Which of the following people identified three basic types or clusters of temperament easy difficult and slow

Dev Psychol. Author manuscript; available in PMC 2018 Oct 1.

Published in final edited form as:

PMCID: PMC5612890

NIHMSID: NIHMS892383

Maria A. Gartstein, Amanda Prokasky, Martha Ann Bell, Susan Calkins, David J. Bridgett, Julia Braungart-Rieker, Esther Leerkes, Carol L. Cheatham, Rina D. Eiden, Krystal D. Mize, Nancy Aaron Jones, and Erich Seamon

Abstract

There is renewed interest in person-centered approaches to understanding the structure of temperament. However, questions concerning temperament types are not frequently framed in a developmental context, especially during infancy. In addition, the most common person-centered techniques, Cluster Analysis [CA] and Latent Profile Analysis [LPA], have not been compared with respect to derived temperament types. To address these gaps, we set out to identify temperament types for younger and older infants, comparing LPA and CA techniques. Multiple data sets [N = 1,356; 672 girls, 677 boys] with maternal ratings of infant temperament obtained using the Infant Behavior Questionnaire-Revised [Gartstein & Rothbart, 2003] were combined. All infants were between 3 and 12 months of age [mean = 7.85; SD = 3.00]. Due to rapid development in the first year of life, LPA and CA were performed separately for younger [n = 731; 3-to-8 months of age] and older [n = 625; 9-to-12 months of age] infants. Results supported 3-profile/cluster solutions as optimal for younger infants, and 5-profile/cluster solutions for the older subsample, indicating considerable differences between early/mid and late infancy. LPA and CA solutions produced relatively comparable types for younger and older infants. Results are discussed in the context of developmental changes unique to the end of the first year of life, which likely account for the present findings.

Keywords: infant temperament, latent class analysis, cluster analysis

According to Rothbart’s psychobiological model, temperament represents constitutionally based individual differences in emotional, motor, and attentional reactivity, and in self-regulation, demonstrating consistency across situations and relative stability over time [Rothbart & Bates, 2006; Rothbart & Derryberry, 1981]. The term “constitutional” emphasizes the connection between temperament and biology, including the link to underlying neurobehavioral systems, as well as genetic and epigenetic influences. Reactivity encompasses multiple domains of affectivity, with self-regulation, largely dependent on attentional functioning, serving to modulate reactive tendencies [Gartstein, Putnam, Aaron, & Rothbart, 2016]. In the first year of life [especially early-to-mid infancy], orienting attention plays a critical role, as executive functions supported by the frontal lobe maturation have not yet “come online” [Posner, Rothbart, Sheese, & Voelker, 2012]. Along with more advanced attentional skills and capacity for regulation, significant increases in fear/behavioral inhibition were noted at the end of the first year of life, as for example, infants became slower, rather than faster, in reaching toward high-intensity toys [Rothbart, 1988]. These increases in fearfulness have been demonstrated with respect to mean levels and individual trajectories, indicating considerable changes later in infancy [Gartstein & Rothbart, 2003; Gartstein, Hancock, & Iverson, in press].

Temperament domains outlined on the basis of the psychobiological model have been examined primarily through a variable-centered/dimensional approach, wherein scales are combined into overarching factors. At the same time, fine-grained temperament dimensions are important in their own right, demonstrating unique predictive relationships with outcomes such as developmental psychopathology, sleep and eating/feeding problems [e.g., Gartstein, Potapova, & Hsu, 2014]. For example, low levels of falling reactivity and soothability in infancy were associated with an increased risk for oppositional defiant disorder and callous-unemotional traits [Willoughby, Wasschbusch, Moore, & Propper, 2011]. Other investigators reported that fear and sadness made more substantial contributions to internalizing difficulties, whereas anger/frustration were related to both internalizing and externalizing problems at different ages [Lengua, 2006; Oldenhinkel, Hartman, de Winter, Veenstra & Ormel, 2004; Nigg, 2006]. Although regulation and negative affect have received the most attention, positive affectivity distinctions also are important. For example, higher levels of Low Intensity Pleasure [enjoyment of calm activities] may protect against internalizing and externalizing problems, whereas more High Intensity Pleasure [enjoyment of more stimulating activities] appears to convey risk for externalizing difficulties only [Gartstein, Putnam, & Rothbart, 2012].

