Part two of this paper estimated fl

Part two of this paper estimated flourishing prevalence rates among 10,009 adult New
Zealanders, according to replications of each of the four frequently used operationalizations of
flourishing identified in part one, using the SWI variables and dataset. Results indicated there was a substantial difference in prevalence rates of flourishing depending upon the
operationalization employed, from 24% (Huppert & So), to 39% (Keyes), 41% (Diener et al.), and
47% (Seligman et al.). The low prevalence rate of flourishing from the SWI replication of Huppert
and So’s conceptualisation (24%) most likely reflects their more stringent theoretical and
conceptual criteria for flourishing: to be categorised as flourishing participants are required to
endorse the one item representing positive emotion (which only 41% of the sample did), plus
three out of four components of ‘positive functioning’, and four out of five components of
‘positive characteristics’; thereby allowing participants to score below the thresholds on only two
out of ten items. In contrast, participants could score below the thresholds on six out of 13
components in the SWI replication of Keyes’ operationalization, or seven out 15 items in the SWI
replication of Seligman et al.’s operationalization, and still be categorised as flourishing. In only
requiring an average score of 48 and above, our interpretation of Diener et al.’s operationalization
also allowed greater flexibility across components than our interpretation of Huppert and So’s
operationalization. (This is the most striking difference between these four operationalizations,
and the cause of the variation in prevalence rates.) It is important to note that the use of different
response formats in the SWI survey meant that some of the variation in prevalence rates between
our study and previous studies might be due to the use of different thresholds, making for
potentially inaccurate international comparisons. For example, New Zealand’s 24% flourishing
according to our replication of Huppert and So’s model may not be directly comparable to the
Danes’ 41% flourishing or Portugal’s 10% flourishing diagnosed using the same model (Huppert
& So, 2013). However, by applying consistent methodology for selecting thresholds across all four models in our study, we are confident that the flourishing prevalence rates according to the
four different models are comparable with each other in our study.
While related samples Cochrane’s Q tests indicated all four operationalizations were
significantly different to one another, cross tabulation analysis revealed a strong agreement
between our replications of Keyes’ and Seligman et al.’s operationalizations (81%) and Diener et
al. and Seligman et al.’s (80%). Even the least comparable operationalizations (Huppert and So
and Seligman et al.) indicated moderate agreement (74%). In the absence of an established
empirical benchmark stating what degree of agreement is meaningful, or indeed any criterion
for interpreting what these levels of agreement mean, it is hard to draw any concrete conclusions
from these findings.
The strengths and unique contributions of this study include the application of the four
operational definitions to a very large, nationally representative, sample of adults, which allows
our results to be compared to other population samples; the prospective nature of the SWI, with
two more longitudinal rounds scheduled over the next four years, allowing us to monitor the
prevalence of flourishing among New Zealand adults over time using all four
operationalizations; and the use of cross-tabulation and pairwise Cochrane’s Q tests allowing us
to calculate, for the first time, the degree of agreement between the SWI replications of the
different measures commonly employed to assess flourishing.
In terms of limitations, we experienced challenges in accurately replicating three of the four
operationalizations of flourishing using the available dataset (the FS was replicated exactly).
While the SWI’s large number of wellbeing variables (n = 87) presented us with a compelling four models in our study, we are confident that the flourishing prevalence rates according to the
four different models are comparable with each other in our study.
While related samples Cochrane’s Q tests indicated all four operationalizations were
significantly different to one another, cross tabulation analysis revealed a strong agreement
between our replications of Keyes’ and Seligman et al.’s operationalizations (81%) and Diener et
al. and Seligman et al.’s (80%). Even the least comparable operationalizations (Huppert and So
and Seligman et al.) indicated moderate agreement (74%). In the absence of an established
empirical benchmark stating what degree of agreement is meaningful, or indeed any criterion
for interpreting what these levels of agreement mean, it is hard to draw any concrete conclusions
from these findings.
The strengths and unique contributions of this study include the application of the four
operational definitions to a very large, nationally representative, sample of adults, which allows
our results to be compared to other population samples; the prospective nature of the SWI, with
two more longitudinal rounds scheduled over the next four years, allowing us to monitor the
prevalence of flourishing among New Zealand adults over time using all four
operationalizations; and the use of cross-tabulation and pairwise Cochrane’s Q tests allowing us
to calculate, for the first time, the degree of agreement between the SWI replications of the
different measures commonly employed to assess flourishing.
