In well-being research, positive and negative affectivity, have emerged as fundamental dimensions of human flourishing [1-6]. The positive affect is a dimension that moves from pleasant engagement [7-9] (e.g., enthusiastic and active), to unpleasant disengagement (e.g., sad and bored). Individuals high in positive affect are characterized by enthusiasm, activity, alertness, “hardiness” (i.e., control, commitment and challenge), and experience a greater appreciation of life, feel more secure and confident, and have more positive social relations [10-11]. That is, a general disposition towards a positive attitude both over time and varying circumstances [12,13]. The negative affect dimension moves from unpleasant engagement (e.g., anger and fear) to disengagement (e.g. calm and serene) reflecting expressions such as anger, contempt, guilt, shame, fear and depressiveness [9,14]. These two dimensions of affect are seen as two independent dimensions in which individuals might experience either high and/or low affectivity, thus, leading to the possibility of four different combinations [15].

In this line of thought, Archer, Garcia et al. have developed model: self-fulfilling (high positive affect, low negative affect); high affective (high positive affect, high negative affect); low affective (low positive affect, low negative affect); and self-destructive (low positive affect, high negative affect) [16-24]. These studies have shown that individuals with a self-fulfilling profile report feeling more energetic and optimistic than individuals with any of the other three affective profiles [19]. Individuals with a self-fulfilling profile also report higher satisfaction with life, higher psychological well-being, lower depressive symptoms, and score higher in agentic values (i.e. autonomy, responsibility, self-acceptance). There is, indeed, growing evidence that positive affect and life satisfaction are associated with positive outcomes in relationships, work, and physical health [25]. Moreover, a self-fulfilling profile, which is at the fundamental level an optimistic disposition or a happy personality [26], has been linked to better physical health and more effective coping strategies [20,27]. In the pursuit of happiness, for instance, people seem to use specific and different strategies to maintain or increase happiness [28,29]: social affiliation, partying and clubbing, instrumental goal pursuit, mental control, active leisure, passive leisure, direct attempts, and religion (see Table 1 for a definition of each strategy). Seeing that individuals with different profiles vary in aspects of cognition, emotion, and conation [19,23], we expected that individuals differ in the use of strategies to increase happiness depending on their affective profile.lower depressive symptoms, and score higher in agentic values (i.e. autonomy, responsibility, self-acceptance). There is, indeed, growing evidence that positive affect and life satisfaction are associated with positive outcomes in relationships, work, and physical health [25]. Moreover, a self-fulfilling profile, which is at the fundamental level an optimistic disposition or a happy personality [26], has been linked to better physical health and more effective coping strategies [20,27]. In the pursuit of happiness, for instance, people seem to use specific and different strategies to maintain or increase happiness [28,29]: social affiliation, partying and clubbing, instrumental goal pursuit, mental control, active leisure, passive leisure, direct attempts, and religion (see Table 1 for a definition of each strategy). Seeing that individuals with different profiles vary in aspects of cognition, emotion, and conation [19,23], we expected that individuals differ in the use of strategies to increase happiness depending on their affective profile.

Table 1: Definition of each of the happiness-increasing strategies [35].

Indeed, in a recent study, Schütz et al. [1] used the affective profile model as the backdrop for the investigation of differences in happinessincreasing strategies between individuals. The major findings by Schütz et al. [1] were that individuals with a self-destructive profile reported using seven of the happiness-increasing strategies to a significantly lower extent than all other profiles. The only exception was the mental control strategy (i.e., avoiding negative experiences by suppressing negative thoughts and feelings but also ponder about negative aspects of life), which the individuals with a self-destructive profile used more often than any of the other profiles. Moreover, individuals with a self-fulfilling profile reported using mental control less often than individuals with a high negative affect profile (i.e., high affects and selfdestructives). This suggests that, the tendencies that are characteristic of mental control are not only significantly different between unhappy (i.e., who express a self-destructive profile) and happy individuals (i.e., who express a self-fulfilling profile), but also within individuals who experience high levels of positive affect.

In other words, because the affective profile model allows the comparison of individuals who differ in one affect dimension and are similar in the other (i.e., a person-centered model; [30,31], it helps our understanding of the affective system as a dynamic and adaptive system within the individual [32]. Specifically, a common comparison or linear association would only suggest that mental control is positively related to negative affect and negatively to positive affect. In contrast, using the affective profile model, mental control as a strategy to increase happiness seems to be associated to high levels of negative affect when the individual is low in positive affect (i.e., between differences: selfdestructive vs. self-fulfilling), but also when positive affect is high (i.e., between differences: self-destructive vs. self-fulfilling). That is, increases in positive emotions or experiences would not decrease individuals’ tendency to rumination and avoidance of negative experiences by suppressing negative thoughts, as long as they are not accompanied with decreases in negative emotions or experiences [33,34].