The fine-grained focus of the present study is thus a function of important distinctions among more narrowly defined attributes, often combined for convenience [e.g., reducing the number of analyses] or due to sample size limitations, rather than theoretical reasons. In the context of person-centered techniques, fine-grained temperament attributes can be expected to result in more differentiated typologies, likely increasing effectiveness of classification. Refining classification is of interest in part because it could enhance targeting for temperament-based prevention efforts, identifying children who face high versus low levels of risk as a result of their temperament profiles. However, person-centered approaches have not been widely used to distinguish types based on fine-grained temperament attributes, particularly during infancy. Thus, the primary goal of this study was to identify typologies of infant temperament, at the same time comparing the two most widely used person-centered techniques, cluster analysis [CA] and latent profile analysis [LPA].

Person-Centered Approaches in Temperament: Cluster and Latent Profile Analyses

The study of children’s temperament has a longstanding tradition of relying on typologies. Notably, Thomas and Chess [1977] identified three infant temperament types: difficult, easy, and slow-to-warm up, relying on parental perceptions of nine underlying fine-grained temperament dimensions. These temperament types have the inherent appeal of answering the question: “What kind of kid is she”? Yet the efforts to understand children’s temperament within the psychobiological framework have relied primarily upon the variable-centered perspective; person-centered approaches, by comparison, have received relatively little attention [Zentner & Bates, 2008]. A holistic interactionist perspective, wherein an individual is viewed as the unit of analysis, represents the conceptual foundation for person-centered approaches, with all variables considered simultaneously [von Eye & Bergman, 2003]. Applying a person-centered perspective to child temperament in a quantitative manner requires that combinations of multiple temperament dimensions be considered. Typologies based on these combinations can be compared as to their ability to explain the observed pattern of results, differentiating between individuals.

Cluster Analysis [CA] represents the most frequently used person-centered technique for the identification of temperament types. CA is a data-driven approach, which begins by randomly assigning cases to a specified number of clusters, and subsequently reassigning cases to minimize the distance to the cluster center [Mooi & Sarstedt, 2011]. CA has produced mixed results with respect to the number of temperament types. Caspi & Silva [1995] identified five temperament types: Undercontrolled, Inhibited, Confident, Reserved, and Well-adjusted, using CA with investigator behavior ratings provided for a sample of 1,037 3-year-old children. Sanson et al. [2009], on the other hand, derived four temperament types based on maternal ratings on the Child Temperament Questionnaire [CTQ; Thomas & Chess, 1977] in a sample of 1,662 3 to 4-year-olds: Nonreactive/Outgoing, High Attention Regulation, Poor Attention Regulation, and Reactive/Inhibited. Also using CTQ maternal reports, Martin, Bridger and Huttunen [2000] identified seven clusters in a 5-year old sample of 1,000: Inhibited, Impulsive, Highly Emotional, Typical, Reticent, Uninhibited, and Passive. Recently, Prokasky et al. [2017] concluded that six temperament types were optimal using maternal report on the Children’s Behavior Questionnaire [CBQ; Rothbart, Ahadi, Hershey & Fisher, 2001]: Unregulated, Bold, High Reactive, Average, Well Adjusted, and Regulated, replicating these groups with independent samples. In the only investigation employing CA to derive temperament types from infancy to middle childhood, Komsi et al. [2006] identified three clusters: Overcontrolled, Undercontrolled, and Resilient. These typologies were based on two broadly defined temperament dimensions: positive and negative affectivity, and the fine-grained attribute of activity level.

Variations of Latent Class Analysis [LCA] were utilized to identify children’s temperament typologies during toddlerhood and middle childhood. Relative to CA, latent class analysis [LCA] is a newer, model-based, person-centered approach that has started to gain use in identification of temperament types. LCA determines the optimal number of latent subsets of children who share similar patterns of temperament attributes based on scale scores. Using a variation of LCA for continuous variables, Latent Profile Analysis [LPA], van den Akker, Dekovic, Prinzie and Asscher [2010] identified three profiles [Typical, Fearful, and Expressive] based on maternal ratings on the Toddler Behavior Assessment Questionnaire [TBAQ; Goldsmith et al., 1996]. In a sample of 787 twin pairs [mean age = 7.4 years], Scott et al. [2016] employed twin factor mixture modeling [LCA which allows simultaneous modeling of profile and factor structure] and identified 4 temperament profiles [Regulated/Typical Reactive, Well-Regulated/Positive Reactive, Regulated/Surgent, and Dysregulated/Negative Reactive], with mother/father rating compo sites obtained via a modified CBQ.

