What is naturalistic observation state with examples?

Naturalistic observation

Naturalistic observation is a method that involves observing subjects in their natural environment. The goal is to look at behavior in a natural setting without intervention. This can be applied to tutorial or web-based evaluation if subjects are given a task and asked to go through the process without intervention from the researcher, in order to observe the “natural” way a subject would proceed. Researchers may take notes or tallies of various behaviors they observe. This could also include taking time samplings. For example, in the Mestre (2010) study, time marks were taken to document how long it took students to get to the requested database when they were on the “Online Research Resources Page,” as well as their various unsuccessful attempts. Of value in these types of situations is to understand why students chose the paths they did. This type of observation can then be extended into more robust usability testing that includes debriefing and interviews.

Collecting Audiovisual Records

In naturalistic observation, participants are normally requested to behave in their usual manner, to do whatever they normally do, and to disregard the observer's presence as much as possible. Observational coding can be done in real time, or audiovisual records of behavior can be made using videotape or digital video technology. The presence of an observer-recorder (camera in hand) can be intrusive and may represent a kind of novelty that evokes atypical responses from those observed, a phenomenon termed reactivity. For example, observation may promote socially desirable or appropriate behaviors and suppress socially undesirable or inappropriate behaviors (e.g., adults may display higher than normal rates of positive interactions with children). Nonetheless, reactivity can be and often is successfully alleviated by observers' spending time in the situation with participants before recording to set participants at ease. Observers must be trained to make audiovisual records that have few (if any) breaks or gaps in the behavior stream, and conventions must be developed and adhered to for filming distances and angles that maximize the possibility of continuously and reliably coding behaviors of interest. Decisions must be made about which actor to focus on when multiple actors cannot be captured simultaneously on audiovisual records, and conventions must be developed to provide (future) coders with important off-camera information. Moreover, audiovisual records of naturally occurring behavior in unstructured settings suffer inherent problems and limitations, and the codes that are developed to score them must take these shortcomings into account.

Early Experience and Attachment Security

Ainsworth's naturalistic observations of infants and families in Uganda directed attention to caregiver sensitivity as the main source of individual differences in attachment organization. Caregiver sensitivity has been operationalized in many ways, but research across diverse social and cultural contexts confirms Ainsworth's initial observations that a secure attachment is fostered by the caregiver's accurate perception of, and prompt and appropriate response to, the infant's needs or distress. Sensitive, synchronous interactions, warmth, and support, especially as infants are developing expectations of how their caregiver will respond, predict infant security. Less sensitive and supportive care in infancy predicts insecure attachment. Caregiving that is intrusive, controlling, or hostile is linked to insecure-avoidant attachments, and caregiving that is inconsistently responsive, or unresponsive, is linked to insecure-resistant attachments. Although the way in which insecure infants organize attachment behavior does not facilitate an optimal balance between exploration and proximity, in the context of a less-supportive caregiving environment, their strategies may be adaptations that allow them to best maintain proximity to a caregiver. For example, avoidant infants are thought to defensively hide their distress, ignore the caregiver, and turn their attention to the environment. If the caregiver has been hostile toward infant's bids for comfort in the past, infants may gradually come to understand that they can better maintain proximity to the caregiver by directing attention away from their attachment needs. Similarly, resistant infants may benefit from intensifying attachment behaviors to engage an inconsistently responsive caregiver.

Experimental intervention studies support a causal link between sensitivity and security. Multiple studies indicate that by providing support and training to improve parenting sensitivity, infant attachment security can be enhanced. Importantly, this finding generalizes to dyads in challenging ecological contexts and to infants with more difficult predispositions such as an irritable temperament. Overall, however, relations between sensitivity and security are modest (i.e., sensitivity does not explain all of the variability in attachment security), and additional predictors of security that operate independently of sensitivity, or that moderate the effects of sensitivity, should be explored.

Caregiver sensitivity is less clearly related to attachment disorganization. Rather, disorganized attachment is associated with parenting that induces fear in the infant. Disorganization is more prevalent in samples with abuse or neglect and in samples with high levels of parental depression and unresolved loss. Patterns of affective communication that result from frightened or frightening caregiver behavior (e.g., contradictory emotional cues, withdrawal) also correspond to infant disorganization.

