peerj_json_files/PeerJ_Json_113.json

{
    "v1_Abstract": "Background. The affective profile model categorizes individuals as 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). The model has been used extensively among Swedes to discern differences between profiles regarding happiness, depression, and also life satisfaction. The aim of the present study was to investigate such differences in a sample of residents of the USA. The study also investigated differences between profiles with regard to happiness-increasing strategies. Methods. In Study I, 900 participants reported affect (Positive Affect Negative Affect Schedule; PANAS) and happiness (Happiness-Depression Scale). In Study II, 500 participants self-reported affect (PANAS), life satisfaction (Satisfaction With Life Scale), and how often they used specific strategies to increase their own happiness (Happiness-Increasing Strategies Scales). Results. The results showed that, compared to the other profiles, self-fulfilling individuals were less depressed, happier, and more satisfied with their lives. Nevertheless, self-destructive were more depressed, unhappier, and less satisfied that all other profiles. The self-fulfilling individuals tended to use strategies related to agentic (e.g., instrumental goal-pursuit), communal (e.g., social affiliation), and spiritual (e.g., religion) values when pursuing happiness. Conclusion. These differences suggest that promoting positive emotions can positively influence a depressive-to-happy state as well as increasing life satisfaction. Moreover, the present study shows that pursuing happiness through strategies guided by agency, communion, and spirituality is related to a self-fulfilling experience described as high positive affect and low negative affect.",
    "v1_col_limitations": "limitations and future research : One limitation of the present set of studies is that the results are based on MTurk workers\u2019 selfreports. Nevertheless, consistent with earlier research suggesting MTurk as a valid tool for collecting data using personality scales (Buhrmester et al., 2011), other researchers have found that health measures using MTurk data shows satisfactory internal reliability and test-retest reliability (Shapiro, Chandler & Mueller, 2013). Furthermore, the prevalence of depression among MTurk workers matches the prevalence of this illness in the general population; which makes MTurk a valid tool even for clinical research (Shapiro et al., 2013). The measures used here are validated and reliable measures of happiness, depression, life satisfaction, and affect; however, there are other established measures that could have been used for the measurement of depression (e.g., The Patient Health Questionnaire; Kroenke, Spitzer & Williams, 2001). The Short Depression-Happiness Scale (Joseph et al., 2004), used in Study I, was found appropriate firstly because it was developed as a short easy-to-distribute scale based on the increasing awareness of the therapeutic potential of the positive psychological perspective (e.g. Cloninger, 2006, Joseph & Linley, 2004; Keyes & Lopez, 2002). This scale has shown good psychometric\n342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366\nPeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013)\nR ev ie w in g M an\nus cr ip t\n17\nproperties of internal consistency reliability (Cronbach\u2019s \u03b1 between .77-92), test-retest reliability (r = .68 in a 2-week interval), and convergent and discriminant validity with measures of depression (Beck\u2019s Depression Inventory), happiness (Oxford\u2019s Happiness Inventory) and personality (NEO Five Factor Inventory) (Joseph et al., 2004).\nThe lack of studies in adult populations using the affective profiles model and positive\nmeasures of well-being did not permit comparison of the results presented to other than earlier research among adolescents and young adults, thus, showing the need for further studies on adults regarding these factors. The reliability coefficients for some of the happiness-increasing strategies were low (e.g., Direct Attempts showed an Cronbach\u2019s alpha = .56). In studies among Swedes this scales have been modified through factor analyses (Nima et al., 2013). Although most of the scales in the present study showed alphas above .63, further studies focusing in the validation of these scales are needed. Furthermore, specific emotions vary widely across the lifespan. Findings among men and women in the US, for example, show that as people age they become less stressed and angry, although worry seems to persist as a negative emotion in peoples lives during middle age (Stone, Schwartz, Broderick & Deaton, 2010). Positive emotions such as happiness and enjoyment along with negative emotions such as sadness, however, show very limited change with age (Stone et al., 2010). Although the present study did not aim to investigate variations in specific emotions with respect to age, further studies exploring increases/decreases in PA and NA are needed.\nFinally, since median splits distort the meaning of high and low, it is plausible to criticize\nthe validity of the procedure used here to create the different affective profiles scores just-above\nand just-below the median become high and low by fiat, not by reality (Sch\u00fctz, Archer & Garcia, 2013). Nevertheless, a recent study (MacDonald & Kormi-Nouri, 2013) used k-means cluster analysis to test if the affective profiles model emerged as theorized by Archer and colleagues. The\n367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386\n387\n388 389 390\nPeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013)\nR ev ie w in g M an\nus cr ip t\n18\naffective profile model was replicated using the k-means cluster analysis and the four affective profiles emerged as the combinations of high vs. low affectivity. The procedure used by these researchers is useful for person-oriented analyses (see Bergman, Magnusson et al., 2003), thus, suggesting the original procedure by Archer as valid.",
    "v2_Abstract": "Background. The affective profile model categorizes individuals as 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). The model has been used extensively among Swedes to discern differences between profiles regarding happiness, depression, and also life satisfaction. The aim of the present study was to investigate such differences in a sample of residents of the USA. The study also investigated differences between profiles with regard to happiness-increasing strategies. Methods. In Study 1, 900 participants reported affect (Positive Affect Negative Affect Schedule; PANAS) and happiness (Happiness-Depression Scale). In Study 2, 500 participants self-reported affect (PANAS), life satisfaction (Satisfaction With Life Scale), and how often they used specific strategies to increase their own happiness (Happiness-Increasing Strategies Scales) Results. The results showed that, compared to the other profiles, self-fulfilling individuals were less depressed, happier, and more satisfied with their lives. Nevertheless, self-destructive were more depressed, unhappier, and less satisfied that all other profiles. The self-fulfilling individuals tended to use strategies related to agentic (e.g., instrumental goal-pursuit), communal (e.g., social affiliation), and spiritual (e.g., religion) values when pursuing happiness. Conclusion. These differences suggest that promoting positive emotions can positively influence a depressive-to-happy state as well as increasing life satisfaction. Moreover, the present study shows that pursuing happiness through strategies guided by agency, communion, and spirituality is related to a self-fulfilling experience described as high positive affect and low negative affect.",
    "v1_text": "results and discussion : A Multiple Analysis of Variance (MANOVA) indicated a significant effect for gender (F(4, 889) = 4.32; p = .002, Eta2 = 0.02, power = 0.93) as well as for affective profile (F(12, 2673) = 162.19; p < .001, Eta2 = 0.42, power = 1.00). The interaction of gender and affective profile was not significant (p = .236). A between-subjects ANOVA showed an significant gender effects for happiness (F(1, 892) = 7.60; p = 0.006), whereby the female participants expressed a higher level of happiness (M = 9.66, SD = 2.13) than the male participants (M =9.35, SD = 2.33). A between-subject ANOVA indicated significant affective profile effects for PA (F(3, 892) = 513.78; p < .001), NA (F(3, 892) = 503.58; p < .001), happiness (F(3, 892) = 68.20; p < .001), and depression (F(3, 892) = 71.50; p < .001). A Bonferroni correction to the alpha level of .01 showed that the self-destructive group had significantly higher scores in NA and depression as well as lower scores in happiness in comparison to the other affective profiles. The self-fulfilling group differed significantly from the self-destructive profiles in all measured variables; PA, NA, happiness and depression. As expected, the high affective ones differed significantly from the self-fulfilling group in all variables except PA and the low affective ones differed significantly from the self-fulfilling group in all variables except NA. Which is not so strange since both the self-fulfilling group and the high affective group are characterized as high in PA and the same goes for self-fulfilling individuals and low affective individuals who are characterized by low NA. For further details, see table 1. Table 1 here Study II First a MANOVA (3 x 2 factorial design) was applied with affective profiles and gender as independent variables and with PA, NA and life satisfaction as dependent variables. The analysis did not indicate any significant interaction effect (p = 0.14), but did indicate a significant effect for gender (F(3, 490) = 4.91; p < 0.01, Eta2 = 0.03, power = 0.91) as well as for affective profiles (F(9, 1476) = 119.15; p < 0.001, Eta2 = 0.42, power = 1.00). Secondly, a MANOVA (1 x 2 factorial design) was applied with affective profiles and gender as independent variables and with happiness-increasing strategies as dependent variables. The analysis did not indicate any significant interaction effect (p = 0.93), but did indicate a significant effect for gender (F(8, 485) = 5.85; p < 0.001, Eta2 = 0.09, power = 1.00) as well as for affective profiles (F(24, 1461) = 8.64; p < 0.001, Eta2 = 0.12, power = 1.00). A between-subjects ANOVA was conducted in order to test gender differences in PA, NA and life satisfaction. The result indicated significant gender effects for: NA (F(1, 492) = 10.89; p 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 12 <0.01), whereby the female participants expressed a higher level of NA (M = 1.94, SD = 0.83) than the male participants (M =1.72, SD = 0.67). This specific result stands in contrast to the results from Study I, which showed that females reported higher happiness than males. Nevertheless, this is a well-known paradox in the literature females seem to experience positive and negative emotions equally intensive, explaining why female often report both experiencing more negative moods and depressive symptoms and also higher levels of happiness than males (Fujita, Diener & Sandvik, 1991). A between-subjects ANOVA was conducted to investigate gender differences in happiness-increasing strategies. The result indicated significant gender effects for: Social Affiliation (F(1, 492) = 17.67; p <0.001), whereby the female participants expressed a higher level of Social Affiliation (M = 3.43, SD = 0.56) than the male participants (M =3.27, SD = 0.65); Instrumental Goal Pursuit (F(1, 492) = 6.60; p <0.01), whereby the female participants expressed a higher level of Instrumental Goal Pursuit (M = 3.33, SD = 0.81) than the male participants (M =3.19, SD = 0.82); Religion (F(1, 492) = 23.18; p <0.001), whereby the female participants expressed a higher Religion (M = 3.08, SD = 1.13) than the male participants (M =2.63, SD = 1.04); Passive Leisure (F(1, 492) = 9.25; p <0.01), whereby the female participants expressed a higher level of Passive Leisure (M = 3.30, SD = 0.55) than the male participants (M =3.16, SD = 0.60); Direct Attempts (F(1, 492) = 4.06; p <0.05), whereby the female participants expressed a higher level of Direct Attempts (M = 3.66, SD = 0.58) than the male participants (M =3.60, SD = 0.64). The differences presented here are a replication of the original study conducted by Tkach and Lyubomirsky (2006): females focus on behaviour such as maintaining relationships (i.e., Social Affiliation), pursuing career goals (i.e., Instrumental Goal Pursuit), performing religious activities (i.e., Religion), and watching TV (i.e., Passive Leisure) more frequently than males when they try to increase their happiness. As suggested by Tkach and Lyubomirsky (2006, pp. 214), the gender differences replicated here \u201care consistent with the 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 13 gender differences reported for behaviors used to combat bad moods (Thayer et al., 1994)\u201d. In order to test differences in life satisfaction for each of the four affective profiles a between-subject ANOVA was conducted The result indicated significant effects for life satisfaction (F(3, 492) = 49.26; p <0.001). Further, a between-subject ANOVA was conducted in order to test differences in happiness-increasing strategies for each of the four affective profiles. The mean scores of life satisfaction as well as for happiness-increasing strategies for all four affective profiles are presented in Table 2. Table 2 here A Bonferroni test, with alpha level set to .01, was conducted to compare the mean differences in life satisfaction as well as for happiness-increasing strategies between affective profiles. The results showed, replicating earlier findings, among Swedes, that that the selfdestructive group had lower scores in life satisfaction compared to all the other affective profiles. The self-fulfilling group had higher scores in life satisfaction compared to all the other affective profiles. Regarding happiness-increasing strategies the results showed that that the selfdestructive group had lower scores in all happiness-increasing strategies except for Mental Control. For further details, see Table 3. Table 3 here General discussion The aim of this set of studies was to examine the connections between the four types of affective profiles (self-fulfilling, high affective, low affective, self-destructive) to happiness and depression (Study I), satisfaction with life and happiness-increasing strategies (Study II) in US-residents. The results showed that the self-fulfilling group reported a significantly higher level of happiness and a significantly lower level of depression than all the three other groups (high affective, low affective, self-destructive). Furthermore, the self-destructive group reported a significantly higher 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 14 level of depression and lower level of happiness than all the other three groups (self-fulfilling, high and low affective). The results also show that the high affective and low affective reported higher level of happiness and lower level of depression than the self-destructive group. But at the same time these groups (high and low affective) also showed significantly lower levels of happiness and significantly higher levels depression than the self-fulfilling group. As suggested by Garcia (2011), low PA among low affectives seems to influence happiness negatively as high NA influences happiness negatively among high affectives. The results presented here are corresponding to the results found in research with Swedish populations showing that high PA is related to less stress, depression, and anxiety (e.g., Garcia et al., 2012; Lindahl & Archer, 2013; Nima, Rosenberg, Archer & Garcia, 2013). Moreover, self-fulfilling, high affective and low affective participants all have higher life satisfaction compared with self-destructive participants. This result also replicates findings among Swedish pupils where self-fulfilling, high and low affective participants showed higher level of life satisfaction compared with self-destructives (e.g., Garcia & Archer, 2012). As suggested by Lindahl and Archer (2013; see also Archer & Kostrzewa, 2013; Archer, Oscar-Berman, Blum & Gold, 2013), positive affect might serve as an anti-depressive factor and, as suggested here, also as protective factor for happiness and life satisfaction. The self-fulfilling participants showed significantly higher results than all other profiles on the direct attempts strategy. Suggesting that in order to increase their happiness the selffulfilling individuals are more prone to directly attempt to smile, get them selves in a happy mood, improve their social skills, and work on their self-control. Indeed, Garcia (2012a) showed that self-fulfilling score higher in personality traits related to agentic values (i.e., autonomy, responsibility, self-acceptance, intern locus of control, self-control) as measured by the Temperament and Character Inventory (Cloninger, Svrakic & Przybeck, 1993). Moreover, selffulfilling individuals scored lower than high NA individuals (high affectives and self- 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 15 destructives) in the strategy of mental control. The mental control scale has been defined as ambivalent behavior, that is, the individual using this happiness-increasing strategy make efforts to avoid negative experiences by suppressing negative thoughts and feelings but also ruminating about negative aspects of life (Tkach and Lyubomirsky, 2006). These tendencies may not only prolong unhappiness, suppressing negative thoughts actually may end up in maintaining these thoughts and thereby aggravate negative affect (Tkach & Lyubomirsky, 2006), which may explain why these tendencies are more frequent among high affective and self-destructive than self-fulfilling individuals. Compared to low PA individuals (i.e., low affectives and self-destructives), the self- fulfilling individuals also reported using more often three of the other happiness-increasing strategies: social affiliation, instrumental goal pursuit, active leisure. Social affiliations activities comprise communal (i.e., cooperation) values to guide behavior such as: supporting and encouraging friends, helping others, trying to improve one self, interacting with friends, and receiving help from friends (Tkach & Lyubomirsky, 2006). Instrumental goal pursuit includes activities directed to achieving goals by trying to reach one\u2019s full potential, studying, organizing one\u2019s life and goals, and striving for the accomplishment of tasks (Tkach & Lyubomirsky, 2006). Finally, the use of active leisure comprises a proness to wellness through fitness and flow, that is, exercising and working on hobbies or activities in which the individual uses her/his strengths and becomes absorbed by the activity itself (Tkach & Lyubomirsky, 2006). In other words, both instrumental goal pursuit and active leisure comprises agentic (i.e., autonomous, self-directed) values guiding behavior in order to approach well-being. Indeed, among Swedes (Nima et al., 2012, 2013), these three strategies (social affiliation, instrumental goal pursuit, and active leisure) have been found to be positively related to subjective well-being. Agency and cooperation are also related to mental health, dysfunction and suffering (Cloninger & Zohar, 2011; Garcia, Anckars\u00e4ter & Lundstr\u00f6m, 2013; Garcia, Lundstrom, Brandstrom, Rastam, Cloninger, et al., \u0308 \u0308 \u0308 \u030a 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 16 2013; Garcia, Nima & Archer, 2013) and are suggested to help the individual to become happier and healthier (Cloninger, 2013; see also Johansson Lyssarides, Andersson & Rousseau, 2013, who showed that increases in agency and cooperation are associated to improvement in depression). Moreover, compared to the self-destructives, the self-fulfilling individuals reported more frequently seeking support from faith, performing religious activities, praying, and drinking less alcohol (i.e., the religion happiness-increasing strategy). Indeed, Cloninger (2013) has suggested that while agency and cooperation might lead to happiness and health, spiritual values might be needed for becoming a self-fulfilled individual that lives in harmony with the changing world. See Figure 1 for a summary of the results. Figure 1 should be here statistical treatment : We used participants\u2019 self-reported affect measured by the PANAS from both Study I and 2 (N = 1,400) in order to classify participants in the four affective profiles. Participants\u2019 PA and NA scores were divided into high and low (cut-off points: low PA = 3.0 or less; high PA = 3.1 or above; low NA = 1.8 or less; and high NA = 1.9 or above). For Study I, the two independent variables of the study were gender and affective profile: self-fulfilling (n = 241; 153 males, 88 females), low affective (n = 236; 137 males, 99 females), high affective (n = 180; 115 males, 65 females), and self-destructive (n = 243; 145 males, 98 females). The dependent variables were PA, NA, happiness, and depression. 