Furthermore, individuals with a self-fulfilling profile used more often strategies related to agency (e.g., instrumental goal pursuit, direct attempts), communion (e.g., social affiliation), and spirituality (e.g., religion) (Schütz et al. [1]). In other words, in order to increase their happiness, individuals with a self-fulfilling profile are more prone to directly attempt to smile, get them selves in a happy mood, improve themselves, and work on their self-control (i.e., agency). In addition, as part of their pursue of happiness, they have also the tendency to pursue cooperative values: support and encourage friends, help and receive help from others, and try to improve social skills; as well as spiritual tendencies, such as seeking support from faith, performing religious activities, praying, and drinking less alcohol. Nevertheless, compared to individuals with a self-destructive profile, even individuals with low affective and those with high affective profiles reported using different strategies to a higher rate. These difference again suggest that the affective profile model provides information about individuals that are equal in one affective dimension but are different in another. These differences might be useful to understand how one affective dimension is related to specific strategies, while the other affective dimension is held constant.

In the present study we aim to replicate the Schütz et al. [1] using an merged larger sample described in Nima and Garcia [35]. Although this sample also comprises the original cohort from Schütz et al. [1], we use here a two times larger sample [36]. More importantly, the present study differs in specific methodological issues. Firstly, instead of the most commonly used median split method used by, for example, Schütz et al. [1], we used k-means cluster analysis for the profiling. This procedure is useful for person-oriented analyses because it is a bottom-up procedure, which starts by sequentially joining the most similar participants on variables of interest (e.g., positive affect and negative affect) to form groups (i.e., pattern and individual focused) [37]. In contrast, median splits, the method used by Schütz et al. [1], might distort the meaning of high and low, thus, scores just-above and just-below the median become high and low by fiat, not by reality (for a comparison between these two procedures [38]. Secondly, we also use a factor validated happiness-increasing strategies measure [35] that comprises 33 validated items extracted from the 53 items in the version used by Schütz et al. [1]. As an addition and in contrast to Schütz et al. [1], we also discuss our results focusing on matched comparisons or differences between profiles that are similar in one affective dimension and differ in the other in order to shade light on what changes could be expected when individuals increase/decrease their experience of positive or negative affect: self-destructive vs. high affective (matching: high-high negative affect, differing: low-high positive affect), selfdestructive vs. low affective (matching: low-low positive affect, differing: high-low negative affect), high affective vs. self-fulfilling (matching: high-high positive affect, differing: high-low negative affect), and low affective vs. self-fulfilling (matching: low-low negative affect, differing: low-high positive affect).

Ethics statement

The review board of the Network for Empowerment and Well- Being approved the research protocol, which was found to comply with the law concerning research involving humans and requiring only informed consent from the participants.

Participants and procedure

The participants (N = 1000, age mean = 34.22 sd. = 12.73, 404 males and 596 females; [35]) were recruited through Amazons’ Mechanical Turk (MTurk; https://www.mturk.com/mturk/welcome). MTurk allows data collectors to recruit participants (workers) online for completing different tasks in exchange for wages. This method for data collection online has become more common during recent years and it is an empirical tested valid tool for conducting research in the social sciences [39]. Participants were recruited by the following criteria: USresident and to both speak and write fluent in English. Participants were paid a wage of 50 cents (American dollars) for completing the task and informed that the study was confidential and voluntary. The participants were presented with a battery of self-reports comprising the affect and happiness measures, as well as questions pertaining age and gender.

Instruments

Positive affect and negative affect schedule [9]: This instrument instructs participants to rate to what extent they generally have experienced 20 different feelings or emotions (10 positive affect and 10 negative affect) during the last weeks, using a 5-point Likert scale (1 = very slightly, 5 = extremely). The 10–item positive affect scale includes adjectives such as strong, proud, and interested. The 10– item negative affect scale includes adjectives such as afraid, ashamed and nervous. Cronbach’s α were .89 for the positive affect scale and 0.91 for the negative affect scale in the present study.