Structured observations

Although naturalistic observations generally yield findings with high ecological validity, oftentimes it may be difficult to observe social behaviors of key interest because they occur at low frequency or out of the range of the observer. To address these challenges, structured or analogue observation can be used, in which circumstances are created that will increase the opportunity to observe the behavior of interest in a more controlled setting. Such work has often been conducted within a research setting. For example, to examine children’s peer group entry strategies, Putallaz (1983) had two experimental confederates who were of the same gender and of about the same age as the participant play a game. The participant was then sent into the room, and the child’s behaviors when attempting to join the ongoing interaction were observed. In a study that focused on children’s responses to ambiguous provocation, Hudley and Graham (1993) set up a game involving two participants who had a chance to win a prize. However, the conditions of the game were such that the children’s ability to win was thwarted. Of interest was how the participants would interpret and respond to the provocation, given that it appeared that the other peer had caused the loss.

Structured observations can be especially useful when assessing adolescents’ peer interactions, given that the majority of adolescents’ everyday social interactions occur in more private, less easily observable settings. The Contextual Assessment of Social Skills (CASS; Ratto, Turner-Brown, Rupp, Mesibov, & Penn, 2011) is a role-play assessment of conversational skills for adolescents and young adults that can be used with typically developing individuals, as well as those with high-functioning autism spectrum disorder (ASD). In the CASS, participants engage in two 3-min role play conversations with two different confederates (unfamiliar peers) who are of the opposite gender. In the first interaction, the confederate shows social interest and engagement. In the second interaction, the confederate displays boredom and disengagement. The conversations are recorded, and participants’ verbal and nonverbal behaviors are coded in 10 categories (e.g., asking questions, topic changes, overall involvement, overall quality of rapport). Asking questions and topic changes are coded as behavioral counts, whereas the other categories are scored by two trained raters on a scale of 1 (low) to 7 (high). Ratto et al. (2011) found that the internal consistency of the CASS was quite high, and interrater reliability was adequate.

Although primarily used in the research context, structured observations also can be used clinically. However, the clinician must be aware of confidentiality concerns and informed consent requirements. Given these challenges, particularly regarding bringing confederates into the clinical setting, it may be easier for the clinician to set up structured interactions outside of the clinic in ways that uphold the client’s confidentiality. For example, a client who has social anxiety might be asked to initiate a conversation with a stranger in a public setting while the clinician observes.

2 Do Cell-Phone Conversations Increase the Crash Risk?

There are several methodologies that have been used to address this question. Each methodology has strengths and weaknesses. Converging evidence from the different techniques provides a definitive answer to the question (“YES”).

The simplest method uses naturalistic observations to see how their driving behavior is altered with the concurrent use of a cell phone to dial, talk, or text. In one such study, we observed over 1700 drivers as they approached a residential intersection with four-way stop signs. We determined through observation whether the drivers were or were not using their cell phone as they approached the intersection and whether they came to a complete stop (as required by law) before proceeding through the intersection.1 The resulting data are presented in Table 1.

Table 1. Frequency Totals for the 2 (Cell Phone in Use Vs. Cell Phone Not in Use) × 2 (Stopping Violation Vs. No Violation) Observational Study of Four-Way Stop Sign Compliance.

Stopping violationNo violationOn cell8228110Not on cell3521286163843413141748

For drivers not using a cell phone, the majority stopped in accordance with traffic laws. By contrast, for the drivers who were observed talking on their cell phone as they approached the intersection, the majority failed to stop in accordance with traffic laws. For drivers not using a cell phone, the odds ratio for failing to stop was 0.27, whereas the odds ratio for failing to stop for drivers who were using their cell phone was 2.93. This 10-fold increase in failing to stop was significant (χ2(1) = 129.8, p < 0.01).

Observational studies have a high validity. After all, it is real driving and if a cell phone is in use, it is a real conversation. But one important limitation of the observational approach is that it cannot establish a causal link between the use of a cell phone and impaired driving. For example, it is possible that those drivers who regularly use a cell phone are willing to engage in more risky activities and that this increase in risk taking also leads drivers to engage in more risky driving behaviors such as running stop signs.

Epidemiological studies provide another method for assessing the crash risk associated with using a cell phone while driving. Redelmeier and Tibshirani (1997) obtained the cell phone records of 699 drivers who were involved in a noninjury motor-vehicle collision. They used a case-crossover design in which the same driver was evaluated to see whether they were using a cell phone at several comparison intervals (e.g., same day of the week). The authors found that the odds of a crash were over four times higher when drivers were using their cell phone. McEvoy et al. (2005) replicated this procedure, but instead used crashes that required the driver to be transported to a hospital for medical care. Similar to Redelmeier and Tibshirani (1997), the odds of crashing were over four times higher when drivers were using their cell phone.

