specific moment in your life that stands out as [emblematic of the topic of interest]. Please describe this scene in detail. What happened, when and where, who was involved, and what were you thinking and feeling? Also, please say a bit about why you think this particular moment stands out to you now and what the scene may say about who you are as a person.

Embedded in this prompt are several central elements: a specific, bounded scene, a request for detail and elaboration, and a request for reflection or meaning-making about the scene. Examining specific life experiences, as opposed to the full life story, allows researchers to ask questions about the meaning of particular experiences and about the ways in which meaning-making processes differ across experiences (e.g., Mansfield et al., 2015).

Prompts may also be designed in ways that differ from the LSI. Whether drawn from the LSI or not, the design of prompts ought to be grounded in the theoretical questions under examination. For example, Singer’s (Blagov & Singer, 2004) self-defining memory prompt provides an alternative prototype that has been widely used in research on autobiographical memory and narrative identity (e.g., Singer, Blagov, Berry, & Oost, 2013). Likewise, the Adult Attachment Interview (AAI; Main, George, & Kaplan, 1985) has been used to elicit narratives that tap the narrative organization of personal experiences with attachment figures (e.g., Waters et al., 2013). Critically, working with such narratives requires an understanding of how the prompt derives from the research question. For example, the LSI turning point prompt assumes the importance of change to identity, whereas a self-defining memory allows more room for the narration of change and stability. In contrast, the AAI assumes nonconscious processes, so the coding systems center on capturing implicit emotional coherence of narration.

Pilot testing newly written prompts, or standard prompts used with novel samples, can ensure that participants have understood the researchers’ intentions, so that the prompts will elicit the kind of data sought (e.g., Syed, 2015). There is no quantitative metric for evaluating the pilot testing of prompts, but examining pilot data can indicate that participants can answer prompts appropriately and provide code-able narrative data.

Narrative data raise unique ethical issues. First, reporting personal narratives can be a taxing, emotional experience, particularly for certain types of events (e.g., low points, traumas, and transgressions). The consent, data collection, and debriefing procedures should be organized with this in mind. It may be especially important to clearly define the task before consent, check in with participants during data collection, and/or be prepared to make referrals during debriefing. Second, narratives cannot be anonymized in the same way other types of data can. Such issues should inform the consent process (by explicating for participants the ways in which their data might be used), the training of raters (by establishing a standard of professionalism and respect in engaging with the data), the handling of the data (by setting clear guidelines for restricting access), and the presentation of results (by striving to mask particular individuals’ identity while allowing authentic voices to illustrate broader trends). More broadly, these ethical issues pose challenges to data sharing.

Collecting Narratives

Narratives may be collected from participants in either oral or written formats. Interview approaches allow for follow-up questioning and tend to produce more elaborated narratives. They can also help to establish rapport and comfort, which may facilitate disclosure. Written approaches may produce briefer and possibly more coherent responses, reduce interviewer effects, and may facilitate the sharing of sensitive information or stories for which participants might desire more anonymity (cf. Grysman & Denney, 2017; McCoy & Dunlop, 2016).

When collecting oral narratives, interviewers need to be carefully selected for professionalism, as they will need to walk a delicate balance between establishing and maintaining rapport without directing the content of participants’ responses. Training of interviewers, including pilot interviews with feedback, is critical (Jossleson, 2013). We encourage interviewers to begin by explaining that they will be using a protocol and need to adhere to it but will strive to make the interview feel as conversational as possible. In order to assure that participants are exposed to the same types of stimuli, interviewers should refrain from commenting on responses or directing the conversation beyond adhering to the prompts. Remaining engaged listeners and using nonverbal cues to convey their interest are encouraged, and research suggests this will elicit more elaborated responses (Bavelas, Coates, & Johnson, 2000). Interviewers should also strive to elicit responses to each part of the prompt. For example, participants in studies using the LSI often forget to address the last part of the prompts that asks them to reflect on why the scene stands out as important and what it might say about them as a person. The choice to follow-up on this omission depends on the motivating research questions—is it more important to have participants respond to all aspects of the prompt or are omissions of some parts of the prompts in and of themselves meaningful data?

Interviews should be recorded and transcribed precisely; it is not possible to code narratives adequately from audio or video. Many researchers use professional transcription services. If not employing professional services, double-checking all transcriptions is necessary, at minimum. For example, simple deletions such as missing the word “not” can transform the meaning of a sentence (“that was not good” could become “that was good”).

Once transcribed, most researchers code from the transcripts. Although vocal intonation and nonverbal behavior reveal aspects of people’s experiences that are not apparent in the words they use, returning to the recording is primarily done to increase precision in coding statements that are otherwise unclear (e.g., “that was great” could mean what it says, or be said with sarcasm). Methods exist for coding nonverbal material as well (e.g., Harrigan, Rosenthal, & Scherer, 2005), and the field would benefit from systematically investigating these associated components of narration.