Happiness-increasing strategies scales [29]: In the present study, participants were asked to rate (1 = never, 7 = all the time) how often they used the strategies identified by Tkach and Lyubomirsky [29]. The 33 items are organized in eight clusters of strategies: social affiliation (e.g., ‘‘Support and encourage friends’’; Cronbach’s α = 0.77), partying and clubbing (e.g., ‘‘Drink alcohol’’; Cronbach’s α = 0.75), mental control (e.g., ‘‘Try not to think about being unhappy’’; Cronbach’s α = 0.52), instrumental goal pursuit (e.g. ‘‘Attempt to reach full potential’’; Cronbach’s α = 0.75), passive leisure (e.g. ‘‘Surf the internet’’; Cronbach’s α = 0.52), active leisure (e.g. ‘‘Exercise’’; Cronbach’s α = 0.65), religion (e.g. ‘‘Seek support from faith’’; Cronbach’s α = 0.70), and direct attempts.

Statistical treatment and analyses

Statistical analyses showed that there were no significant differences between the original sample (N = 500) and the new cohort (N = 500) in any of the studied variables. A Chi-Square (Goodness of fit) indicated that there were no significant gender differences between the samples (χ² (1, N = 1000) = 3.738, p > 0.05). Further analysis using t-test showed no significant differences between the samples regarding age, positive and negative affect and happiness-increasing strategies. Subsequent analyses were therefore conducted using the whole sample.

The statistical program Ropstat [40] was used to cluster participants into the four affective profiles. Specifically, hierarchical k-means cluster analyses with Ward’s method and Average Squared Euclidian distance measures was used to allocate individuals into four profiles, The correlates of cluster membership were analyzed by means of multinomial regressions using the self-destructive group as the reference group [38,41]. The four profiles explained a suboptimal variation of 62.8%: self-fulfilling (n = 380), low affective (n = 239), high affective (n = 207), and self-destructive (n = 172).

Analyses of variance (ANOVA), using SPSS, were conducted with the affective profile as independent variable and with happinessincreasing strategies as dependent variables. Post hoc tests according to Bonferroni were performed for data exhibiting homogeneity of variance according to Levene’s test. When homogeneity of variance was not met, post hoc tests according to Games-Howell were performed. For these variables, Welch’s test of equality of means was used to assess any significant differences between groups.

The ANOVA indicated a significant effect on the strategies by the individuals’ type of affective profile (F(24, 2931) = 12.79; p < 0.001, Eta2 = 0.095, power = 1.00). A One-way ANOVA with affective profiles as the independent variable indicated significant differences upon all the happiness-increasing strategies among the affective profiles: social affiliation (Welch’s (F (3, 455.864) = 58.22, p = .000, est. ω2 = .147), partying and clubbing (F (3, 996) = 22.47, p = .000, η2 = .063, power = 1.00), mental control (F (3, 996) = 58.91, p = .000, η2 = .151, power = 1.00), instrumental goal pursuit (Welch’s (F (3, 463.801) = 50.41, p = .000, est. ω2 = .129), Religion (F (3, 996) = 7.00, p = .000, η2 = .021, power = 0.98), passive leisure (F (3, 996) = 8.50, p = .000, η2 = .025, power = 0.994), active leisure (F (3, 996) = 43.30, p = .000, η2 = .115, power = 1.00), and direct attempts (Welch’s (F (3, 456, 268) = 91.59, p = .000, est. ω2 = .214).

A Bonferroni test and a Games-Howell test, with alpha level set to 0.01, were conducted to compare the mean differences in happinessincreasing strategies between individuals with the four affective profiles. The results showed that the individuals with a self-fulfilling profile had among the highest scores in all happiness-increasing strategies except for mental control and passive leisure, in which individuals with a self-destructive profile scored the highest. More specifically, individuals with a self-fulfilling profile reported more frequent use of social affiliation (compared to all profiles), partying and clubbing (compared to individuals with self-destructive and low affective profiles), instrumental goal pursuit (compared to individuals with self-destructive and low affective profiles), active leisure (compared to individuals with self-destructive and low affective profiles), religion (compared to individuals with a low affective profile), and direct attempts (compared to all profiles).

Individuals with a high affective profiles also reported more frequent use of social affiliation (although lower compared to individuals with the self-fulfilling profile), partying and clubbing (compared to individuals with self-destructives and low affective profiles), mental control (compared to individuals with a self-fulfilling profile), instrumental goal pursuit (compared to individuals with self-destructive and low affective profiles), passive leisure (compared to individuals with the low affective profile), active leisure (compared to individuals with selfdestructive and low affective profiles, but lower than individuals with a self-fulfilling profile), and direct attempts (compared to individuals with self-destructive and the low affective profiles, but lower than individuals with a self-fulfilling profile).