As with observational studies, epidemiological studies have high face validity and establish a real-world association between use of a cell phone and crashes. However, like observational studies, this method does not establish a causal link between cell-phone use and crashes. Note that establishing a causal link between driving impairment and the concurrent use of a cell-phone is important if the research is to advance our theoretical understanding of driver distraction.

The final method that we consider in detail involves the use of high-fidelity driving simulators to establish a causal relationship between the use of a cell phone and driving impairment. Figure 2 shows a participant using our driving simulator. The simulator is composed of five networked microprocessors and three high-resolution displays providing a 180° field of view. It incorporates proprietary vehicle dynamics, traffic scenario, and road surface software to provide realistic scenes and traffic conditions. The dashboard instrumentation, steering wheel, gas, and brake pedal were taken from a Ford Crown Victoria® sedan with an automatic transmission. For the majority of our studies, the simulator used a freeway road database simulating a 24-mile multilane highway with on- and off-ramps, overpasses, and two- and three-lane traffic in each direction.

Our first simulator study used a car-following paradigm to determine how driving performance is altered by conversations over a cell phone. The participant's task was to follow a periodically braking pace car that was driving in the right-hand lane of the highway. When the participant stepped on the brake pedal in response to the braking pace car, the pace car released its brake and accelerated to normal highway speed. If the participant failed to depress the brake, they would eventually collide with the pace car. That is, like real highway stop and go traffic, the participant was required to react in a timely and appropriate manner to vehicles slowing in front of them.

Car following is an important requirement for the safe operation of a motor vehicle. In fact, failures in car following account for ~ 30% of police-reported accidents (e.g., National Highway Transportation Safety Administration, 2001). In our study, the performance of a nondistracted driver was contrasted with the performance of that same driver when they were conversing on either a hand-held or hands-free cell phone. We were particularly interested in examining the differences in driving performance of the hand-held cell-phone driver with that of the hands-free cell-phone driver, because six US States currently prohibit the former while allowing the latter form of cellular communication. To preview, our analyses will show that the performance of drivers engaged in a cell-phone conversation differs significantly from that of the nondistracted driver and that there is no safety advantage for hands-free over hand-held cell phones.

Figure 3 presents a typical sequence of events in the car-following paradigm. Initially, both the participant's car (solid line) and the pace car (long-dashed line) were driving at about 62 MPH with a following distance of 40 m (dotted line). At some point in the sequence, the pace car's brake lights illuminated for 750 ms (short-dashed line) and the pace car began to decelerate at a steady rate. As the pace car decelerated, following distance decreased. At a later point in time, the participant responded to the decelerating pace car by pressing the brake pedal. The time interval between the onset of the pace car's brake lights and the onset of the participant's brake response defines the brake reaction time. Once the participant depressed the brake, the pace car began to accelerate at which point the participant removed his foot from the brake and applied pressure to the gas pedal. Note that in this example, following distance decreased by about 50% during the braking event.

Here, we report three parameters associated with the participant's reaction to the braking pace car. Brake reaction time is the time interval between the onset of the pace car's brake lights and the onset of the participant's braking response (i.e., a 1% depression of the brake pedal). Following distance is the distance between the rear bumper of the pace car and the front bumper of the participant's car. Speed is the average driving speed of the participant's vehicle.

Figure 4 presents the brake reaction time Vincentized cumulative distribution functions (CFFs) as participants reacted to the pace car's brake lights. In Figure 4, the reaction time at each decile of the distribution is plotted, and it is evident that the functions for the hand-held and hands-free cell-phone conditions are displaced to the right, indicating slower reactions, compared to the single-task condition. Analysis indicated that RT in each of the dual-task conditions differed significantly from the single-task condition at each decile of the distribution, whereas the distributions for hand-held and hands-free conditions did not differ significantly across the deciles. A companion analysis of median brake reaction time found that braking reactions were significantly slower in dual-task conditions than in single-task conditions, F(2,78) = 13.0, p < 0.01. Subsidiary pair-wise t-tests indicated that the single-task condition differed significantly from the hand-held and hands-free cell-phone conditions, and the difference between hand-held and hands-free conditions was not significant.