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 9 As detailed in Study I, both samples were used in the classification of the four affective profiles. 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 11 The number of participants in each profile for Study II were as follows: 158 self-fulfilling (75 males, 83 females), 92 low affective (42 males, 50 females), 123 high affective (54 males, 69 females), and 127 self-destructive (46 males, 81 females). The affective profiles and gender were the independent variables, PA, NA, life satisfaction, and the happiness-increasing strategies were the dependent variables. An important observation here is the gender distribution between profiles. For example, in Study I there were more self-destructive males than females, while in Study II there were more self-destructive females than males. This difference might mirror the gender distribution across Study I (550 males and 350 females) and Study II (217 male and 283 females). Across both samples of females, the prevalence of the self-destructive profile was 28%, while among men was 25%. The prevalence of this profile reported here among males and females is the same that was observed among Swedes (Sch\u00fctz, Garcia & Archer, 2013). participants and procedure : The participants (N = 900, age mean = 28.72 sd. = 19.10, 550 males and 350 females) were USresidents recruited through Amazons\u2019 Mechanical Turk (MTurk; https://www.mturk.com/mturk/welcome). MTurk allows data collectors to recruit participants (workers) online for completing different tasks in change 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 (see Buhrmester, Kwang & Gosling, 2011). Participants were recruited by the following criteria: US-resident and to both speak and write fluent in English. Participants were paid a wage of two American dollars for completing the task 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 8 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. As in Study I, participants (N = 500, age mean = 34.08 sd. =12.55; 217 male and 283 female) 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 10 were recruited from MTurk by the following criteria: resident of the USA and to both speak and write fluent in English. Participants were paid a wage of two 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, life satisfaction, and happiness-increasing strategies measures, as well as questions pertaining age and gender. ethics statement : This research protocol was approved by the Ethics Committee of the University of Gothenburg and written informed consent was obtained from all the study participants. conclusion : The present set of studies expands earlier results among Swedes to a relative large sample of USresidents. The results suggest that the affective profile model distinguish important differences in happiness, depression, and life satisfaction between individuals. These differences suggest that promoting positive emotions can positively influence a depressive-to-happy state as well as increasing life satisfaction. Moreover, the present study describes further how affective profiles differ with regard to happiness-increasing strategies. These specific results suggest that the pursue of happiness through agentic, communal, and spiritual values leads to a self-fulfilling experience defined as frequently experiencing positive emotions and infrequently experiencing negative emotions. \u201cIt was right then that I started thinking about legends and captions : Table 1. Mean scores in PA, NA, happiness, and depression for each affective profile in Study I. Table 2. Means in life satisfaction and happiness-increasing strategies among affective profiles in Study II. Table 3. Mean differences in life satisfaction and happiness-increasing strategies between affective profiles. Figure 1. Summary of the results from Study I and II showing the differences between affective profiles in happiness, depression, life satisfaction, and the happiness-increasing strategies. 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 20 happiness-increasing strategies : In order to intentionally pursue happiness, people seem to use different strategies. Tkach and Lyubomirsky (2006) have identified, using first an open-ended survey, 53 happiness-increasing strategies used by residents of the USA (for studies using this scale among Swedes see, Garcia, 2012b; Nima, Archer and Garcia, 2012, 2013). Tkach and Lyubomirsky (2006) found, using 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 6 factor analysis, eight factors: Social Affiliation (e.g. \u2018\u2018Support and encourage friends\u2019\u2019), Partying and Clubbing (e.g. \u2018\u2018Drink alcohol\u2019\u2019), Mental Control (e.g. \u2018\u2018Try not to think obout being unhappy\u2019\u2019), Instrumental Goal Pursuit (e.g. \u2018\u2018Study\u2019\u2019), Passive Leisure (e.g. \u2018\u2018Surf the internet\u2019\u2019), Active Leisure (e.g. \u2018\u2018Exercise\u2019\u2019), Religion (e.g. \u2018\u2018Seek support from faith\u2019\u2019) and Direct Attempts (e.g. \u2018\u2018Act happy/smile, etc.\u2019\u2019). Results have shown that these happiness-increasing strategies accounted for 52% of the variance in happiness, while the Big Five personality traits, which traditionally have been linked to happiness, accounted for 46%. Further, even after controlling for the contribution of personality, the happiness-increasing strategies accounted for 16% of the variance in happiness. However, these relationships might not be a direct one. For example, Extraversion, which is strongly related to high PA (Larsen & Ketelaar, 1991), is related to the use of the Social Affiliation strategy, which, in turn, is related to happiness. Tkach and Lyubomirsky (2006) suggested that the efficacy of the happiness-increasing strategies is also likely to vary to some extent. However, the strategy that was the most robust predictor of low levels of happiness was Mental Control, which was closely related to Neuroticism. This strategy is defined as ambivalent intentional efforts aimed, on one side and avoidance of negative thoughts and feelings as well as proneness towards contemplation of negative aspects of life on the other. Regarding the affective profiles, if the profiles differ in the way they pursue happiness (i.e., approaching happy experiences versus preventing unhappy experiences), then it could be expected that the profiles differ in the use of the strategies described here. For example, it could be expected that high PA profiles should score higher in strategies such as Social Affiliation and Active Leisure due to the close positive relationship between Extraversion and PA. High NA profiles could be expected to score higher in strategies such as Mental Control, because the positive relationship between Neuroticism and NA. The present study 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 7 To the best of our knowledge, the affective profiles model has been mostly used among Swedish participants. Some cultures explain the world as good and controllable and others emphasize negative emotions as normal (Myers & Diener, 1995; Diener, Suh and Oishi, 1997). In this context, it is interesting noticing that the right to pursuit individual happiness is listed as an absolute right in the United States of America\u2019s declaration of independence (Tkach and Lyubomirsky, 2006). The model, however, has shown identical results in the few studies using other populations (for three studies using Dutch, Indonesian, respectively Iranian participants see Kunst, 2011; Adrianson, Djamaludin, Neila and Archer, 2013; Garcia and Moradi, 2013). The aim of the present study was to investigate differences in happiness, depression, life satisfaction and use of strategies to increase happiness among affective profiles in residents of the United States of America (US-residents). Study I method : instruments : Positive Affect and Negative Affect Schedule (PANAS; Watson et al., 1988). The PANAS instructs participants to rate to what extent they generally have experienced 20 different feelings or emotions (10 PA and 10 NA) during the last weeks, using a 5-point Likert scale (1 = very slightly, 5 = extremely). The 10\u2013item PA scale includes adjectives such as strong, proud, and interested. The 10\u2013 item NA scale includes adjectives such as afraid, ashamed and nervous. Cronbach\u2019s \u03b1 were .87 for PA and .89 for NA in the present study. The Short Depression-Happiness Scale (Joseph et al., 2004). This instrument consists of six items, three items measuring happiness (e.g., \u201cI felt happy\u201d) and three reverse coded items measuring depressive states (e.g., \u201cI felt my life was meaningless\u201d). Participants rate how frequently they feel the way described in the item on a four-point scale: \u201cnever\u201d, \u201crarely\u201d, \u201csometimes\u201d, \u201coften\u201d. In the present study, Cronbach\u2019s \u03b1 was .85 for the happiness scale and .76 for the depression scale. The same instrument as in Study I was used in Study II to measure PA and NA (i.e., the PANAS). Cronbach\u2019s \u03b1 were .88 for PA and .90 for NA in Study II. Satisfaction with Life Scale (Diener, Emmons, Larsen and Griffin, 1985). The instrument consists of 5 statements (e.g., \u201cIn most of my ways my life is close to my ideal\u201d) for which participants are asked to indicate degree of agreement in a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The life satisfaction score was established by summarizing the 5 statements for each participant. Cronbach\u2019s \u03b1 were .90 in the present study. Happiness-Increasing Strategies Scales (Tkach and Lyubomirsky, 2006). 