In terms of limitations, we experienced challenges in accurately replicating three of the four
operationalizations of flourishing using the available dataset (the FS was replicated exactly).
While the SWI’s large number of wellbeing variables (n = 87) presented us with a compelling opportunity to compare these operationalizations, we acknowledge that the fit was not perfect.
Differences in questionnaire items and response formats required us to make subjective decisions
regarding the best way to replicate the original models. The challenge was to stay true to the
theory and conceptualisation of the original models, while also remaining consistent in our methodology across models. We offer the following four examples of the types of challenges we
faced, and our methods for overcoming them.
Firstly, the absence of any categorical diagnosis of flourishing for the Flourishing Scale or
PERMA-P required us to devise our own methods. We were guided by Keyes, and Huppert and
So, in our methodology. This meant selecting a threshold for flourishing on the FS that allowed
endorsement of most, but not necessarily all, of the scale's eight components (scores ≥ 48, range
7-56, meaning respondents had to score an average of six on the 7-point Likert scale). To be
categorised as flourishing in the SWI replication of the PERMA-P required participants to score
above a threshold on two of three items of each component, and four out of the five components
overall. While we acknowledge the limitations in our approach, and acknowledge the PERMA-
P research team’s preference for dashboard reporting, categorical diagnoses of flourishing
provide vital information for decision makers.
Secondly, the various items selected and response formats used in the SWI frequently
differed from those in the original scales. For instance, while the response option for the MHC-
SF measured the frequency with which respondents experienced each component over the past
month, several items in the SWI asked respondents “how much of the time during the past week”
or “how much of the time would you generally say…”. Where possible we used the same items
as the original scale, but some could not be matched to an SWI variable (such as ‘social
coherence’), which meant this component had to be excluded from our analysis. Others were
matched, but not perfectly so, leaving us having to choose the item which came closest to
representing the original construct. Some of these were far from ideal. For instance, the MHC-SF
item for ‘social growth’ (“during the past month, how often did you feel our society is a good
place, or is becoming a better place for all people?”) was operationalized using the reverse-scored
SWI item “For most people in New Zealand life is getting worse rather than better”. Similarly,
Keyes’ ‘social contribution’ item assesses respondents’ contribution at a societal level, while the
SWI item has a greater focus on the individual. The MHC-SF’s ‘social integration’ item
concerning belonging to a community could be interpreted to refer to any type of group or
community, in contrast to the SWI item we were forced to use, which reflects respondents’
perceptions of people in their local area. In this sense we cannot claim to have replicated Keyes’
validated scale completely. The SWI items selected to match the PERMA-P were also not a perfect
replication, but we were at least able to include three different items for each PERMA construct,
allowing us to represent the original scale well in this regard. Despite these obvious limitations,
we maintain that having such a large number of wellbeing variables in the SWI, a large
representative sample, and the FS and ESS models represented in their entirety, made
comparison of the four models a worthwhile exercise.
Thirdly, the greatest single challenge involved the decision making around the selection of
thresholds differentiating between participants endorsing a component of flourishing and those
not endorsing a component. Recently published OECD guidelines on measuring

Results (Indonesian) 1:

Part two of this paper estimated flourishing prevalence rates among 10,009 adult New Zealanders, according to replications of each of the four frequently used operationalizations of flourishing identified in part one, using the SWI variables and dataset. Results indicated there was a substantial difference in prevalence rates of flourishing depending upon the operationalization employed, from 24% (Huppert & So), to 39% (Keyes), 41% (Diener et al.), and 47% (Seligman et al.). The low prevalence rate of flourishing from the SWI replication of Huppert and So’s conceptualisation (24%) most likely reflects their more stringent theoretical and conceptual criteria for flourishing: to be categorised as flourishing participants are required to endorse the one item representing positive emotion (which only 41% of the sample did), plus three out of four components of ‘positive functioning’, and four out of five components of ‘positive characteristics’; thereby allowing participants to score below the thresholds on only two out of ten items. In contrast, participants could score below the thresholds on six out of 13 components in the SWI replication of Keyes’ operationalization, or seven out 15 items in the SWI replication of Seligman et al.’s operationalization, and still be categorised as flourishing. In only requiring an average