In studies with infants, LCA has been applied to laboratory observations of reactivity at four months of age [N = 169; Loken, 2004]. Results supported at least three temperament classes, corresponding to high reactive [high distress/activity, low smiling], low reactive [low distress/activity, high smiling], and a category characterized as “aroused” [low distress/high activity]. More recently, Beekman et al. [2015] utilized LPA to identify temperament profiles when children were 9, 18, and 27 months of age [N = 561]. Typical/Low Expressive, Typical/Expressive, Negative Reactive, and Positive Reactive profiles were identified at 9 months. Positive Reactive, Negative Reactive, Active Reactive [marked by high levels of activity and above average levels of both pleasure and anger], and Fearful profiles emerged at 18 and 27 months of age.

Although these person-centered findings may seem disparate at first, a number of themes emerge across existing studies. First, there is a consistent grouping marked by reactivity/negative affect, also reminiscent of “difficult temperament” [Beekman et al., 2015; Loken, 2004; Martin, et al., 2000; Prokasky et al., 2017; Sanson et al., 2009; Scott et al., 2016]. Another theme has to do with children being well regulated and/or presenting with high levels of positive affectivity, sometimes combined under labels referring to adjustment or resilience [Beekman et al., 2015; Caspi & Silva, 1995; Komsi et al., 2006; Prokasky et al., 2017; Scott et al., 2016]. In addition, several typologies included fear-based groups [Beekman et al., 2015; Martin et al., 2000; Sanson et al., 2009; van den Akker, et al., 2010] and those defined by fearlessness [Martin, et al., 2000] or under-control [Caspi & Silva, 1995; Komsi et al., 2006].

Existing research has pointed to a number of themes, yet unanswered questions remain, in part due to the relatively limited scope of infant temperament attributes considered to date. Beekman et al. [2015] and Komsi et al. [2006] assessed infant temperament using the Infant Behavior Questionnaire [IBQ; Rothbart, 1981], the predecessor of the IBQ-R, which includes only two regulation-related scales and one positive emotionality dimension. Loken [2004] just considered distress, smiling, and activity level aspects of infant temperament, measured using laboratory observations. The age range of infants in existing studies is restricted as well, as Loken [2004] collected temperament data at 4, Komsi et al. [2006] at 6 months, and Beekman et al. [2015] evaluated infants at 9 months of age. As a result, these studies are not able to inform about potential shifts in typologies that stem from changes in temperament at the end of the first year. Temperament typologies are likely not impervious to rapid developmental transitions, such as those evident between early/mid and late infancy. This developmental period is defined by marked locomotor advances [Shonkoff & Phillips, 2000], the emergence of different domains of reactivity, such as anger/frustration earlier in infancy [Carranza, Perez-Lopez, Gonzalez, & Martinez-Fuentes, 2000; Rothbart & Bates, 2006], and notable increases in fear at the end of the first year of life [Gartstein et al., in press]. Later infancy, relative to early/mid infancy, is also marked by the “coming online” of more advanced attentional capabilities [Bridgett, Burt, Edwards & Deater-Deckard, 2015; Posner, et al., 2012], linked with improved regulation. Thus, while existing studies employing CA and LCA are informative, additional work sensitive to developmental shifts in temperament at the end of the first year of life is needed.

Finally, no study to date has directly compared CA and LCA in the same sample of children to determine if these approaches result in comparable temperament typologies. Only two studies comparing CA and LCA solutions have been conducted. Eshghi, Hauton, Legrand, Skaletsky and Woolford [2011] examined groupings of countries [N = 160] formed on the basis on 10 socio-demographic variables [per capita income, education, percent urban population, etc.]. CA was found superior in terms of within-group homogeneity [i.e., producing types with the most similar members]. DeStefano and Kamphaus [2006] compared CA and LCA deriving child behavioral adjustment types for 6 to 11-year-olds using teacher ratings. CA results supported seven, and LCA three, adjustment categories. Thus, questions concerning differences among these analytic techniques require consideration in deriving temperament types.