The individuals with a low affective profile scored lower than the individuals with a self-fulfilling profile in the strategies of social affiliation, partying and clubbing, instrumental goal pursuit, religion, passive leisure active leisure and direct attempts, however not lower than the individuals with a self-destructive profile. The individuals in the self-destructive profile scored higher in mental control (compared to all the profiles) and passive leisure (compared to low affective). For further details, see Table 2.

The aim of the present study was to replicate the findings from Schütz et al. [1] using a larger sample, a person-oriented method to construe the four affective profiles (i.e., k-means cluster analysis), and a recently validated measure of the happiness-increasing strategies originally created by Tkach and Lyubomirsky [29]. In essence, the findings by Schütz et al. [1] were replicated. Specifically, the results showed that, compared to individuals with any of the other profiles, the individuals with a self-fulfilling profile used three of the eight happiness-increasing strategies to a greater extent: social affiliation, active leisure, and direct attempts. As in Schütz et al. [1], this suggests a higher level of cooperative and agentic behavior among individuals with this affective profile. The activities in the social affiliation strategy comprise cooperative values to guide behavior such as: supporting and encouraging friends, helping others, interacting with friends, and receiving help from friends. The agentic strategies of direct attempts and active leisure comprise self-directed choices (e.g., directly attempt to smile, get oneself in a happy mood, work on one’s self-control, exercise) that enables people to have control over their lives and own well-being [42]. Self-directed and cooperative behavior have been related to psychological well-being, mental health, psychological dysfunction and suffering [43-45], and are suggested to help the individual become happier and healthier who showed that increases in agency and cooperation are associated to improvement in depression). The differences found here, however, are more congruent across profiles. Past findings, for instance, have not showed higher tendency among individuals with a self-fulfilling profile to use social affiliation and active leisure more frequently than individuals with a high affective profile. We argue that these discrepancies between our results and those in the Schütz et al. [1] might depend on the different methods used.

The use of cluster analyses to categorize individuals in different profiles does indeed create profiles that are more heterogeneous between groups and equally homogeneous within groups [38]. Hence, the differences found here might correspond to actual differences in affectivity combinations between profiles. Also in this line, the results here showing that individuals with a self-destructive profile use the strategy of mental control more often than individuals with any of the other profiles is more straightforward that in past research. Furthermore, differences between individuals with a low affective and those with a high affective profiles in the usage of social affiliation is more consistent with the notion of extroversion being related to more frequently socializing when compared to introversion (see Garcia [46], who showed that individuals with a high affective profile report higher levels of extroversion than individuals with a low affective profile). Again, probably due to the person-oriented approach used to create the profiles, but also due to the more reliable and valid measure of the happiness-increasing strategies used here.

That being said, as in past studies, the differences in happinessincreasing strategies between individuals found at the extreme from each other are rather similar to those found by Schütz et al. [1] when the self-fulfilling profile was compared to the self-destructive profile (i.e., high positive and low negative affect vs. low positive and high negative affect). In contrast, when the other extremes were compared, high affective vs. low affective (i.e., high positive and negative affect vs. low positive and negative affect), our results show that this individuals differ in their usage of all strategies except two (i.e., mental control and religion). See Figure 1, black arrows, for differences when profiles at each extreme were compared in the present study. In Schütz et al. [1], individuals with these profiles differed only in the strategies of mental control and goal pursuit. At first sight, the comparison between extremes suggests that most of the happiness-increasing strategies are associated with high levels of positive affect even when the individual is high in negative affect, which is accordingly to Fredrickson’s theory of positive emotions: positive emotions serve as a buffer against negative ones [47]. However, we mapped the differences between profiles that differ in one affectivity dimension while holding the other constant in order to understand in which conditions increases/decreases in any of the affectivity dimensions will lead to changes in usage of strategies (Figure 1, grey arrows). For example, decreases in negative affect while positive affect is low (self-destructive vs. low affective) will lead or might be a function of a decrease in usage of both the mental control and the passive leisure strategies. In contrast, an increase in positive affect while negative affect is high (self-destructive vs. high affective) will lead or might be a function of both an increase in the usage of five strategies and the decrease in the usage of mental control.

Table 2: Mean differences between individuals with the four affective profiles. All significant differences are at the p< 0.01 level. Note: SE = standard error; * Homogeneity of variance according to Levene’s test was not met. Welch test of equality of means revealed a significant difference between groups; D significantly higher compared to individuals with a self-destructive profile, L significantly higher compared to individuals with a low affective profile; H significantly higher compared to individuals with a high affective profile; F significantly higher compared to individuals with a self-fulfilling profile.