score of 48 and above, our interpretation of Diener et al.’s operationalization also allowed greater flexibility across components than our interpretation of Huppert and So’s operationalization. (This is the most striking difference between these four operationalizations, and the cause of the variation in prevalence rates.) It is important to note that the use of different response formats in the SWI survey meant that some of the variation in prevalence rates between our study and previous studies might be due to the use of different thresholds, making for potentially inaccurate international comparisons. For example, New Zealand’s 24% flourishing according to our replication of Huppert and So’s model may not be directly comparable to the Danes’ 41% flourishing or Portugal’s 10% flourishing diagnosed using the same model (Huppert & So, 2013). However, by applying consistent methodology for selecting thresholds across all four models in our study, we are confident that the flourishing prevalence rates according to the four different models are comparable with each other in our study. While related samples Cochrane’s Q tests indicated all four operationalizations were significantly different to one another, cross tabulation analysis revealed a strong agreement between our replications of Keyes’ and Seligman et al.’s operationalizations (81%) and Diener et al. and Seligman et al.’s (80%). Even the least comparable operationalizations (Huppert and So and Seligman et al.) indicated moderate agreement (74%). In the absence of an established empirical benchmark stating what degree of agreement is meaningful, or indeed any criterion for interpreting what these levels of agreement mean, it is hard to draw any concrete conclusions from these findings. The strengths and unique contributions of this study include the application of the four operational definitions to a very large, nationally representative, sample of adults, which allows our results to be compared to other population samples; the prospective nature of the SWI, with two more longitudinal rounds scheduled over the next four years, allowing us to monitor the prevalence of flourishing among New Zealand adults over time using all four operationalizations; and the use of cross-tabulation and pairwise Cochrane’s Q tests allowing us to calculate, for the first time, the degree of agreement between the SWI replications of the different measures commonly employed to assess flourishing. In terms of limitations, we experienced challenges in accurately replicating three of the four operationalizations of flourishing using the available dataset (the FS was replicated exactly). While the SWI’s large number of wellbeing variables (n = 87) presented us with a compelling four models in our study, we are confident that the flourishing prevalence rates according to the four different models are comparable with each other in our study. While related samples Cochrane’s Q tests indicated all four operationalizations were significantly different to one another, cross tabulation analysis revealed a strong agreement between our replications of Keyes’ and Seligman et al.’s operationalizations (81%) and Diener et al. and Seligman et al.’s (80%). Even the least comparable operationalizations (Huppert and So and Seligman et al.) indicated moderate agreement (74%). In the absence of an established empirical benchmark stating what degree of agreement is meaningful, or indeed any criterion for interpreting what these levels of agreement mean, it is hard to draw any concrete conclusions from these findings. The strengths and unique contributions of this study include the application of the four operational definitions to a very large, nationally representative, sample of adults, which allows our results to be compared to other population samples; the prospective nature of the SWI, with two more longitudinal rounds scheduled over the next four years, allowing us to monitor the prevalence of flourishing among New Zealand adults over time using all four operationalizations; and the use of cross-tabulation and pairwise Cochrane’s Q tests allowing us to calculate, for the first time, the degree of agreement between the SWI replications of the different measures commonly employed to assess flourishing. In terms of limitations, we experienced challenges in accurately replicating three of the four operationalizations maju menggunakan dataset tersedia (FS adalah direplikasi persis). Sementara SWI jumlah variabel kesejahteraan (n = 87) disajikan kita dengan kesempatan menarik untuk membandingkan operationalizations ini, kami mengakui bahwa kecocokan itu tidak sempurna. Perbedaan dalam respon format dan kuesioner diperlukan kita membuat keputusan subjektif mengenai cara terbaik untuk mereplikasi model asli. Tantangannya adalah untuk tetap setia teori dan konseptualisasi model asli, namun juga tetap konsisten dalam metodologi kami di seluruh model. Kami menawarkan empat contoh berikut dari jenis tantangan kami menghadapi, dan metode kami untuk mengatasi mereka. Pertama, tidak adanya diagnosis setiap kategoris subur untuk berkembang skala atau PERMA-P diperlukan kita untuk merancang metode kita sendiri. Kita dibimbing oleh Keyes, dan Huppert dan Jadi, dalam metodologi kami. Ini berarti memilih ambang batas untuk berkembang di FS yang diperbolehkan dukungan dari sebagian besar, tapi tidak semua, dari skala