The Current Study

Given the relative dearth of research addressing temperament types in a fine-grained manner across infancy, the primary goal of this study was to identify infant temperament types based on the 14 IBQ-R scale scores. Developmental considerations, including the overall rapid rate of growth in infancy [Shonkoff & Phillips, 2000] and noted changes in temperament [e.g., Gartstein et al., 2010], dictated that typologies be derived for younger and older infants separately. As CA has been most widely utilized to investigate temperament types, and LCA represents a less established approach, an additional aim of this study was to compare LCA and CA temperament types.

The literature is not consistent with respect to the number of clusters/classes; however, 3 or 4 have been typically reported for infants [Beekman, et al., 2015; Loken, 2004; Komsi, et al., 2006]. On the basis of these findings, we tentatively anticipated identifying 3 to 4 profiles/clusters. Nevertheless, in light of the limited existing infant studies, more differentiated profiles/clusters identified among older children [e.g., Caspi & Silva, 1995; Prokasky, 2017], and our consideration of 14 IBQ-R scales, up to 8 profiles/clusters were evaluated. We also hypothesized differences among solutions derived for younger and older infants, because of considerable changes between early/mid infancy and the end of the first year. Moreover, solutions for older infants were expected to be more complex in nature, given that in prior work 3 classes were identified for 4-month-olds [Loken, 2004], with a 4-profile solution deemed optimal for older infants [Beekman et al., 2015]. In regards to the specific nature of hypothesized profiles/clusters, most relevant studies with infants [i.e., Beekman et al. 2015; Loken, 2004] suggest that High Reactive [high distress, activity level, low smiling], Low Reactive [demonstrating an opposite pattern], and a Aroused [low distress/high activity] types could be expected earlier in the first year. For older infants, types consistent with Typical/Low Expressive, Typical/Expressive, Negative Reactive, Positive Reactive, Active Reactive, and/or Fearful [Beekman et al., 2015] were expected.

Finally, with respect to the optimal solutions, we generally anticipated consistency across the two person-centered approaches. Nevertheless, limited evidence based on direct comparisons of CA and LCA techniques in non-temperament contexts suggests the possibility of some differences among types derived by these approaches, although sufficient specificity for a-priori hypotheses is currently lacking. Thus, probabilities of participants’ assignment to parallel profiles/clusters were compared via a chi-square test, and within-group homogeneity differences were considered in direct comparisons of LPA and CA solutions.

Method

Sample

Data sets were acquired by emailing researchers who had requested the IBQ-R or published research using the instrument between 2006 and 2011 [See Table 1 for additional demographic information]. Only families with healthy infants were eligible to participate in the projects [samples of origin] providing IBQ-R data.

1. The first infant temperament data set [n = 410] was provided by the 3d and 4th authors. These data were collected in the context of a longitudinal study examining individual differences in cognition-emotion integration [Gartstein, Bell & Calkins, 2014].

2. The second data set [n = 158], provided by the 5th author, included information collected when infants were 6 [n = 114], 8 [n = 95], 10 [n = 87], and 12 months of age [n = 79]. This study addressed temperament development, parenting, and emerging behavior problems [Bridget et al., 2009].

3. The third data set was contributed by the 6th author and included temperament ratings at 3 [n = 135], 5 [n = 127], 7 [n = 116], and 12 [n = 116] months of age, as described by Braungart-Rieker et al. [2014]. This work focused on temperament, parent-child interactions, and attachment.

4. The fourth data set [n = 118] was provided by the 7th author, with the IBQ-R collected at 6 months for a study of temperament and mother-infant interactions [Parade & Leerkes, 2008].

5. The fifth data set [n = 86] was contributed by the 8th author, who obtained temperament ratings at 6 months for a study addressing nutrition and cognitive development [Cheatham & Sheppard, 2015].