In order to better understand the changes in driving performance with cell-phone use, we examined driver performance profiles in response to the braking pace car. Driving profiles were created by extracting 10 s epochs of driving performance that were time-locked to the onset of the pace car's brake lights. That is, each time that the pace car's brake lights were illuminated, the data for the ensuing 10 s were extracted and entered into a 32 × 300 data matrix (i.e., on the jth occasion that the pace car brake lights were illuminated, data from the 1st, 2nd, 3rd, …, and 300th observations following the onset of the pace car's brake lights were entered into the matrix X[j,1], X[j,2], X[j,3], …, X[j,300]; where j ranges from 1 to 32 reflecting the 32 occasions in which the participant reacted to the braking pace car). Each driving profile was created by averaging across j for each of the 300 time points.

Figure 5 presents the average driving speed profile, time-locked to the onset of the pace car's brake lights, for the three conditions in the study. Over the 10-s epoch, participants in the single-task condition drove at a faster rate of speed than when they were conversing on a cell phone, F(2,78) = 3.3, p < 0.05; however, vehicle speed during the prebraking interval did not differ significantly between conditions. Driving speed reached the nadir between 2 and 3 s after the onset of the pace car's brake lights whereupon the participant's vehicle reaccelerated toward prebraking speed. The difference in overall speed was primarily determined by the time it took participants to recover the speed lost during braking. In particular, the time that it took participants to recover 50% of the speed lost during the braking episode was significantly shorter in the single-task condition than the hand-held or the hands-free cell-phone conditions, F(2,78) = 4.4, p < 0.01. Subsidiary pair-wise t-tests indicated that single-task recovery was significantly faster than either the hand-held or the hands-free cell-phone conditions and that the rate of recovery time did not differ for the two cell-phone conditions. This sluggish behavior appears to be a key characteristic of the driver distracted by a cell-phone conversation, and such a pattern of driving is likely to have an adverse impact on the overall flow of dense highway traffic (see Cooper, Vladisavljevic, Medeiros-Ward, Martin, & Strayer, 2009).

Figure 6 cross-plots driving speed and following distance to illustrate the relationship between these two variables over the braking episode. In the figure, the beginning of the epoch is indicated by a left-pointing arrow, and the relevant symbol (circle, triangle, or square) is plotted every third of a second in the time series. The distance between the symbols provides an indication of how each function changes over time (i.e., on a given function, symbols closer together indicate a slower change over time than symbols farther apart). The figure clearly illustrates that the relationship between driving speed and following distance is virtually identical for the driver distracted by either a hand-held or hands-free cell phone. By contrast, the performance of the participant in single-task conditions provides a qualitatively different pattern than what is seen in the dual-task conditions. In particular, the functions representing the dual-task conditions are displaced toward the lower right quadrant, indicative of a driver operating the vehicle more conservatively (i.e., somewhat slower and with a greater following distance from the pace car) than in single-task conditions.

Figure 6 also illustrates the dynamic stability of driving performance following a braking episode. From a dynamic systems perspective, driving performance in single- and dual-task conditions can be characterized as operating in different speed-following distance basins of attraction with performance returning to equilibrium following each braking perturbation. Note also that the curves in Figure 6 for the nondistracted driver and the driver conversing on a cell phone did not intersect. This suggests that the basin of attraction created with either the hand-held or hands-free cell-phone conversations was sufficiently “deep” that participants returned to their respective prebraking set points after a braking episode had perturbed their position in the speed/following-distance space.

Taken together, the data demonstrate that conversing on a cell phone impaired driving performance and that the distracting effects of cell-phone conversations were equivalent for hand-held and hands-free devices. Compared to single-task conditions, cell-phone drivers' brake reaction times were slower and they took longer to recover the speed that was lost following braking. The cross-plot of speed and following distance showed that drivers conversing on a cell phone tended to have a more cautious driving profile, which may be indicative of a compensatory strategy to counteract the delayed brake reaction time. Elsewhere, Brown, Lee, & McGehee (2001) found that the sluggish brake reactions, such as the ones described herein, can increase the likelihood and severity of motor-vehicle collisions.

Another way to evaluate these risks is by comparison with other activities commonly engaged in while driving (e.g., listening to the radio, talking to a passenger in the car, etc.). The benchmark that we used in our second study was driving while intoxicated from ethanol at the legal limit (0.08 wt/vol). We selected this benchmark because the epidemiological study by Redelmeier and Tibshirani (1997) noted that “the relative risk [of being in a traffic accident while using a cell phone] is similar to the hazard associated with driving with a blood alcohol level at the legal limit” (p. 465).