delapan komponen (Partitur ≥ 48, kisaran 7-56, berarti responden harus rata-rata enam pada Skala Likert 7 titik). Menjadi dikategorikan sebagai berkembang di replikasi SWI peserta PERMA-P diperlukan untuk Skor di atas ambang batas pada dua dari tiga item dari setiap komponen, dan empat dari lima komponen secara keseluruhan. Sementara kita mengakui keterbatasan dalam pendekatan kami, dan mengakui PERMA-P research team’s preference for dashboard reporting, categorical diagnoses of flourishing provide vital information for decision makers. Secondly, the various items selected and response formats used in the SWI frequently differed from those in the original scales. For instance, while the response option for the MHC-SF measured the frequency with which respondents experienced each component over the past month, several items in the SWI asked respondents “how much of the time during the past week” or “how much of the time would you generally say…”. Where possible we used the same items as the original scale, but some could not be matched to an SWI variable (such as ‘social coherence’), which meant this component had to be excluded from our analysis. Others were matched, but not perfectly so, leaving us having to choose the item which came closest to representing the original construct. Some of these were far from ideal. For instance, the MHC-SF item for ‘social growth’ (“during the past month, how often did you feel our society is a good place, or is becoming a better place for all people?”) was operationalized using the reverse-scored SWI item “For most people in New Zealand life is getting worse rather than better”. Similarly, Keyes’ ‘social contribution’ item assesses respondents’ contribution at a societal level, while the SWI item has a greater focus on the individual. The MHC-SF’s ‘social integration’ item concerning belonging to a community could be interpreted to refer to any type of group or community, in contrast to the SWI item we were forced to use, which reflects respondents’ perceptions of people in their local area. In this sense we cannot claim to have replicated Keyes’ validated scale completely. The SWI items selected to match the PERMA-P were also not a perfect replication, but we were at least able to include three different items for each PERMA construct, allowing us to represent the original scale well in this regard. Despite these obvious limitations, we maintain that having such a large number of wellbeing variables in the SWI, a large representative sample, and the FS and ESS models represented in their entirety, made comparison of the four models a worthwhile exercise. Thirdly, the greatest single challenge involved the decision making around the selection of thresholds differentiating between participants endorsing a component of flourishing and those not endorsing a component. Recently published OECD guidelines on measuring

Results (Indonesian) 2:

Bagian kedua dari tulisan ini diperkirakan tingkat prevalensi berkembang di antara 10.009 orang dewasa New
Selandia, menurut ulangan dari masing-masing empat operationalizations sering digunakan
Berkembang diidentifikasi di bagian satu, menggunakan variabel SWI dan dataset. Hasil penelitian menunjukkan ada perbedaan substansial dalam tingkat prevalensi berkembang tergantung pada
operasionalisasi bekerja, dari 24% (Huppert & Jadi), untuk 39% (Keyes), 41% (Diener et al.), Dan
47% (Seligman et al .). Tingkat prevalensi rendah berkembang dari replikasi SWI dari Huppert
dan konseptualisasi So (24%) kemungkinan besar mencerminkan lebih ketat teoritis dan mereka
kriteria konseptual untuk berkembang: untuk dikategorikan sebagai berkembang peserta diwajibkan
mendukung satu item yang mewakili emosi positif (yang hanya 41% dari sampel lakukan), ditambah
tiga dari empat komponen 'fungsi positif', dan empat dari lima komponen
'karakteristik positif'; sehingga memungkinkan peserta untuk mencetak di bawah ambang batas hanya pada dua
dari sepuluh item. Sebaliknya, peserta bisa mencetak gol di bawah ambang batas pada enam dari 13
komponen dalam replikasi SWI dari operasionalisasi Keyes ', atau tujuh 15 item dalam SWI
replikasi Seligman operasionalisasi dkk., Dan masih dikategorikan sebagai berkembang. Hanya
membutuhkan skor rata-rata 48 dan di atas, penafsiran kita tentang Diener et al. Operasionalisasi
juga memungkinkan fleksibilitas yang lebih besar di seluruh komponen dari penafsiran kita tentang Huppert dan So
operasionalisasi. (Ini adalah perbedaan yang paling mencolok antara empat operationalizations,
dan penyebab variasi dalam tingkat prevalensi.) Penting untuk dicatat bahwa penggunaan yang berbeda
format respon dalam survei SWI berarti bahwa beberapa variasi dalam tingkat prevalensi antara
kami studi dan penelitian sebelumnya mungkin disebabkan karena penggunaan ambang batas yang berbeda, membuat
perbandingan internasional berpotensi tidak akurat. Misalnya, Selandia Baru 24% berkembang
sesuai dengan replikasi kami model Huppert dan Jadi mungkin tidak secara langsung sebanding dengan
Denmark '41% berkembang atau Portugal 10% berkembang didiagnosis menggunakan model yang sama (Huppert
& Jadi, 2013). Namun, dengan menerapkan metodologi yang konsisten untuk memilih ambang batas di semua empat model dalam penelitian kami, kami yakin bahwa tingkat prevalensi berkembang sesuai dengan
empat model yang berbeda sebanding dengan satu sama lain dalam penelitian kami.