6. The sixth data set, containing IBQ-R assessments when children were 9 months of age, was collected by the 9th author for a study examining the effects of prenatal tobacco exposure [see Eiden et al., 2015 for full sample description] on infant functioning. Only control group infants [n = 75] not exposed to tobacco in utero were included in the current study.

7. The seventh data set was contributed by the 10th and 11th authors, who obtained infant temperament ratings [n = 85] when children were 7 to 12-months of age as part of ongoing research on the loss of maternal attention to a social-rival [Mize & Jones, 2012; Mize, Pineda, Blau, Marsh, & Jones, 2014].

8. The eighth data set, provided by the 12th author, included monthly longitudinal data on 30 three-month-old infants collected through six-months, and again at 12-months of age [Mireault et al., 2012]. This research examined infant humor perception.

9. The final three samples, contributed by the first author, were recruited for several studies addressing temperament development. The first sample of 147 children was assessed at 4, 6, 8 [n = 114], 10 [n = 102] and 12 [n = 101] months of age, with portions of this dataset described in Gartstein et al. [2010] and Gartstein et al. [2013]. The second sample [N = 140] was equally divided across four age groups: 3- months [n = 35]; 6- months [n = 35]; 9- months [n = 35]; and 12- months [n = 35; Gartstein & Bateman, 2008]. The third sample [n = 9] participated in a parental guidance temperament intervention, wherein caregivers were provided with information based on the psychobiological model [Iverson et al., 2014].

Table 1

Demographic Metrics as a Function of Sample.

These data sets obtained by multiple laboratories were collectively utilized in the present study [N = 1,356]. All infants were between 3- and 12- months of age [mean = 7.85; SD = 3.00], and were equally distributed across sex [females: n = 672; males: n = 677]. A number of studies relied on longitudinal evaluations. In these instances, in order to maintain independence of observations, only one assessment point per child was included in the combined data set. About an equal number of cases were selected from each of the different phases of the longitudinal studies. For example, for the first dataset [n = 410], 205 infants contributed 5-month temperament scores, whereas the remainder [n = 205] of the sample contributed 10-month data. To use all of the available data, if a participant completed only a portion of the longitudinal assessments, their data were selected from a completed assessment [i.e., not from one of the missing evaluations].

Measures

Infant Behavior Questionnaire-Revised [IBQ-R; Gartstein & Rothbart, 2003]

The IBQ-R is a parent-report measure of infant temperament for use between 3 and 12 months of age. The 191 items [rated on a 7-point Likert-type scale] represent 14 subscales, which in turn form three over-arching factors. The Surgency factor consists of Approach [app], Vocal Reactivity [vr], High Intensity Pleasure [hp], Smiling and Laughter [sl], Activity Level [act], and Perceptual Sensitivity [ps] subscales. The Negative Emotionality factor consists of Sadness [sad], Distress to Limitations [dl], Fear, and Falling Reactivity [fall] subscales. Finally, the Regulatory Capacity/Orienting factor includes Low Intensity Pleasure [lp], Cuddliness/Affiliation [cud], Duration of Orienting [do], and Soothability [sooth] subscales. Each item reflects the frequency of occurrence of reactivity/regulation during the prior week [most items], or 2 weeks, for less common events. The IBQ-R has consistently demonstrated good psychometric properties with mothers, fathers, and international samples, with Cronbach’s alphas ranging from .77 to .96 [Gartstein & Rothbart, 2003; Parade & Leerkes, 2008]. Inter-rater reliability, concurrent/predictive and construct validity, have been demonstrated for IBQ-R scales [Gartstein & Bateman, 2008; Gartstein, Knyazev, & Slobodskaya, 2005; Gartstein & Marmion, 2008; Parade & Leerkes, 2008].