If this claim can be substantiated in a controlled laboratory experiment, then these data would be of immense importance for public safety. In particular, the World Health Organization recommended that the behavioral effects of an activity should be compared to alcohol under the assumption that performance should be no worse than when operating a motor vehicle at the legal limit (Willette & Walsh, 1983). How does conversing on a cell phone compare with the drunk-driving benchmark?

Here, we directly compared the performance of 40 drivers who were conversing on a cell phone with the performance of these same drivers who were legally intoxicated with ethanol. Three counterbalanced conditions were studied: single-task driving (baseline condition), driving while conversing on a cell phone (cell-phone condition), and driving with a blood alcohol concentration of 0.08 wt/vol (alcohol condition, verified using an Intoxilyzer 5000).

Table 2 presents nine performance variables that were measured to determine how participants reacted to the vehicle braking in front of them. Three of the variables (brake reaction time, speed, and following distance) were used in our first study. We also added several new variables to provide a more fine-grained comparison between drunk driving and cell-phone conditions.2Braking force is the maximum force that the participant applied to the brake pedal in response to the braking pace car. SD following distance is the standard deviation of following distance. Time to collision (TTC), measured at the onset of the participant's braking response, is the time that remains until a collision between the participant's vehicle and the pace car if the course and speed were maintained (i.e., had the participant failed to brake). Also reported is the frequency of trials with TTC values below 4 s, a level found to discriminate between cases where the drivers find themselves in dangerous situations from cases where the driver remains in control of the vehicle (e.g., Hirst & Graham, 1997). Half-recovery time is the time for participants to recover 50% of the speed that was lost during braking. Also shown in the table is the total number of collisions in each phase of the study. We used a multivariate analysis of variance (MANOVA) followed by planned contrasts to provide an overall assessment of driver performance in each of the experimental conditions.

Table 2. Driving Performance Measures Obtained in the Alcohol, Baseline, and Cell-Phone Driving Conditions.

AlcoholBaselineCell phoneTotal accidents003Brake reaction time (ms)779 (33)777 (33)849 (36)Speed (MPH)52.8 (2.0)55.5 (0.7)53.8 (1.3)Following distance (m)26.0 (1.7)27.4 (1.3)28.4 (1.7)Maximum braking force percentage of max69.8 (3.7)56.7 (2.6)55.5 (3.0)SD following distance (m)10.3 (0.6)9.5 (0.5)11.8 (0.8)Time to collision (s)8.0 (0.4)8.5 (0.3)8.1 (0.4)Time to collision < 4 s3.0 (0.7)1.5 (0.3)1.9 (0.5)Half-recovery time (s)5.4 (0.3)5.3 (0.3)6.3 (0.4)

MANOVAs indicated that both cell phone and alcohol conditions differed significantly from single-task baseline (F(8,32) = 6.26, p < 0.01 and F(8,32) = 2.73, p < 0.05, respectively). When drivers were conversing on a cell phone, they were involved in more rear-end collisions, their initial reaction to vehicles braking in front of them was slowed, and the variability in following distance increased. In addition, compared to the single-task baseline, it took participants who were talking on a cell phone longer to recover the speed that was lost during braking.

By contrast, when participants were intoxicated, neither accident rates nor reaction time to vehicles braking in front of the participant nor recovery of lost speed following braking differed significantly from single-task baseline. Overall, drivers in the alcohol condition exhibited a more aggressive driving style. They followed closer to the pace vehicle and braked with more force than in the single-task baseline condition. Unexpectedly, our study found that accident rates in the alcohol condition did not differ from baseline; however, the increase in hard braking is predictive of increased accident rates over the long run (e.g., Brown et al., 2001; Hirst & Graham, 1997).

The MANOVA also indicated that the cell-phone and alcohol conditions differed significantly from each other, F(8,32) = 4.06, p < 0.01. When drivers were conversing on a cell phone, they were involved in more rear-end collisions and took longer to recover the speed that they had lost during braking than when they were intoxicated. Drivers in the alcohol condition also applied greater braking pressure than drivers in the cell-phone condition.

Finally, the accident data indicated that there were significantly more accidents when participants were conversing on a cell phone than in the single-task baseline or alcohol conditions. χ2(2) = 6.15, p < 0.05.

Taken together, we found that both intoxicated drivers and cell-phone drivers performed differently from the single-task baseline and that the driving profiles of these two conditions differed. Drivers using a cell phone exhibited a delay in their response to events in the driving scenario and were more likely to be involved in a traffic accident. Drivers in the alcohol condition exhibited a more aggressive driving style, following closer to the vehicle immediately in front of them, necessitating braking with greater force. With respect to traffic safety, the data suggest that when controlling for driving conditions and time on task, the impairments associated with cell-phone drivers may be as great as those commonly observed with intoxicated drivers.