Sementara sampel terkait Cochrane Q tes menunjukkan keempat operationalizations yang
berbeda secara signifikan satu sama lain, analisis tabulasi silang menunjukkan perjanjian yang kuat
antara ulangan kami Keyes 'dan operationalizations Seligman dkk. (81%) dan Diener et
al. dan Seligman dkk. (80%). Bahkan operationalizations sebanding setidaknya (Huppert dan Jadi
dan Seligman et al.) Menunjukkan perjanjian sedang (74%). Dengan tidak adanya mapan
patokan empiris yang menyatakan apa yang tingkat kesepakatan bermakna, atau memang ada kriteria
untuk menafsirkan apa tingkat ini perjanjian berarti, sulit untuk menarik kesimpulan yang konkret
dari temuan ini.
Kekuatan dan kontribusi yang unik dari penelitian ini meliputi penerapan empat
definisi operasional untuk sampel yang sangat besar, perwakilan nasional, orang dewasa, yang memungkinkan
hasil kami untuk dibandingkan dengan sampel populasi lain; sifat calon dari SWI, dengan
dua putaran membujur dijadwalkan selama empat tahun ke depan, memungkinkan kita untuk memantau
prevalensi berkembang di kalangan orang dewasa Selandia Baru dari waktu ke waktu menggunakan semua empat
operationalizations; dan penggunaan tabulasi silang dan berpasangan Cochrane Q tes memungkinkan kita
untuk menghitung, untuk pertama kalinya, tingkat kesepakatan antara ulangan SWI dari
langkah-langkah yang berbeda yang biasa digunakan untuk menilai berkembang.
Dalam hal keterbatasan, kita mengalami tantangan di akurat mereplikasi tiga dari empat
operationalizations dari berkembang menggunakan dataset yang tersedia (FS direplikasi persis).
Sementara sejumlah besar orang SWI tentang kesejahteraan variabel (n = 87) disajikan kita dengan menarik empat model dalam penelitian kami, kami yakin bahwa berkembang Angka prevalensi menurut
empat model yang berbeda sebanding dengan satu sama lain dalam penelitian kami.
Sementara sampel terkait Cochrane Q tes menunjukkan semua empat operationalizations yang
signifikan berbeda satu sama lain, analisis tabulasi silang menunjukkan perjanjian yang kuat
antara ulangan kami Keyes 'dan Seligman et al. operationalizations (81%) dan Diener et
al. dan Seligman dkk. (80%). Bahkan operationalizations sebanding setidaknya (Huppert dan Jadi
dan Seligman et al.) Menunjukkan perjanjian sedang (74%). Dengan tidak adanya mapan
patokan empiris yang menyatakan apa yang tingkat kesepakatan bermakna, atau memang ada kriteria
untuk menafsirkan apa tingkat ini perjanjian berarti, sulit untuk menarik kesimpulan yang konkret
dari temuan ini.
Kekuatan dan kontribusi yang unik dari penelitian ini meliputi penerapan empat
definisi operasional untuk sampel yang sangat besar, perwakilan nasional, orang dewasa, yang memungkinkan
hasil kami untuk dibandingkan dengan sampel populasi lain; sifat calon dari SWI, dengan
dua putaran membujur dijadwalkan selama empat tahun ke depan, memungkinkan kita untuk memantau
prevalensi berkembang di kalangan orang dewasa Selandia Baru dari waktu ke waktu menggunakan semua empat
operationalizations; dan penggunaan tabulasi silang dan berpasangan Cochrane Q tes memungkinkan kita
untuk menghitung, untuk pertama kalinya, tingkat kesepakatan antara ulangan SWI dari
langkah-langkah yang berbeda yang biasa digunakan untuk menilai berkembang.
Dalam hal keterbatasan, kita mengalami tantangan di akurat mereplikasi tiga dari empat
operationalizations dari berkembang menggunakan dataset yang tersedia (FS direplikasi persis).
Sementara sejumlah besar orang SWI tentang kesejahteraan variabel (n = 87) disajikan kita dengan kesempatan yang menarik untuk membandingkan operationalizations ini, kami mengakui bahwa fit itu tidak sempurna.