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 (2006). The happiness-increasing strategies are organized in eight clusters: Social Affiliation (e.g., \u2018\u2018Support and encourage friends\u2019\u2019; Cronbach\u2019s \u03b1 = 0.79), Partying and Clubbing (e.g., \u2018\u2018Drink alcohol\u2019\u2019; Cronbach\u2019s \u03b1 = 0.74), Mental Control (e.g., \u2018\u2018Try not to think about being unhappy\u2019\u2019; Cronbach\u2019s \u03b1 = 0.43), Instrumental Goal Pursuit (e.g. \u2018\u2018Study\u2019\u2019; Cronbach\u2019s \u03b1 = 0.76), Passive Leisure (e.g. \u2018\u2018Surf the internet\u2019\u2019; Cronbach\u2019s \u03b1 = 0.63), Active Leisure (e.g. \u2018\u2018Exercise\u2019\u2019; Cronbach\u2019s \u03b1 = 0.65), Religion (e.g. \u2018\u2018Seek support from faith\u2019\u2019; Cronbach\u2019s \u03b1 = 0.70), and Direct Attempts (e.g. \u2018\u2018Act happy/smile, etc.\u2019\u2019; Cronbach\u2019s \u03b1 = 0.56). thomas jefferson on the declaration of independence : and the part about our right to life, liberty, and the pursuit of happiness. And I remember thinking how did he know to put the pursuit part in there?\u201d Will Smith as Christopher Gardner in The Pursuit of Happyness 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 19 affective profiles self-fulfilling : N = 158 High affective N = 123 Low affective N = 92 Self-destructive N = 127 self-fulfilling : Positive Affect 0.17* 1.32* 1.46* Negative Affect -1.12* 0.05ns -1.28* Life satisfaction 1.05* 0.75* 2.01* Social Affiliation 0.05ns 0.28* 0.54* Partying and Clubbing -0.16ns -0.06ns 0.12ns Mental Control -0.31* -0.09ns -0.47* Instrumental Goal Pursuit -0.04ns 0.39* 0.54* Religion 0.17ns 0.23ns 0.54* Passive Leisure -0.16ns 0.05ns 0.05ns Active Leisure 0.11ns 0.29* 0.49* Direct Attempts 0.24* 0.31* 0.64* High affective Positive Affect -0.17* 1.15* 1.29* Negative Affect 1.11* -1.17* -0.16ns Life satisfaction -1.05* -0.31ns 0.96* Social Affiliation -0.05ns 0.24ns 0.50* Partying and Clubbing 0.16ns 0.11ns 0.29* Mental Control 0.31* 0.23* -0.16ns Instrumental Goal Pursuit 0.04ns 0.43* 0.58* Religion -0.17ns 0.05ns 0.36ns Passive Leisure 0.16ns 0.21ns 0.20ns Active Leisure -0.11ns 0.18ns 0.38* Direct Attempts -0.23* 0.07ns 0.40ns Low affective Positive Affect -1.32* -1.15* 0.14ns Negative Affect -0.05 -1.17* -1.32* 1 2 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 34 Life satisfaction -0.75* 0.31ns 1.26* Social Affiliation -0.28* -0.24ns 0.26* Partying and Clubbing 0.06ns -0.11ns 0.18ns Mental Control 0.09ns -0.23* -0.40* Instrumental Goal Pursuit -0.39* -0.43* 0.15ns Religion -0.23ns -0.05ns 0.31ns Passive Leisure -0.05ns -0.21ns -0.00ns Active Leisure -0.29* -0.18ns 0.20ns Direct Attempts -0.31* -0.07ns 0.33* Self-destructive Positive Affect -1.46* -1.29* -0.14* Negative Affect 1.28* 0.16ns 1.33* Life satisfaction -2.01* -0.96* -1.26* Social Affiliation -0.54* -0.50* -0.26* Partying and Clubbing -0.12ns -0.29* -0.18ns Mental Control 0.47* 0.16ns 0.39* Instrumental Goal Pursuit -0.54* -0.58* -0.15ns Religion -0.54* -0.36ns -0.31ns Passive Leisure -0.05ns -0.20ns 0.00ns Active Leisure -0.49* -0.38* -0.20ns Direct Attempts -0.64* -0.40* -0.33* ns = non significant, * p < 0.01 with Bonferroni Correction.3 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 35 Figure 1 Figure 1 Summary of the results from Study I and II showing the differences between affective profiles in happiness, depression, life satisfaction, and the happiness-increasing strategies. PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t 36 PeerJ reviewing PDF | (v2013:07:649:1:1:NEW 15 Aug 2013) R ev ie w in g M an us cr ip t",
    "v2_text": "results and discussion : First a MANOVA (3 x 2 factorial design) was applied with affective profiles and gender as independent variables and with PA, NA and life satisfaction as dependent variables. The analysis did not indicate any significant interaction effect (p = 0.14), but did indicate a significant effect for gender (F(3, 490) = 4.91; p < 0.01, Eta2 = 0.03, power = 0.91) as well as for affective profiles (F(9, 1476) = 119.15; p < 0.001, Eta2 = 0.42, power = 1.00). Secondly, a MANOVA (1 x 2 factorial design) was applied with affective profiles and gender as independent variables and with happiness-increasing strategies as dependent variables. The analysis did not indicate any significant interaction effect (p = 0.93), but did indicate a significant effect for gender (F(8, 485) = 5.85; p < 0.001, Eta2 = 0.09, power = 1.00) as well as for affective profiles (F(24, 1461) = 8.64; p < 0.001, Eta2 = 0.12, power = 1.00). A between-subjects ANOVA was conducted in order to test gender differences in PA, NA and life satisfaction. The result indicated significant gender effects for: NA (F(1, 492) = 10.89; p <0.01), whereby the female participants expressed a higher level of NA (M = 1.94, SD = 0.83) than the male participants (M =1.72, SD = 0.67). This specific result stands in contrast to the results from Study I which showed that females reported higher happiness than males. Nevertheless, this is a well-known paradox in the literaturefemales seem to experience positive and negative emotions equally intensive, explaining why female often report both experiencing more negative moods and depressive symptoms and also higher levels of happiness than males (Fujita, Diener & Sandvik, 1991). A between-subjects ANOVA was conducted to investigate gender differences in happiness-increasing strategies. The result indicated significant gender effects for: Social Affiliation (F(1, 492) = 17.67; p <0.001), whereby the female participants expressed a higher level of Social Affiliation (M = 3.43, SD = PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t 0.56) than the male participants (M =3.27, SD = 0.65); Instrumental Goal Pursuit (F(1, 492) = 6.60; p <0.01), whereby the female participants expressed a higher level of Instrumental Goal Pursuit (M = 3.33, SD = 0.81) than the male participants (M =3.19, SD = 0.82); Religion (F(1, 492) = 23.18; p <0.001), whereby the female participants expressed a higher Religion (M = 3.08, SD = 1.13) than the male participants (M =2.63, SD = 1.04); Passive Leisure (F(1, 492) = 9.25; p <0.01), whereby the female participants expressed a higher level of Passive Leisure (M = 3.30, SD = 0.55) than the male participants (M =3.16, SD = 0.60); Direct Attempts (F(1, 492) = 4.06; p <0.05), whereby the female participants expressed a higher level of Direct Attempts (M = 3.66, SD = 0.58) than the male participants (M =3.60, SD = 0.64). The differences presented here are a replication of the original study conducted by Tkach and Lyubomirsky (2006). In order to test differences in life satisfaction for each of the four affective profiles a between-subject ANOVA was conducted The result indicated significant effects for life satisfaction (F(3, 492) = 49.26; p <0.001). Further, a between-subject ANOVA was conducted in order to test differences in happiness-increasing strategies for each of the four affective profiles. The mean scores of life satisfaction as well as for happiness-increasing strategies for all four affective profiles are presented in Table 2. Table 2 here A Bonferroni test, with alpha level set to .01, was conducted to compare the mean differences in life satisfaction as well as for happiness-increasing strategies between affective profiles. The results showed, replicating earlier findings, among Swedes, that that the self-destructive group had lower scores in life satisfaction compared to all the other affective profiles. The self-fulfilling group had higher scores PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t in life satisfaction compared to all the other affective profiles. Regarding happiness-increasing strategies the results showed that that the self-destructive group had lower scores in all happiness-increasing strategies except for Mental Control. For further details, see Table 3. Table 3 here General discussion The aim of this set of studies was to examine the connections between the four types of affective profiles (self-fulfilling, high affective, low affective, self-destructive) to happiness and depression (Study I), satisfaction with life and happiness-increasing strategies (Study II) in US-residents. The results showed that the self-fulfilling group reported a significantly higher level of happiness and a significantly lower level of depression than all the three other groups (high affective, low affective, self-destructive). Furthermore, the self-destructive group reported a significantly higher level of depression and lower level of happiness than all the other three groups (self-fulfilling, high and low affective). The results also show that the high affective and low affective reported higher level of happiness and lower level of depression than the self-destructive group. But at the same time these groups (high and low affective) also showed significantly lower levels of happiness and significantly higher levels depression than the self-fulfilling group. As suggested by Garcia (2011), low PA among low affectives seems to influence happiness negatively as high NA influences happiness negatively among high affectives. The results presented here are corresponding to the results found in research with Swedish populations showing that high PA is related to less stress, depression, and anxiety (e.g., Garcia et al., 2012; Lindahl & Archer, 2013). Moreover, self-fulfilling, high affective and low affective participants all have higher life satisfaction compared with self-destructive PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t participants. This result also replicates findings among Swedish pupils where self-fulfilling, high and low affective participants showed higher level of life satisfaction compared with self-destructives (e.g., Garcia & Archer, 2012). As suggested by Lindahl and Archer (2013; see also Archer & Kostrzewa, 2013; Archer, Oscar-Berman, Blum & Gold, 2013), positive affect might serve as an anti-depressive factor and, as suggested here, also as protective factor for happiness and life satisfaction. The self-fulfilling participants showed significantly higher results than all other profiles on the direct attempts strategy. Suggesting that in order to increase their happiness the self-fulfilling individuals are more prone to directly attempt to smile, get them selves in a happy mood, improve their social skills, and work on their self-control. Indeed, Garcia (2012a) showed that self-fulfilling score higher in personality traits related to agentic values (i.e., autonomy, responsibility, self-acceptance, intern locus of control, self-control) as measured by the Temperament and Character Inventory (Cloninger, Svrakic & Przybeck, 1993). Moreover, self-fulfilling individuals scored lower than high NA individuals (high affectives and self-destructives) in the strategy of mental control. The mental control scale has been defined as ambivalent behavior, that is, the individual using this happiness-increasing strategy make efforts to avoid negative experiences by suppressing negative thoughts and feelings but also ruminating about negative aspects of life (Tkach and Lyubomirsky, 2006). These tendencies may not only prolong unhappiness, suppressing negative thoughts actually may end up in maintaining these thoughts and thereby aggravate negative affect (Tkach & Lyubomirsky, 2006), which may explain why these tendencies are more frequent among high affective and self-destructive than self-fulfilling individuals. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t Compared to low PA individuals (i.e., low affectives and self-destructives), the self-fulfilling individuals also reported using more often three of the other happiness-increasing strategies: social affiliation, instrumental goal pursuit, active leisure. Social affiliations activities comprise communal (i.e., cooperation) values to guide behavior such as: supporting and encouraging friends, helping others, trying to improve one self, interacting with friends, and receiving help from friends (Tkach & Lyubomirsky, 2006). Instrumental goal pursuit includes activities directed to achieving goals by trying to reach one\u2019s full potential, studying, organizing one\u2019s life and goals, and striving for the accomplishment of tasks (Tkach & Lyubomirsky, 2006). Finally, the use of active leisure comprises a proness to wellness through fitness and flow, that is, exercising and working on hobbies or activities in which the individual uses her/his strengths and becomes absorbed by the activity itself (Tkach & Lyubomirsky, 2006). In other words, both instrumental goal pursuit and active leisure comprises agentic (i.e., autonomous, self-directed) values guiding behavior in order to approach well-being. Indeed, among Swedes (Nima et al., 2012, 2013), these three strategies (social affiliation, instrumental goal pursuit, and active leisure) have been found to be positively related to subjective well-being. Agency and cooperation are also related to mental health, dysfunction and suffering (Cloninger & Zohar, 2011; Garcia, Anckars\u00e4ter & Lundstr\u00f6m, 2013; Garcia, Lundstrom, Brandstrom, Rastam, \u0308 \u0308 \u0308 \u030a Cloninger, et al., 2013) and are suggested to help the individual to become happier and healthier (Cloninger, 2013; see also Johansson Lyssarides, Andersson & Rousseau, 2013, who showed that increases in agency and cooperation are associated to improvement in depression). Moreover, compared to the self-destructives, the self-fulfilling individuals reported more frequently seeking support from faith, performing religious activities, praying, and drinking less alcohol (i.e., the religion PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t happiness-increasing strategy). Indeed, Cloninger (2013) has suggested that while agency and cooperation might lead to happiness and health, spiritual values might be needed for becoming a self-fulfilled individual that lives in harmony with the changing world. See Figure 1 for a summary of the results. Figure 1 should be here Limitations and future research One major limitation is that the present set of studies was conducted using self-reports. Nevertheless, the measures used here are validated and reliable measures of happiness, depression, life satisfaction, and affect. Moreover, the lack of studies in adult populations using the affective profiles model and measures of well-being did not permit comparison of the results presented to other than earlier research among adolescents and young adults, thus, showing the need for further studies on adults regarding these factors. The reliability coefficients for some of the happiness-increasing strategies were low (e.g., Direct Attempts showed an Cronbach\u2019s alpha = .56). In studies among Swedes this scale has been modified through factor analyses (Nima et al., 2013). Although most of the scales in the present study showed alphas above .63, further studies focusing in the validation of these scales are needed. Finally, since median splits distort the meaning of high and low, it is plausible to criticize the validity of the procedure used here to create the different affective profilesscores just-above and just-below the median become high and low by fiat, not by reality (Sch\u00fctz, Archer & Garcia, 2013). Nevertheless, a recent study (MacDonald & Kormi-Nouri, 2013) used k-means cluster analysis to test if the affective profiles model emerged as theorized by Archer and colleagues. The affective profile model was replicated using the k-means cluster analysis and the four affective PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t profiles emerged as the combinations of high vs. low affectivity. The procedure used by these researchers is useful for person-oriented analyses (see Bergman, Magnusson et al., 2003), thus, suggesting the original procedure by Archer as valid. Conclusion The present set of studies expands earlier results among Swedes to a relative large sample of US-residents. The results suggest that the affective profile model distinguish important differences in happiness, depression, and life satisfaction between individuals. These differences suggest that promoting positive emotions can positively influence a depressive-to-happy state as well as increasing life satisfaction. Moreover, the present study describes further how affective profiles differ with regard to happiness-increasing strategies. Showing that agentic, communal, and spiritual values guide behaviour when self-fulfilling individuals pursue happiness. \u201cIt was right then that I started thinking about Thomas Jefferson on the Declaration of Independence and the part about our right to life, liberty, and the pursuit of happiness. And I remember thinking how did he know to put the pursuit part in there?\u201d Will Smith as Christopher Gardner in The Pursuit of Happyness PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t participants and procedure : The participants (N = 900, age mean = 28.72 sd. = 19.10, 550 males and 350 females) were US-residents recruited through Amazons\u2019 Mechanical Turk (MTurk; https://www.mturk.com/mturk/welcome). MTurk allows data collectors to recruit participants (workers) online for completing different tasks in change 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 (see Buhrmester, Kwang & Gosling, 2011). Participants were recruited by the following criteria: US-resident and to both speak and write fluent in English. Participants were paid a wage of two 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 (PANAS; Watson et al., 1988). The PANAS instructs participants to rate to what extent they generally have experienced 20 different feelings or emotions (10 PA and 10 NA) during the last weeks, using a 5-point Likert scale (1 = very slightly, 5 = extremely). The 10\u2013item PA scale includes adjectives such as strong, proud, and interested. The 10\u2013 item NA scale includes adjectives such as afraid, ashamed and nervous. Cronbach\u2019s \u03b1 were .87 for PA and .89 for NA in the present study. The Short Depression-Happiness Scale (Joseph Linley, Harwood, Lewis & McCollam, 2004). This instrument consists of six items, three items measuring happiness (e.g., \u201cI felt happy\u201d) and three reverse coded items measuring depressive PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t states (e.g., \u201cI felt my life was meaningless\u201d). Participants rate how frequently they feel the way described in the item on a four-point scale: \u201cnever\u201d, \u201crarely\u201d, \u201csometimes\u201d, \u201coften\u201d. In the present study, Cronbach\u2019s \u03b1 was .85 for the happiness scale and .76 for the depression scale. Statistical treatment We used participants\u2019 self-reported affect measured by the PANAS from both Study 1 and 2 (N = 1,400) in order to classify participants in the four affective profiles. Participants\u2019 PA and NA scores were divided into high and low (cut-off points: low PA = 3.0 or less; high PA = 3.1 or above; low NA = 1.8 or less; and high NA = 1.9 or above). For study 1, the two independent variables of the study were gender and affective profile: self-fulfilling (n = 241; 153 males, 88 females), low affective (n = 236; 137 males, 99 females), high affective (n = 180; 115 males, 65 females), and self-destructive (n = 243; 145 males, 98 females). The dependent variables were PA, NA, happiness, and depression. Results and discussion A Multiple Analysis of Variance (MANOVA) indicated a significant effect for gender (F(4, 889) = 4.32; p = .002, Eta2 = 0.02, power = 0.93) as well as for affective profile (F(12, 2673) = 162.19; p < .001, Eta2 = 0.42, power = 1.00). The interaction of gender and affective profile was not significant (p = .236). A between-subjects ANOVA showed an significant gender effects for happiness (F(1, 892) = 7.60; p = 0.006), whereby the female participants expressed a higher level of happiness (M = 9.66, SD = 2.13) than the male participants (M =9.35, SD = 2.33). A between-subject ANOVA indicated significant affective profile effects for PA (F(3, 892) = 513.78; p < .001), NA (F(3, 892) = 503.58; p < .001), happiness (F(3, PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t 892) = 68.20; p < .001), and depression (F(3, 892) = 71.50; p < .001). A Bonferroni correction to the alpha level of .01 showed that the self-destructive group had significantly higher scores in NA and depression as well as lower scores in happiness in comparison to the other affective profiles. The self-fulfilling group differed significantly from the self-destructive profiles in all measured variables; PA, NA, happiness and depression. As expected, the high affective ones differed significantly from the self-fulfilling group in all variables except PA and the low affective ones differed significantly from the self-fulfilling group in all variables except NA. Which is not so strange since both the self-fulfilling group and the high affective group are characterized as high in PA and the same goes for self-fulfilling individuals and low affective individuals who are characterized by low NA. For further details, see table 1. Table 1 here Study II As in Study I, participants (N = 500, age mean = 34.08 sd. =12.55; 217 male and 283 female) were recruited from MTurk by the following criteria: resident of the USA and to both speak and write fluent in English. Participants were paid a wage of two 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, life satisfaction, and happiness-increasing strategies measures, as well as questions pertaining age and gender. Instruments The same instrument as in Study I was used in Study II to measure PA and NA (i.e., the PANAS). Cronbach\u2019s \u03b1 were .88 for PA and .90 for NA in Study II. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t Satisfaction with Life Scale (Diener, Emmons, Larsen and Griffin, 1985). The instrument consists of 5 statements (e.g., \u201cIn most of my ways my life is close to my ideal\u201d) for which participants are asked to indicate degree of agreement in a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The life satisfaction score was established by summarizing the 5 statements for each participant. Cronbach\u2019s \u03b1 were . 90 in the present study. Happiness-Increasing Strategies Scales (Tkach and Lyubomirsky, 2006). 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 (2006). The happiness-increasing strategies are organized in eight clusters: Social Affiliation (e.g., \u2018\u2018Support and encourage friends\u2019\u2019; Cronbach\u2019s \u03b1 = 0.79), Partying and Clubbing (e.g., \u2018\u2018Drink alcohol\u2019\u2019; Cronbach\u2019s \u03b1 = 0.74), Mental Control (e.g., \u2018\u2018Try not to think about being unhappy\u2019\u2019; Cronbach\u2019s \u03b1 = 0.43), Instrumental Goal Pursuit (e.g. \u2018\u2018Study\u2019\u2019; Cronbach\u2019s \u03b1 = 0.76), Passive Leisure (e.g. \u2018\u2018Surf the internet\u2019\u2019; Cronbach\u2019s \u03b1 = 0.63), Active Leisure (e.g. \u2018\u2018Exercise\u2019\u2019; Cronbach\u2019s \u03b1 = 0.65), Religion (e.g. \u2018\u2018Seek support from faith\u2019\u2019; Cronbach\u2019s \u03b1 = 0.70), and Direct Attempts (e.g. \u2018\u2018Act happy/smile, etc.\u2019\u2019; Cronbach\u2019s \u03b1 = 0.56). Statistical treatment As detailed in Study I, both samples were used in the classification of the four affective profiles. The number of participants in each profile for Study II were as follows: 158 self-fulfilling (75 males, 83 females), 92 low affective (42 males, 50 females), 123 high affective (54 males, 69 females), and 127 self-destructive (46 males, 81 females). The affective profiles and gender were the independent variables, PA, NA, life satisfaction, and the happiness-increasing strategies were the dependent variables. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t ethics statement : This research protocol was approved by the Ethics Committee of the University of Gothenburg and written informed consent was obtained from all the study participants. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t legends and captions : Table 1. Mean scores in PA, NA, happiness and depression for each affective profile in Study I. Table 2. Means in life satisfaction and happiness-increasing strategies among affective profiles in Study II. Table 3. Mean differences, in life satisfaction and happiness-increasing strategies between affective temperaments. Figure 1. Summary of the differences between affective profiles in happiness, depression, life satisfaction, and the happiness-increasing strategies. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t method : affective profiles self-fulfilling : N = 158 High affective N = 123 Low affective N = 92 Self-destructive N = 127 self-fulfilling : Positive Affect 0.17* 1.32* 1.46* Negative Affect -1.12* 0.05ns -1.28* Life satisfaction 1.05* 0.75* 2.01* Social Affiliation 0.05ns 0.28* 0.54* Partying and Clubbing -0.16ns -0.06ns 0.12ns Mental Control -0.31* -0.09ns -0.47* Instrumental Goal Pursuit -0.04ns 0.39* 0.54* Religion 0.17ns 0.23ns 0.54* Passive Leisure -0.16ns 0.05ns 0.05ns Active Leisure 0.11ns 0.29* 0.49* Direct Attempts 0.24* 0.31* 0.64* High affective Positive Affect -0.17* 1.15* 1.29* Negative Affect 1.11* -1.17* -0.16ns Life satisfaction -1.05* -0.31ns 0.96* Social Affiliation -0.05ns 0.24ns 0.50* Partying and Clubbing 0.16ns 0.11ns 0.29* Mental Control 0.31* 0.23* -0.16ns Instrumental Goal Pursuit 0.04ns 0.43* 0.58* Religion -0.17ns 0.05ns 0.36ns Passive Leisure 0.16ns 0.21ns 0.20ns Active Leisure -0.11ns 0.18ns 0.38* Direct Attempts -0.23* 0.07ns 0.40ns Low affective Positive Affect -1.32* -1.15* 0.14ns Negative Affect -0.05 -1.17* -1.32* Life satisfaction -0.75* 0.31ns 1.26* PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t Social Affiliation -0.28* -0.24ns 0.26* Partying and Clubbing 0.06ns -0.11ns 0.18ns Mental Control 0.09ns -0.23* -0.40* Instrumental Goal Pursuit -0.39* -0.43* 0.15ns Religion -0.23ns -0.05ns 0.31ns Passive Leisure -0.05ns -0.21ns -0.00ns Active Leisure -0.29* -0.18ns 0.20ns Direct Attempts -0.31* -0.07ns 0.33* Self-destructive Positive Affect -1.46* -1.29* -0.14* Negative Affect 1.28* 0.16ns 1.33* Life satisfaction -2.01* -0.96* -1.26* Social Affiliation -0.54* -0.50* -0.26* Partying and Clubbing -0.12ns -0.29* -0.18ns Mental Control 0.47* 0.16ns 0.39* Instrumental Goal Pursuit -0.54* -0.58* -0.15ns Religion -0.54* -0.36ns -0.31ns Passive Leisure -0.05ns -0.20ns 0.00ns Active Leisure -0.49* -0.38* -0.20ns Direct Attempts -0.64* -0.40* -0.33* ns = non significant, * p < 0.01 with Bonferroni Correction. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t Figure 1 Figure 1. Summary of the differences between affective profiles in happiness, depression, life satisfaction, and the happiness-increasing strategies. PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t PeerJ reviewing PDF | (v2013:07:649:0:0:NEW 12 Jul 2013) R ev ie w in g M an us cr ip t",
    "url": "https://peerj.com/articles/157/reviews/",
    "review_1": "Ludmila Chistoserdova \u00b7 Aug 20, 2013 \u00b7 Academic Editor\nACCEPT\nI find modifications to the original manuscript satisfactory.",
    "review_2": "Ludmila Chistoserdova \u00b7 Jun 1, 2013 \u00b7 Academic Editor\nMINOR REVISIONS\nI look forward to the revised manuscript.",
    "review_3": "Anders Lanz\u00e9n \u00b7 Jun 1, 2013\nBasic reporting\nThe submitted manuscript appears to adhere to all PeerJ policies and is written in very in a very clear and concise English. The Introduction provides a clear overview and presents the scope of the manuscript in relation to previous work.\nExperimental design\nIn this manuscript, McCoy and Matsen provide a well-motivated measure of phylogenetic alpha-diversity (PD), able to take into interpolate between abundance-weighted (Barker 2002, as interpreted by Vellend) and classic -unweigheted PD . Further, they convincingly demonstrate that this measure is able to distinguish between different communities using a selection of three microbiome datasets from healthy and diseased individuals. The work is meaningful, rigorous and the methods described clearly.\n\nHowever, filtering, clustering and other methods to remove PCR- and sequencing-induced artefacts were not addressed properly by the original authors in the two larger sequence datasets (vaginal and oral microbiomes). The first of these used filtering based only on quality scores and the second the RDP pipeline. It has been demonstrated that this method is not sufficient to remove such artefacts (see e.g. Quince et al 2011 (BMC Bioinformatics 12:38.), Schloss et al 2011 (PLoS ONE 6:e27310), Kunin et al 2009 (Environ Microbiol 12:118-123) and will result in inflated OTU richness estimates and skewed OTU-abundance relationships. PD should be less sensitive to such artefacts and this can very well be the main reason for why the estimate suggested here performed better than OTU-based ones. I think that this fact deserves to be mentioned in the Discussion. Better still, the authors could include a dataset in the comparison which has been handled using e.g. AmpliconNoise or DeNoiser and where chimeric sequences were properly addressed, or re-analyse the raw sequence data using such methods, resulting in a new OTU table. Failing this, I think that this common pitfuall of OTU-based analysis deserves to be mentioned in the Discussion as the richness estimates in the original articles do appear very high.\n\nIf the authors are interested in repeating a test with noise-cleaned sequence data and a more robust OTU table, I happily volunteer to help out with data-treatment (sequence cleaning)\nValidity of the findings\nSee Experimental design above. Apart from that no comments.\nAdditional comments\nSpecific comments:\n\nIn the abstract, Simpson diversity is called a \"count-only\" measure. This is unclear and leads this reader to think more of OTU richness. In Table 1, Af and Pf are mentioned in the caption but in the headers \"Ac\" and \"Pc\" are used.\nCite this review as\nLanz\u00e9n A (2013) Peer Review #1 of \"Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth (v0.1)\". PeerJ https://doi.org/10.7287/peerj.157v0.1/reviews/1",