Analytic Strategy

Latent Profile Analysis

LPA was accomplished using Mplus Version 7 [Muthén & Muthén, 2012], with full information maximum likelihood estimation employed to accommodate missing data [Enders, 2013]. LPA provides indices to discern the optimal number of subsets of infants who share similar patterns of maternal ratings concerning fine-grained temperament attributes. As recommended, a number of indices were taken into account simultaneously in making decisions about the optimal number of profiles [Lanza & Cooper, 2016]. We considered the Akaike Information Criteria [AIC], Sample-Size Adjusted Bayesian Information Criteria [BIC], and Entropy measures in comparing models, attempting to minimize the AIC and BIC, and producing a strong entropy measure [approaching 1.00]. The Entropy index reflects effectiveness of categorization based on posterior probabilities, which were also examined in this study. The Lo, Mendell, Rubin Likelihood Ratio Test [Lo, Mendell, & Rubin, 2001] was considered in determining if an additional profile improved the overall model fit [e.g., comparing 2-profile to a 3-profile model]. Infant age and sex, and sample of origin, were considered as covariates and retained in the final models if they were associated with significant paths to the latent variable reflecting profile membership. Multiple solutions [up to 8 profiles] were considered.

Cluster Analysis

In line with previous studies relying on clustering techniques in discerning temperament types [e.g. Caspi & Silva, 1995; Sanson et al., 2009], a two-step clustering procedure was employed. If the number of underlying clusters within the data is unknown – a circumstance encountered in the current investigation, a hierarchical cluster analysis is typically performed as an initial step. Therefore, an exploratory hierarchical cluster analysis was initially conducted on a random sample of 200 cases. This preliminary analysis is performed to help guide decisions about the number of clusters within a dataset, and it is also recommended to examine multiple cluster solutions with the entire sample [Hair, Anderson, Tatham, & Black, 1995; Mooi & Sarstedt, 2011]. For this reason, and to parallel the LPA procedures, a series of k-means cluster analyses [2 through 8 cluster solutions] were performed using the entire sample.

Comparison of LPA and CA

Optimal LPA and CA solutions were plotted to enable interpretation and comparison across these techniques. ANOVAs with IBQ-R scales as dependent variables were conducted to compare types resulting from the optimal solutions, with profiles and clusters compared in turn, to further characterize and contrast these approaches. Follow-up pairwise tests – independent-group t-tests with Bonferroni corrections – were subsequently performed and used to inform decisions regarding profile labels. Specifically, attributes associated with statistically significant differences across all profiles/clusters, and differentiating types as highest/lowest relative to other groups, were prioritized in naming profiles. In addition, we compared LPA and CA classification outcomes in terms of: [1] agreement between LPA and CA with respect to participant assignment to parallel profiles/clusters; and [2] within-group homogeneity differences. To do so, chi-square tests were conducted to discern agreement between LPA and CA solutions in terms of case assignment. Average Euclidean distances for LPA and CA solutions were also computed and compared via matched-pair t-tests to determine which method resulted in greater homogeneity [Eshghi et al., 2011].

Accounting for development

As infant development was expected to play a role in differentiation between temperament types, the sample was divided by age, with parallel LPA and CA analyses conducted separately for younger and older infants. Specifically, the median split [8 months] was used to divide the sample into groups of younger [n = 731; 3-to-8 months of age] and older infants [n = 625; 9-to-12 months of age].

Results

Descriptive statistics and Chronbach’s α for IBQ-R scales were computed using the entire sample, and separately for younger and older infants, using SPSS Version 23 [Table 2].

Table 2

IBQ-R Scale Means, Standard Deviations and Chronbach’s Alphas for the Entire Sample [N = 1,356], Younger [n = 731] and Older [n = 625] Infants.

Latent Profile Analysis [LPA]

All LPA models were initially evaluated with the sample of origin, and infant age and sex as covariates. Infant sex was not retained, with results indicating that a 3-profile model was optimal for younger infants [Figures 1a]. According to the Lo, Mendell, Rubin Likelihood Ratio Test, an additional profile improved the overall model fit from a 2-profile to a 3-profile model, but not from a 3-profile to 4-profile solution [Table 3]. Although BIC and AIC were lowered with additional profiles, the Entropy and posterior probabilities were optimized in the 3-profile model. Although more complex solutions [up to 8 profiles] were examined, these did not result in improved fit1. All path coefficients from infant age to the latent profile variables were significant for the younger age group.

a and b. 3-profile solution for younger and 5-profile solution for older infants.