Perbedaan item kuesioner dan format respon yang dibutuhkan kita untuk membuat keputusan subjektif
mengenai cara terbaik untuk meniru model asli. Tantangannya adalah untuk tetap setia pada
teori dan konseptualisasi dari model asli, sementara juga tetap konsisten dalam metodologi kami di model. Kami menawarkan empat contoh berikut jenis-jenis tantangan yang kita
hadapi, dan metode untuk mengatasi mereka.
Pertama, tidak adanya diagnosis kategoris berkembang untuk Skala Berkembang atau
PERMA-P diperlukan kita untuk merancang metode kita sendiri. Kami dipandu oleh Keyes, dan Huppert dan
Jadi, dalam metodologi kami. Ini berarti memilih ambang batas untuk berkembang di FS yang memungkinkan
dukungan dari sebagian besar, tapi belum tentu semua, dari delapan komponen (skor skala yang ≥ 48, kisaran
7-56, yang berarti responden memiliki skor rata-rata enam pada 7-titik skala Likert). Untuk
dikategorikan sebagai berkembang dalam replikasi SWI dari PERMA-P yang dibutuhkan peserta untuk mencetak
di atas ambang batas pada dua dari tiga item dari masing-masing komponen, dan empat dari lima komponen
keseluruhan. Sementara kita mengakui keterbatasan dalam pendekatan kami, dan mengakui PERMA-
preferensi tim peneliti P untuk pelaporan dashboard, diagnosa kategoris berkembang
memberikan informasi penting bagi para pengambil keputusan.
Kedua, berbagai item yang dipilih dan format respon yang digunakan dalam SWI sering
berbeda dari orang-orang dalam skala yang asli. Misalnya, sedangkan pilihan respon untuk MHC-
SF mengukur frekuensi yang dialami responden setiap komponen selama masa
bulan, beberapa item di SWI bertanya responden "berapa banyak waktu selama seminggu yang lalu"
atau "berapa banyak waktu yang akan Anda umumnya mengatakan ... ". Bila memungkinkan kami menggunakan item yang sama
sebagai skala asli, tapi beberapa tidak bisa dicocokkan dengan variabel SWI (seperti 'sosial
koherensi'), yang berarti komponen ini harus dikeluarkan dari analisis kami. Lain yang
cocok, tapi tidak sempurna sehingga, meninggalkan kami harus memilih item yang datang paling dekat dengan
mewakili konstruk asli. Beberapa dari mereka jauh dari ideal. Misalnya, MHC-SF
barang untuk pertumbuhan sosial '("selama sebulan terakhir, seberapa sering kau merasa masyarakat kita adalah baik
tempat, atau menjadi tempat yang lebih baik bagi semua orang?") Telah dioperasionalkan dengan menggunakan reverse yang mencetak
SWI item "Bagi kebanyakan orang dalam hidup Selandia Baru semakin parah daripada yang lebih baik". Demikian pula,
Keyes '' kontribusi sosial 'item menilai kontribusi responden di tingkat masyarakat, sedangkan
barang SWI memiliki fokus yang lebih besar pada individu. MHC-SF barang 'integrasi sosial'
tentang milik masyarakat bisa ditafsirkan untuk merujuk kepada semua jenis kelompok atau
masyarakat, berbeda dengan item SWI kami terpaksa menggunakan, yang mencerminkan responden
persepsi orang di daerah mereka . Dalam hal ini kita tidak bisa mengklaim telah direplikasi Keyes
'skala divalidasi sepenuhnya. The SWI item yang dipilih untuk mencocokkan PERMA-P yang juga tidak sempurna
replikasi, tapi kami setidaknya mampu mencakup tiga item yang berbeda untuk masing-masing konstruk PERMA,
memungkinkan kita untuk mewakili skala asli baik dalam hal ini. Meskipun ini keterbatasan jelas,
kami mempertahankan bahwa memiliki seperti sejumlah besar kesejahteraan variabel dalam SWI, besar
sampel yang representatif, dan FS dan ESS model diwakili secara keseluruhan, membuat
perbandingan dari empat model latihan yang berharga.
Ketiga, yang terbesar Tantangan tunggal melibatkan pengambilan keputusan di sekitar pemilihan
ambang batas membedakan antara peserta mendukung komponen berkembang dan mereka
tidak mendukung komponen. Baru-baru ini menerbitkan pedoman OECD pada pengukuran