    "review_4": "Catherine Lozupone \u00b7 May 16, 2013\nBasic reporting\nmeets standards\nExperimental design\ngood\nValidity of the findings\ngood\nAdditional comments\nIn this paper, McCoy and Matsen introduce a novel family of phylogenetic alpha diversity measures that interpolates between classical phylogenetic diversity (PD), which does not account for the abundance of phylogenetic lineages in a sample, and an abundance-weighted extension of PD. Using 3 published studies of the human gut microbiota, they evaluate how their new measure and other alpha diversity measures compare in their ability to differentiate samples in different categories, including healthy versus bacterial vaginosis, periodontitis and controls, and skin samples at different developmental stages. Overall, this paper highlights that phylogenetic alpha diversity measures can be more powerful than commonly used \u201cdiscrete\u201d measures that rely on OTU assignments. It also highlights that abundance-weighted phylogenetic alpha diversity measures can be more powerful than phylogenetic alpha diversity measures that do not account for abundances (i.e. PD). The authors point out that abundance-weighted phylogenetic diversity measures are not used commonly in studies of the microbiota and not implemented in commonly used analysis tools such as QIIME and mother, and argue based on their results that they should be. Overall, I agree with them, and thus think that this paper is a valuable contribution to the field. I did, however, think that there were some ways in which the paper could be improved.\n\n1) I was confused by the treatment of rarefaction in this paper. My understanding is that for e.g. the data presented in Table 1, for the OTU and family based \u201cdiscrete\u201d measures (i.e. Shannon, Simpson, Chao1, and ACE), they rarefied the data, and then for the phylogenetic measures they did not rarefy, with the exception of PD, for which they present both the rarefied and unrarefied results. Why do it this way (i.e. rarefy one class of measures and not the other and then for one (PD) do it both ways?).\n\nIn general, I think that you should always rarefy. The variability in the number of sequences per sample has no real meaning (i.e. just an artifact of sequencing since equal amounts of DNA from each sample is added to the sequencer), and this variability has the potential to affect alpha diversity estimates. I realize that later in the paper (in Figure 3 and mathematically) they show that abundance weighted measures are not very sensitive to sampling depth, although I am not convinced that there will not be more sensitivity for environments that are very undersampled.\n\nSo, by making the point that abundance-weighted measures are not sensitive to sampling depth and then not rarefying the data in their analyses, are they trying to say that because of this lack of sensitivity that we should not rarefy when using these measures? What would be the advantage? The only thing that I can think is that you can potentially more accurately estimate alpha diversity with more data, but as this has not been demonstrated here, I still think that it is good practice to rarefy.\n\nWith regard to this, in Figure 3 and Fig. S4, they show that 0.25D(T) and BWPD0.25 are sensitive to sampling depth, and yet as far as I can tell they still do not used rarefied data in their analyses of all three of the microbiota datasets with these measures. On page 10, lines 276-278, the authors note \u201cclassical phylogenetic diversity was among the worst predictors; rarefaction did help\u2026\u201d. Why look at unrarefied at all when they show in Figure 3 themselves that sequencing depth matters and conceptually it of course makes sense that if you have not sequenced fully, the more sequences you look at with PD the more diversity you will see.\n\nFor all three studies, I would really like to see all of the measurements made on rarefied data.\n\n2) The authors evaluate many different alpha diversity measures in this paper. One thing that would be really helpful is a table that described and classifies them all. Perhaps a columns that 1) designated discrete versus phylogenetic 2) designate abundance-weighted/non/in-between, 3) show an equation where appropriate, 4) briefly describes the measure with words, and 5) shows the info in paragraph 1 of the introduction of which measures are phylogenetic \u201cversions\u201d of particular discrete measures and 6) gives a reference.\n\n3) The information given on page 3 on the example datasets should have better consistency on the types of information provided for each one. The description of the skin microbiome is particularly sparse. Information provided for the other samples, such as the range of sequences per sample, how these sequences were generated, quality filtered etc. should be provided.\n\n4) It is kinda interesting that the discrete measures applied at the family level often appear more powerful than those at the OTU level. Any ideas on why this may be the case?\n\nMinor comments:\n1) In the sentence in the abstract \u201cIn all three of the datasets considered, an abundance-weighted measure is the best differentiator between community states.\u201d, the authors should say \u201ca phylogenetic abundance-weighted measure is the best differentiator\u201d as stated could be a discrete abundance-weighted measure which didn't do so good.\n2) Lines 141, 159: I am not sure what the authors mean exactly when they say that they \u201cassigned the root taxonomically\u201d\n3) The meaning of \u201cshallow\u201d and \u201cdeep\u201d in Fig 4 are not defined anywhere.\n4) typos/grammatical\na. line 79: Fix Oh et al reference formatting\nb. line 304: should Tab. 5 be Table 2?\nCite this review as\nLozupone C (2013) Peer Review #2 of \"Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth (v0.1)\". PeerJ https://doi.org/10.7287/peerj.157v0.1/reviews/2",
    "pdf_1": "https://peerj.com/articles/157v0.2/submission",
    "pdf_2": "https://peerj.com/articles/157v0.1/submission",
    "all_reviews": "Review 1: Ludmila Chistoserdova \u00b7 Aug 20, 2013 \u00b7 Academic Editor\nACCEPT\nI find modifications to the original manuscript satisfactory.\nReview 2: Ludmila Chistoserdova \u00b7 Jun 1, 2013 \u00b7 Academic Editor\nMINOR REVISIONS\nI look forward to the revised manuscript.\nReview 3: Anders Lanz\u00e9n \u00b7 Jun 1, 2013\nBasic reporting\nThe submitted manuscript appears to adhere to all PeerJ policies and is written in very in a very clear and concise English. The Introduction provides a clear overview and presents the scope of the manuscript in relation to previous work.\nExperimental design\nIn this manuscript, McCoy and Matsen provide a well-motivated measure of phylogenetic alpha-diversity (PD), able to take into interpolate between abundance-weighted (Barker 2002, as interpreted by Vellend) and classic -unweigheted PD . Further, they convincingly demonstrate that this measure is able to distinguish between different communities using a selection of three microbiome datasets from healthy and diseased individuals. The work is meaningful, rigorous and the methods described clearly.\n\nHowever, filtering, clustering and other methods to remove PCR- and sequencing-induced artefacts were not addressed properly by the original authors in the two larger sequence datasets (vaginal and oral microbiomes). The first of these used filtering based only on quality scores and the second the RDP pipeline. It has been demonstrated that this method is not sufficient to remove such artefacts (see e.g. Quince et al 2011 (BMC Bioinformatics 12:38.), Schloss et al 2011 (PLoS ONE 6:e27310), Kunin et al 2009 (Environ Microbiol 12:118-123) and will result in inflated OTU richness estimates and skewed OTU-abundance relationships. PD should be less sensitive to such artefacts and this can very well be the main reason for why the estimate suggested here performed better than OTU-based ones. I think that this fact deserves to be mentioned in the Discussion. Better still, the authors could include a dataset in the comparison which has been handled using e.g. AmpliconNoise or DeNoiser and where chimeric sequences were properly addressed, or re-analyse the raw sequence data using such methods, resulting in a new OTU table. Failing this, I think that this common pitfuall of OTU-based analysis deserves to be mentioned in the Discussion as the richness estimates in the original articles do appear very high.\n\nIf the authors are interested in repeating a test with noise-cleaned sequence data and a more robust OTU table, I happily volunteer to help out with data-treatment (sequence cleaning)\nValidity of the findings\nSee Experimental design above. Apart from that no comments.\nAdditional comments\nSpecific comments:\n\nIn the abstract, Simpson diversity is called a \"count-only\" measure. This is unclear and leads this reader to think more of OTU richness. In Table 1, Af and Pf are mentioned in the caption but in the headers \"Ac\" and \"Pc\" are used.\nCite this review as\nLanz\u00e9n A (2013) Peer Review #1 of \"Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth (v0.1)\". PeerJ https://doi.org/10.7287/peerj.157v0.1/reviews/1\nReview 4: Catherine Lozupone \u00b7 May 16, 2013\nBasic reporting\nmeets standards\nExperimental design\ngood\nValidity of the findings\ngood\nAdditional comments\nIn this paper, McCoy and Matsen introduce a novel family of phylogenetic alpha diversity measures that interpolates between classical phylogenetic diversity (PD), which does not account for the abundance of phylogenetic lineages in a sample, and an abundance-weighted extension of PD. Using 3 published studies of the human gut microbiota, they evaluate how their new measure and other alpha diversity measures compare in their ability to differentiate samples in different categories, including healthy versus bacterial vaginosis, periodontitis and controls, and skin samples at different developmental stages. Overall, this paper highlights that phylogenetic alpha diversity measures can be more powerful than commonly used \u201cdiscrete\u201d measures that rely on OTU assignments. It also highlights that abundance-weighted phylogenetic alpha diversity measures can be more powerful than phylogenetic alpha diversity measures that do not account for abundances (i.e. PD). The authors point out that abundance-weighted phylogenetic diversity measures are not used commonly in studies of the microbiota and not implemented in commonly used analysis tools such as QIIME and mother, and argue based on their results that they should be. Overall, I agree with them, and thus think that this paper is a valuable contribution to the field. I did, however, think that there were some ways in which the paper could be improved.