Obtaining written narratives is much less time and labor intensive for the investigator, but several key points must be kept in mind. First, it is especially important to pilot written narrative prompts, as there will not be an opportunity to ask targeted follow-up questions to ensure participants are providing appropriate data. Second, the setting of a written narrative assessment is also important. We recommend a quiet room in which the participant can be alone. The presence of others can alter the experience by reducing privacy, creating social comparisons about completion time or process, and merely reminding people of social and self-presentational concerns. Our experience suggests that written narratives obtained in the lab may be substantially higher in quality than those obtained online.¹

Narratives are typically obtained alongside responses to questionnaire data. There are no established best practices about the order in which data should be collected. It may make sense to collect questionnaire data first if narrative data without questionnaire data would not be useful for the analysis. Alternatively, when participation fatigue is a concern, it may make sense to collect narrative data first. Order effects of narratives may be a concern, although two published studies have not found effects on other survey measures (e.g., McLean & Pals, 2008; McLean, Pasupathi, Fivush, Greenhoot, & Wainryb, 2016).

Coding Narratives

Before narratives are presented to raters, they ought to be deidentified and randomized by participant (longer transcripts are typically rated as a set, not randomized by prompt). Deidentification itself can be nuanced (narrative content often includes identifying details), but ensuring that raters are unable to detect any meaningful information about the data that might influence their coding is important. For example, if the focus is on gender differences, researchers may want to remove demographic information for coding, if possible.

Selecting narrative variables for investigation ought to be driven by the hypotheses of the specific study. As noted above, many coding systems have been developed and are readily available for operationalizing these themes within new data sets, such as those listed in Table 1. In some instances, it may be necessary to develop a new coding system for tapping constructs that have not yet been examined. This intensive process is described by Syed and Nelson (2015; see also McAdams et al., 2008). In addition, applying an existing coding system to a new data set is not always straightforward. For example, if participants are of a substantially different developmental stage or background, or if the prompts are quite different, revision of the existing coding system may be necessary. Although we encourage researchers to continue exploring new narrative variables and developing coding systems for operationalizing them, we simultaneously encourage researchers to explicitly relate new systems to existing ones, in both theoretical and empirical ways (to demonstrate convergent, divergent, and incremental validity) when studying topics that have received prior empirical attention.

While there is variation in the field, the most common unit of analysis for coding is the scene. For example, the LSI contains multiple Key Scenes, and typically a single score for each narrative variable being assessed is assigned to each scene. In studies not using the LSI, it is still possible to divide data into scenes, often assigning one score per prompt. This approach allows researchers to conduct between-person analyses at the level of the specific scene (i.e., looking at low points across individuals; e.g., Pals, 2006), at the level of the individual (i.e., averaging scores across all scenes within an individual to create summary scores for comparison across individuals; e.g., McAdams et al., 2008), or for within-person analyses (i.e., comparing high points to low points; e.g., McLean & Pals, 2008).

The first step in coding is training raters to a high standard of interrater reliability. Before any substantial portion of the data is coded, raters must demonstrate that they are using the coding system(s) in reliable and consistent ways. This training phase is crucial to the scientific soundness of the coding, which will ultimately produce the quantitative representation of the narrative data. As Syed and Nelson (2015) point out, interrater reliability is best conceived of as a process, and they outline the many decision points in this process. While a set of statistics will represent this procedure in any report of the study, interrater reliability informs every step of the coding process.

Training is typically undertaken by two raters for each narrative variable being assessed. Narrative coding cannot be completed entirely by a single coder, for that would not allow one to determine the degree of interrater reliability, and larger group of coders may pose challenges to obtaining interrater reliability. Training typically begins by reading the coding system and discussing it to understand the rationale and what the system aims to capture. Then, coders read through a subset of narratives together, discussing how the coding system might be applied. Raters continue this discussion until they reach a growing consensus about the process. During this phase, the coding system may be refined, making additional notations about emerging idiosyncrasies from the match between this particular coding system and this particular data set.

Then, raters individually score a new subset of narratives. These scores are then compared statistically to determine whether interrater reliability has been achieved. There are different approaches to calculating interrater reliability, depending on the nature of the coding systems (see Syed & Nelson, 2015). Raters rarely achieve appropriate interrater reliability on the first attempt, in which case they should discuss disagreements, come to consensus for those data points, refine the coding system if necessary, and then attempt another set of coding independently. This often takes 10–25% of the data set, depending on its size and the frequency of codes (e.g., codes with low base rates may need more narratives to achieve appropriate reliability).