Discussion

Using enhanced audio recordings and coding of naturalistic observations, our study provided a comprehensive description of the home media environment of preschoolers, across multiple media formats (eg, TV, computer, tablet, video games, and other mobile devices). In addition to describing the quantity and quality of children’s media exposure, we also examined the nature of conversations between mothers and their preschool-aged children surrounding electronic media exposure and explored whether maternal interaction with children while using electronic media differed by demographic factors.

Our findings indicate that preschool-aged children are exposed to a variety of media but that TV is still the most commonly used medium in this sample. Nonetheless, our study highlights the need to assess the frequency of multiple types of media in the home, given the rapid growth of mobile devices. We also found differences in the amount and content of media exposure, based on mothers’ level of education. Even in our (relatively speaking) highly educated sample, we found differences in the amount of electronic media exposure. For example, children of mothers with graduate degrees were exposed to a greater proportion of educational programming. Children of mothers with graduate degrees had less electronic media exposure, compared with children of mothers with high school degrees and/or some college courses. Given these demographic differences in the quantity and quality of media exposure in this sample of mothers with high school degrees or higher, future research should examine such differences in a sample that includes mothers without a high school diploma or with a high school diploma/GED only. This is especially important because prior literature suggests that children from lower socioeconomic status (SES) families have higher electronic media exposure and may be more susceptible to the negative correlates of excessive media use (Anand & Krosnick, 2005; Christakis, Ebel, Rivara, & Zimmerman, 2004).

In addition to reliably coding the presence of different types of media formats and programs, we also were able to transcribe a broad array of mother–child interactions about media over many hours of audio recordings. A noted limitation in the literature on parent–child communication related to media content is that the vast majority of the studies use parents’ report of communication during children’s electronic media exposure. Although we did not measure parents’ report of their communication styles, we were able to classify mother–child interactions into categories reflecting coviewing dialogue and communication with children about media content. We found that children of mothers with less than a graduate degree were exposed to media without any dialogue related to the content of media or coviewing dialogue for the vast majority of the time. Indeed, Christakis et al. (2009) found that with each additional hour of TV exposure, there was a decrease of 770 adult words the child heard during the recording session. Our study adds to those findings by demonstrating that not only is there less talk in general, but in the context of media exposure, there is little communication between parents and children related to media content. It is not yet known how prevalent parent–child interaction around media is in families with lower parental education levels or in families with lower SES. Given the small sample size, future research should implement this coding system in a larger, more diverse sample.

2 Clinical relevance

Scientists have been making large-scale, naturalistic observations of children's behavior since technology permitted the data collection and management, but only recently have the advances from big data methods gained popularity in clinical research or practice. We see enormous promise for large-scale data methods, and long-form recordings in particular, in clinical settings to reliably measure speech-language development and accurately diagnose developmental delays. The use of new, semiautomated tools in the clinic could be timely given the current shortage of speech-language pathologists in many countries, as well as the toll that communication disorders take on life quality and opportunities. For the former, Wylie, McAllister, Davidson, and Marshall (2013) estimated just one speech-language pathologist per 2500–4700 people in the United States, United Kingdom, Canada, and Australiaa —an alarming statistic given that approximately 5% of children in the United States have a “noticeable” speech-language disorder or delay by age 6 (NIDCD, 2016). The situation is dire in other geographical regions, with Wylie et al. estimating one speech-language pathologist per 2–4 million people in sub-Saharan Africa. Clearly, tools that can facilitate clinicians’ work are welcome.

Clinicians, such as speech-language pathologists and pediatricians, are responsible for measuring children's speech-language development over time and diagnosing speech-language disorders and delays. But these responsibilities are complicated by (1) participant age (it can be difficult, if not impossible, to elicit controlled speech samples from young children and infants), (2) a paucity of data (for delays characterized by limited communication), and (3) a lack of assessment standardization and reliability, including for situations where clinicians are forced to improvise due to child fussiness or discomfort. In the following sections, we outline how long-form recordings can help address these issues.

Qualitative Analysis

A more comprehensive analysis of safety culture should include qualitative methods such as field observations, artifact analysis, interviews, focus group discussions, and dialog with key individuals in the organization (e.g., Maxwell 2004).

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