Figure 1: (Black arrows) Differences found between individuals with affective profiles that are at their extremes: self-destructive vs. self-fulfilling (low-high positive affect, high-low negative affect) and low affective vs. high affective (low-high positive affect, low-high negative affect). (Grey arrows) Differences found when individuals were matched in one affective dimension, and differed in the other (i.e., within differences): self-destructive vs. high affective (matching: highhigh negative affect, differing: low-high positive affect), self-destructive vs. low affective (matching: low-low positive affect, differing: high-low negative affect), high affective vs. self-fulfilling (matching: high-high positive affect, differing: high-low negative affect), and low affective vs. self-fulfilling (matching: low-low negative affect, differing: low-high positive affect). Note: Reprinted with permission from Well-Being and Human Performance Sweden AB.

The cross-sectional design of the present study only permits comparisons between individuals matched in one of the affective dimensions. Nevertheless, these comparisons help to discern differences between individuals in one affectivity dimension, while holding the other constant [43]. Importantly, individuals were allocated in profiles using a person-centered method or a bottom-up procedure that starts by sequentially joining the most similar individuals on variables of interest (e.g., positive affect and negative affect) to form groups (i.e., pattern and individual focused) [38,41,48]. In this respect cluster analytic methods are data-driven and create affective profiles groups that are relative to each other. Data driven methods, compared to median-splits, come closer to modeling the dynamic nature of within and between group variability of individual patterns of affectivity, while the median-split procedure is static in nature—equally sized groups are pre-determined because two variables are each divided in high and low using the median [38]. Thus, the matched results discussed here are plausible to be found when using longitudinal data to scrutinize possible changes in an individual who moves from one profile to another. In this case, we suggest that the movement of an individual is more likely to be as depicted in Figure 1’s grey arrows, rather than Figure 1’s black arrows. This can be investigated using person-centered methods, such as, cell-wise analysis in which a reference (i.e., an estimated expected cell frequency) to which the observed cell frequency is compared [49,50].

Moreover, some aspects related to data collection using MTurk might influence the validity of the results, such as, workers’ attention levels, cross-talk between participants, and the fact that participants get remuneration for their answers [39]. Nevertheless, a large quantity of studies show that data on psychological measures collected through MTurk meets academic standards, is demographically diverse, and that health measures show satisfactory internal as well as test-retest reliability [39,51-53]. In addition, the amount of payment does not seem to affect data quality; remuneration is usually small, and workers report being intrinsically motivated (e.g., participate for enjoyment) [39].

One final limitation worth mentioning is that although we used a validated measure for the assessment of happiness strategies, the alphas of some of the clusters were rather low [35]. Although beyond the present article, a way to improve the instrument is to re-organize the items around a stronger theoretical basis rather than the factor analyses organization proposed. The experiment suggested that human thought can be organized across five planes based on the heriercharly development of the brain through evolution. Each of the planes corresponds to a specific modulation of a different basic emotional conflict: sexual, material, emotional, intellectual, and spiritual. As Nima and Garcia [35], we suggest that Cloninger’s five planes can be useful in a re-organization of the strategies and in the addition of new important strategies. For instance, the measure lacks items corresponding to basic needs, such as those corresponding to the sexual plane, and self-actualizing needs, such as those corresponding to the spiritual plane [54-58].

It seems that in order to understand increases in happiness using the strategies assessed here, the various combinations of negative and positive affect provides the most detailed information. The self-fulfilling experience, depicted as high positive affect and low negative affect, is a combination of agentic (instrumental goal pursuit, active leisure, direct attempts), communal (social affiliation), and spiritual (religion) strategies. That is, exercising intentionality and self-awareness to work on one’s self-control, try to improve one self, be in the service of others (e.g., help others and improve social skills), and also having faith.

“Happiness is when what you think, what you say, and what you do are in harmony.”

Mahatma Gandhi

We would like to thank Dr. Shane MacDonald for his advice on k-mean analysis.

Dr. Danilo Garcia is the Director of the Blekinge Center of Competence, which is the Blekinge County Council’s research and development unit. The Center works on innovations in public health and practice through interdisciplinary scientific research, person-centered methods, community projects, and the dissemination of knowledge in order to increase the quality of life of the habitants of the county of Blekinge, Sweden. He is also an Associate Professor at the University of Gothenburg and together with Professor Trevor Archer and Associate Professor Max Rapp Ricciardi, the leading researcher of the Network for Emowerment and Well-Being.

Danilo Garcia conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, and reviewed drafts of the paper. Erica Schütz analyzed the data, prepared the tables, and reviewed drafts of the paper. Trevor Archer wrote the paper and reviewed drafts of the paper.

The development of this article was funded by AFA Insurance (Dnr 130345). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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