Once an adequate degree of interrater reliability is achieved, raters either split the remaining data and code independently or two raters proceed with coding the remainder of the data (either generating consensus codes or taking means of any discrepancies). During this phase of largely independent coding, it is important to have regular discussions with the raters in order to prevent deviations from the coding system, referred to as “drift” (Wolfe, Moulder, & Myford, 2000). Early on in the independent coding, raters are encouraged to bring any examples that they feel uncertain about to the team for discussion and consensus rating (or averaging). Typically, the number of such examples decreases dramatically throughout the coding process. It is optimal to repeat the formal reliability phase of coding to ensure appropriate interrater reliability is maintained throughout the coding process, but at minimum periodic checks should be made to avoid coder drift.

If multiple narrative variables are going to be assessed by the same raters, it is appropriate to code the entire data set for a single variable before coding another variable. This allows raters to remain in a focused mind-set when reading the transcripts, as coding can be like viewing a narrative through a specific lens, seeking to filter the complex material to reveal only certain aspects. Multiple coding teams may be used for examining different themes in the same set of narratives as long as interrater reliability is established and maintained within rater teams.

While coding is underway, we recommend that raters keep track of emblematic examples from the data to represent each coding system (high/low poles of each system or quotes that exemplify an instance-based/categorical system). This is helpful for two reasons. First, when preparing the Method section of a manuscript, it is vital to include quotes that illustrate how the coding system was applied to this dataset in both didactic (explaining the mechanics of the system) and illustrative (sharing actual quotes) ways. Second, from a theoretical perspective, it is essential not to let the presentation of the study stray too far from the actual voices of the participants, so evocative quotes serve to ground the statistics in the stories they seek to describe or explain.

Regardless of how engaging a particular sample of narratives may be, coder burnout may become an issue. Quality coding takes a high degree of attention and close reading and can also be emotionally taxing, depending on the type of narratives. We encourage researchers to intersperse the coding process with other research tasks to keep raters engaged and focused when analyzing narratives and to set limits on the amount of time for any one coding session.

Once all coding is complete, the narrative data may be reconnected with other data from participants, and inferential statistics can be applied to address the central questions of the study, typically involving the relationships between narrative variables and other variables of interest.

Conclusion, Limitations, and Future Directions

This primer has outlined the most common approach to empirical quantitative research with narrative methods. Our hope is that it will both encourage scholars to include narratives in their research and serve as a practical tool to guide their initial forays into the field. In addition to the many theoretical reasons for using narrative methods, narrative data are fundamentally generative. A study that includes only responses to questionnaires, laboratory tasks, or psychophysiological data can only ask so many questions, but narratives offer a nearly endless opportunity for revisiting and for discovery.

However, there are limitations to taking a narrative approach. As should be clear from this review, it is time and labor intensive to collect and code narratives. It is an approach that also demands a certain facility with language and careful work for researchers to ensure that the prompts and questions are personally and culturally relevant to participants. Thus, given the time and effort required of narrative coding, we encourage researchers to first ask whether this is the most effective way of tapping a construct of interest. In making that decision, the topic of incremental validity is especially important: Will examining narrative data provide explanatory power not adequately captured by other methods (Adler et al., 2016)? In addition, it is essential to remember that quantitative coding is not always the most appropriate approach either. Many vital questions in the study of narrative identity beg for inductive, qualitative methods that can reveal complexity and explore new avenues better than that could be obtained by relying on existing coding systems (cf., Hammack, 2010; Josselson, 2009). Such research makes an important contribution to the literature on narrative identity itself as well as sometimes lays a strong foundation for subsequent quantitative work (e.g., Syed, 2015).

There are many directions in which researchers can take the narrative approach, many of which can go well beyond the focus on narrative identity, which we have elaborated here. For example, we suggest that the prompts regarding specific experiences, such as the transition to parenthood, can be applied to all kinds of psychologically meaningful phenomena. Researchers interested in learning more about the content and meaning of the phenomena on which their programs of research focus can employ narrative methods to elucidate such phenomena. That is, narrative can be a powerful tool for studies that are not designed with narrative identity as the primary focus.

One other direction that we emphasize is the utility of narrative for understanding the cultural context of the individual. Given the inherent descriptive quality of an individual’s story, it is a rich source of information about the culture and context of personality and development. Many coding systems and studies have aimed to capture the individual processes of personal development, but there is an opportunity to expand that lens to the cultural context in which identity and personality develops—from the particularities of experience for members of a cultural group, to the norms about what experiences qualify as positive or negative, to the cultural structures and formats for narrating such experiences (e.g., Hammack, 2008; McLean & Syed, 2015; Syed & Azmita, 2010). Expanding this approach to more diverse groups and cultures will take a great deal of work in terms of ensuring that the prompts are appropriate but could reveal aspects of unique culturally situated meaning-making experiences that are not as easily grasped by surveys and which complement the results of ethnographic work.

This primer has sought to make explicit the most common research practices using narrative methods that have been used during this period of dramatic growth in the field. Without a doubt, the field will continue to evolve. A publication such as this is fundamentally a static document, but the nine authors of this article welcome questions, feedback, and recommendations from scholars who use it, as we collectively seek to continually improve the field.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Note

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