\n\n1) I was confused by the treatment of rarefaction in this paper. My understanding is that for e.g. the data presented in Table 1, for the OTU and family based \u201cdiscrete\u201d measures (i.e. Shannon, Simpson, Chao1, and ACE), they rarefied the data, and then for the phylogenetic measures they did not rarefy, with the exception of PD, for which they present both the rarefied and unrarefied results. Why do it this way (i.e. rarefy one class of measures and not the other and then for one (PD) do it both ways?).\n\nIn general, I think that you should always rarefy. The variability in the number of sequences per sample has no real meaning (i.e. just an artifact of sequencing since equal amounts of DNA from each sample is added to the sequencer), and this variability has the potential to affect alpha diversity estimates. I realize that later in the paper (in Figure 3 and mathematically) they show that abundance weighted measures are not very sensitive to sampling depth, although I am not convinced that there will not be more sensitivity for environments that are very undersampled.\n\nSo, by making the point that abundance-weighted measures are not sensitive to sampling depth and then not rarefying the data in their analyses, are they trying to say that because of this lack of sensitivity that we should not rarefy when using these measures? What would be the advantage? The only thing that I can think is that you can potentially more accurately estimate alpha diversity with more data, but as this has not been demonstrated here, I still think that it is good practice to rarefy.\n\nWith regard to this, in Figure 3 and Fig. S4, they show that 0.25D(T) and BWPD0.25 are sensitive to sampling depth, and yet as far as I can tell they still do not used rarefied data in their analyses of all three of the microbiota datasets with these measures. On page 10, lines 276-278, the authors note \u201cclassical phylogenetic diversity was among the worst predictors; rarefaction did help\u2026\u201d. Why look at unrarefied at all when they show in Figure 3 themselves that sequencing depth matters and conceptually it of course makes sense that if you have not sequenced fully, the more sequences you look at with PD the more diversity you will see.\n\nFor all three studies, I would really like to see all of the measurements made on rarefied data.\n\n2) The authors evaluate many different alpha diversity measures in this paper. One thing that would be really helpful is a table that described and classifies them all. Perhaps a columns that 1) designated discrete versus phylogenetic 2) designate abundance-weighted/non/in-between, 3) show an equation where appropriate, 4) briefly describes the measure with words, and 5) shows the info in paragraph 1 of the introduction of which measures are phylogenetic \u201cversions\u201d of particular discrete measures and 6) gives a reference.\n\n3) The information given on page 3 on the example datasets should have better consistency on the types of information provided for each one. The description of the skin microbiome is particularly sparse. Information provided for the other samples, such as the range of sequences per sample, how these sequences were generated, quality filtered etc. should be provided.\n\n4) It is kinda interesting that the discrete measures applied at the family level often appear more powerful than those at the OTU level. Any ideas on why this may be the case?\n\nMinor comments:\n1) In the sentence in the abstract \u201cIn all three of the datasets considered, an abundance-weighted measure is the best differentiator between community states.\u201d, the authors should say \u201ca phylogenetic abundance-weighted measure is the best differentiator\u201d as stated could be a discrete abundance-weighted measure which didn't do so good.\n2) Lines 141, 159: I am not sure what the authors mean exactly when they say that they \u201cassigned the root taxonomically\u201d\n3) The meaning of \u201cshallow\u201d and \u201cdeep\u201d in Fig 4 are not defined anywhere.\n4) typos/grammatical\na. line 79: Fix Oh et al reference formatting\nb. line 304: should Tab. 5 be Table 2?\nCite this review as\nLozupone C (2013) Peer Review #2 of \"Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth (v0.1)\". PeerJ https://doi.org/10.7287/peerj.157v0.1/reviews/2\nReview 5: nan\nReview 6: \n",
    "LLM_extracted_review": "['Review 1: I find modifications to the original manuscript satisfactory.\\n\\nReview 2: I look forward to the revised manuscript.\\n\\nReview 3: The submitted manuscript appears to adhere to all PeerJ policies and is written in very clear and concise English. The Introduction provides a clear overview and presents the scope of the manuscript in relation to previous work. However, filtering, clustering and other methods to remove PCR- and sequencing-induced artefacts were not addressed properly by the original authors in the two larger sequence datasets (vaginal and oral microbiomes). The first of these used filtering based only on quality scores and the second the RDP pipeline. It has been demonstrated that this method is not sufficient to remove such artefacts and will result in inflated OTU richness estimates and skewed OTU-abundance relationships. PD should be less sensitive to such artefacts and this can very well be the main reason for why the estimate suggested here performed better than OTU-based ones. I think that this fact deserves to be mentioned in the Discussion. Better still, the authors could include a dataset in the comparison which has been handled using e.g. AmpliconNoise or DeNoiser and where chimeric sequences were properly addressed, or re-analyse the raw sequence data using such methods, resulting in a new OTU table. Failing this, I think that this common pitfall of OTU-based analysis deserves to be mentioned in the Discussion as the richness estimates in the original articles do appear very high. If the authors are interested in repeating a test with noise-cleaned sequence data and a more robust OTU table, I happily volunteer to help out with data-treatment (sequence cleaning). Apart from that no comments. Specific comments: In the abstract, Simpson diversity is called a \"count-only\" measure. This is unclear and leads this reader to think more of OTU richness. In Table 1, Af and Pf are mentioned in the caption but in the headers \"Ac\" and \"Pc\" are used.\\n\\nReview 4: Overall, this paper highlights that phylogenetic alpha diversity measures can be more powerful than commonly used \u201cdiscrete\u201d measures that rely on OTU assignments. It also highlights that abundance-weighted phylogenetic alpha diversity measures can be more powerful than phylogenetic alpha diversity measures that do not account for abundances (i.e. PD). The authors point out that abundance-weighted phylogenetic diversity measures are not used commonly in studies of the microbiota and not implemented in commonly used analysis tools such as QIIME and mother, and argue based on their results that they should be. I did, however, think that there were some ways in which the paper could be improved. I was confused by the treatment of rarefaction in this paper. My understanding is that for the data presented in Table 1, for the OTU and family based \u201cdiscrete\u201d measures, they rarefied the data, and then for the phylogenetic measures they did not rarefy, with the exception of PD, for which they present both the rarefied and unrarefied results. Why do it this way? In general, I think that you should always rarefy. The variability in the number of sequences per sample has no real meaning, and this variability has the potential to affect alpha diversity estimates. I realize that later in the paper they show that abundance weighted measures are not very sensitive to sampling depth, although I am not convinced that there will not be more sensitivity for environments that are very undersampled. So, by making the point that abundance-weighted measures are not sensitive to sampling depth and then not rarefying the data in their analyses, are they trying to say that because of this lack of sensitivity that we should not rarefy when using these measures? What would be the advantage? The only thing that I can think is that you can potentially more accurately estimate alpha diversity with more data, but as this has not been demonstrated here, I still think that it is good practice to rarefy. With regard to this, they show that 0.25D(T) and BWPD0.25 are sensitive to sampling depth, and yet as far as I can tell they still do not use rarefied data in their analyses of all three of the microbiota datasets with these measures. On page 10, lines 276-278, the authors note \u201cclassical phylogenetic diversity was among the worst predictors; rarefaction did help\u2026\u201d. Why look at unrarefied at all when they show in Figure 3 themselves that sequencing depth matters? For all three studies, I would really like to see all of the measurements made on rarefied data. The authors evaluate many different alpha diversity measures in this paper. One thing that would be really helpful is a table that described and classified them all. The information given on page 3 on the example datasets should have better consistency on the types of information provided for each one. The description of the skin microbiome is particularly sparse. Minor comments: In the sentence in the abstract \u201cIn all three of the datasets considered, an abundance-weighted measure is the best differentiator between community states.\u201d, the authors should say \u201ca phylogenetic abundance-weighted measure is the best differentiator\u201d. Lines 141, 159: I am not sure what the authors mean exactly when they say that they \u201cassigned the root taxonomically\u201d. The meaning of \u201cshallow\u201d and \u201cdeep\u201d in Fig 4 are not defined anywhere. Typos/grammatical issues noted.\\n\\nReview 5: nan\\n\\nReview 6: ']"
}