Table 3

Latent Profile Analysis: Assessing model fit for older and younger infants

For older infants, the 5-profile solution was deemed optimal based on a number of indicators [Figure 1b]. Specifically, the 5-profile solution resulted in the minimum BIC and AIC values, while also maximizing Entropy. None of the 5 profiles was associated with an n < 10% of the total N, further supporting this model as superior overall, despite a non-significant Lo, Men-dell, and Rubin Likelihood Ratio Test. In contrast to our findings with the younger age group, path coefficients from infant age to the latent profiles were not significant for the older infants.

Consistent with prior investigations [e.g., Beeckman et al., 2015], interpretation of types was guided by visual inspection, in conjunction with statistical tests. Resulting types were compared via ANOVAs, for younger and older age groups, respectively. All IBQ-R dimensions reliably differentiated between the 3 temperament types derived on the basis of LPA for younger infants [mean η2 = .23; range .01–.52]. Cuddliness [η2 = .41] and Vocal Reactivity [η2 = .42] were associated with the largest effects sizes. In the older age group, all comparisons indicated significant profile differences [mean η2 = .28; range .07–.41]. Vocal Reactivity [η2 = .41] and Approach [η2 = .39] produced the largest effects.

Follow-up t-tests with Bonferroni corrections [see Supplementary Tables] and Figure 1a indicated that in the younger subsample the lowest scores on all but one of the positive affectivity scales marked Profile 1. That is, Profile 1 was significantly different from Profiles 2 and 3 on Smiling and Laughter, Activity Level, High Intensity Pleasure, Perceptual Sensitivity and Vocal Reactivity, with Approach scores significantly different from Profile 3 only. Significantly lower levels of Fear, Duration of Orienting, and Low Intensity Pleasure were also observed for Profile 1, thus labeled Fearless/Low Positive/Low Orienting. According to the follow-up tests and Figure 1a, Profile 2 was characterized by a pattern of average positive affectivity, with Approach significantly different from Profile 3 only. This type was also associated with the highest levels of Distress to Limitations, as well as the lowest Falling Reactivity, Soothability, and Cuddliness. Given the salience of frustration, coupled with a lack of responsiveness to caregivers’ attempts to calm, and an inability to lower one’s own level of arousal, Profile 2 was labeled Frustrated/Difficult to Calm. Profile 3 was described as High Positive/Regulated, as infants in this group received significantly higher scores for positive affectivity and all but one scale related to regulation [Cuddliness was significantly different from Profile 2 ratings only], coupled with average or low negative emotionality dimension ratings.

In the older age group, Profile 1 was best described as Low Positive, because of significantly lower levels of Smiling and Laughter, High and Low Intensity Pleasure, as well as Vocal Reactivity. Profile 2 scores were generally unremarkable, and this type was referred to as Average Approach/Average Vocal Reactivity, as these scales significantly differentiated Profile 2 infants from all remaining groups, placing them in the middle. Profile 3 was characterized by low levels of Falling Reactivity, Soothability, Cuddliness, and Approach, and was thus referred to as Low Approach/Difficult to Calm. Profile 4 can be described as Active, as Activity Level was the only scale associated with an extreme [highest] value for this type and statistically significant differences relative to the other four profiles. Follow-up t-tests and Figure 1b indicated that the 5th Profile was distinguished by high scores on a number of scales addressing positive affectivity and regulation: Smiling and Laughter, High Intensity Pleasure, Perceptual Sensitivity, Vocal Reactivity, Duration of Orienting, Low Intensity Pleasure, and Soothability, and was thus labeled High Positive/Regulated.

Cluster Analysis

Hierarchical cluster analysis, linking pairs of cases with the smallest distance between them until all cases are linked into one cluster composed of all cases [agglomerative clustering], was performed first, consistent with previous studies [e.g. Caspi & Silva, 1995; Prokasky et al., 2017; Sanson et al., 2009]. Visual inspection of the resulting dendogram indicated that a three-cluster solution provided optimal fit for the younger infants. Consistent with findings from LPA, a five-cluster solution fit well for the older subsample. Model fit indices are not available with CA techniques, thus, K-means clustering analyses results were evaluated conceptually, providing an interpretable 3-cluster solution for the younger, as well as a 5-cluster solution for the older age group. Decisions concerning the number of clusters are generally based on “practical judgment or theoretical foundations” [Hair, Anderson, Tatham, & Black, 1995, p. 443], and these solutions were deemed optimal in light of the available literature supporting both 3 and 5-cluster typologies. These findings parallel reported LPA results with respect to the number of types. The clustering results were also graphed [Figures 2a and ​2b], and examined for content similarity to the LPA solutions.

a and b. 3-cluster solution younger and 5-cluster solution for older infants.

Resulting types were compared via ANOVAs, conducted separately for younger and older age groups. All 14 IBQ-R dimensions were able to reliably differentiate between 3 temperament types derived on the basis of CA for younger infants [mean η2 = .25; range .07–.45]. Approach [η2 = .45], Vocal Reactivity [η2 = .42], and Smiling and Laughter [η2 = .42] were associated with the largest effect sizes. Similarly, all IBQ-R dimensions reliability differentiated between 5 temperament clusters for the older infants [mean η2 = .33; range .20–.45]. The largest effects sizes were noted for Approach [η2 = .45] and Soothability [η2 = .41].

The 3-cluster solution for younger infants paralleled LPA types [Figure 2a]. For younger infants, Cluster 1 was labeled High Negative/Difficult to Calm, because of highest scores on all negative emotionality scales [Distress to Limitations, Fear, and Sadness], along with the lowest levels of Falling Reactivity, Soothability, and Cuddliness; significantly different from the other two profiles according to follow-up t-tests with Bonferroni corrections [Supplementary Tables]. Cluster 2 was referred to as High Positive/Regulated, because infants in this group consistently received the highest positive affectivity and regulation-related ratings, including: Smiling and Laughter, High Intensity Pleasure, Perceptual Sensitivity, Approach, Vocal Reactivity, Falling Reactivity, Duration of Orienting, Low Intensity Pleasure, Soothability and Cuddliness. Cluster 3 was marked by low positive affectivity/surgency scores, including: Activity Level, Smiling and Laughter, High Intensity Pleasure, Perceptual Sensitivity, Approach, and Vocal Reactivity, as well as low levels of Fear and Duration of Orienting, and was thus labeled Fearless/Low Positive/Low Orienting.

There were also notable similarities to the LPA solution for the older subsample [Figure 2b]. Cluster 1 had the highest scores on Fear and Duration of Orienting, significantly different from the remaining four clusters, and was thus labeled as the Fearful/Attentive type. Cluster 2 infants had the highest scores on Approach and Soothability, also statistically significant in differentiating this cluster from all others, with this type referred to as High Approach/Soothable as a result. Cluster 3 was associated with significantly different and lowest scores on High Intensity Pleasure, Approach, Falling Reactivity, Soothability, and Cuddliness, consistent with the assigned Low Pleasure/Low Approach/Difficult to Calm label. Cluster 4 was average on all dimensions, as none of the scales were associated with statically significant differences relative to all remaining clusters, so was labeled Average. Finally, Cluster 5 scores represented the lowest levels of Distress to Limitations and Sadness, and this group was accordingly referred to as the Low Frustration/Low Sadness type. Perceptual Sensitivity and Low Intensity Pleasure also significantly differentiated this cluster from all others; however, these scores were mid-range relative to the other clusters, and thus not referenced in the label.

In summary, there are important similarities between LPA and CA solutions, wherein both approaches indicated potentially key differences in types that coalesce in early/mid vs. late infancy. Parallel profiles/clusters could be identified [Table 4], and despite some notable differences, both person-centered techniques point to the importance of surgency/positive affectivity and regulation-related dimensions in discriminating among temperament types. For example, the combination of low Falling Reactivity, Soothability and Cuddliness, referred to as Difficult to Calm, often contributed to differentiation among types across approaches. Although cluster and profile solutions for younger infants involved fearlessness, distinctions based on high levels of fear for the older age group emerged only in the context of CA follow-up t-tests. LPA and CA results indicated that Approach gained importance in type differentiation among older infants.

Table 4

Matching profiles and clusters for younger and older age groups.

Direct Comparison of LPA and CA results

Younger Infants

The chi-square test comparing distributions of cases assigned to matched types [Table 4] based on the LPA vs. the CA classification of the younger subsample was significant [χ2 = 60.49; p