Perbedaan Tujuan SEM Kovarians dan SEM PLS

Sumber Kutipan:
Jorg Henseler, Christian M. Ringle and Rudolf R. Sinkovics (2009),
The Use Of Partial Least Squares Path Modeling In International Marketing,
New Challenges to International Marketing Advances in International Marketing, Volume 20,
277–319

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According to Jo¨reskog (1982, p. 270) ‘‘ML is theory-oriented, and emphasizes the transition from exploratory to confirmatory analysis. PLS is primarily intended for causal-predictive analysis in situations of high complexity but low theoretical information.’’ The philosophical distinction between these approaches is whether to use CBSEM for theory testing and development, or PLS path modeling for predictive applications. ..., in causal modeling situations where prior theory is strong and further testing and development is the goal, CBSEM is the most appropriate statistical methodology. Yet, due to the indeterminacy of factor score estimations, there is a loss of predictive accuracy. This occurrence, of course, is not of concern in theory testing where structural relationships (i.e. parameter estimation) between concepts is of primary concern. Moreover, hypothesis building and the assessment of CBSEM results through global goodness-of-fit
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Menurut Joreskog (1982), Maksimul Likelihood atau taksiran model dalam SEM Kovarians berorientasi teori dan menkankan pada studi konfirmatori atau transisi studi eksplorasi ke konfirmasi. Studi PLS bertujuan pada analisis kausal prediksi dalam  situasi model yang kompleks akan tetapi memiliki informasi teori yang kurang/lemah/pengembangan teori. Filosofi mendasar perbedaan kedua analisis tersebuty adalah bahwa SEM Kovarians berguna untuk pengujian teori dan pengembangan sedangkan PLS untuk model prediksi. Ketika peneliti mempunyai rujukan teori yang mendasari pembentukan model yang kuat dan tujuannya adalah pengujian teori/ model pengembangan maka SEM Kovarians lebih baik digunakan.  Akan tetapi untuk menghindari permasalahan estimasi "indeterminacy of factor score" dan kehilangan akurasi predikasi maka lebih dianjurkan analisis SEM PLS.

Salam,
Sofyan Yamin
@2017, Bogor

Ukuran Sampel dalam SEM PLS

Tulisan berasal dari:
Jorg Henseler, Christian M. Ringle and Rudolf R. Sinkovics (2009),
The Use Of Partial Least Squares Path Modeling In International Marketing, 

New Challenges to International Marketing Advances in International Marketing, Volume 20, 277–319

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The sample size argument has its roots in the considerable obstacles faced when conducting CBSEM with small samples. A substantial number of simulation studies on CBSEM compare alternative discrepancy functions and their estimation bias, accuracy, and robustness with respect to sample size. Boomsma and Hoogland (2001), for example, conclude that there are nonconvergence problems and improper CBSEM solutions in small samples (e.g., 200 or fewer cases). These authors provide evidence that CBSEM – depending on the selected discrepancy function and the model complexity – requires several hundred or even thousands of observations.

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SEM Kovarians menghadapi masalah jika menggunakan ukuran sampel yang kecil. Hasil studi yang dilakukan oleh Boomsma dan Hoogland (2001) terhadap penggunaan sampel kecil dalam SEM Kovarians dengan ukuran "bias estimasi, akurasi, kerobust-an model" menemukan bahwa model SEM Kovarians dengan sampel kecil akan memperoleh permasalahan model tidak konvergen (solusi taksiran parameter model tidak tercapai dan taksiran paramter model tidak tepat). Dengan meningkatnya kompleksitas model maka diperlukan ukuran sampel data yang lebih tinggi (lebih banyak).

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In contrast, the sample size can be considerably smaller in PLS path modeling. For example, ‘‘there can be more variables than observations and there may be a small amount of data that are missing completely at random’’ (Tenenhaus et al., 2005, p. 202). Wold (1989) illustrates the low sample size requirement by analyzing a path model based on a data set consisting of 10 observations and 27 manifest variables. A rule of thumb for robust PLS path modeling estimations suggests that the sample size be equal to the larger of the following (Barclay, Higgins, & Thompson, 1995): (1) ten times the number of indicators of the scale with the largest number of formative indicators, or (2) ten times the largest number of structural paths directed at a particular construct in the inner path model. Chin and Newsted (1999) present a Monte Carlo simulation study on PLS with small samples. They find that the PLS path modeling approach can provide information about the appropriateness of indicators at sample size as low as 20. This study confirms the consistency at large on loading estimates with increased numbers of observations and numbers of manifest variables per measure-ment model.

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Model SEM PLS dapat bekerja dengan sampel yang kecil. Akan tetapi  untuk memperoleh model SEM PLS yang robust maka ukuran sample yang dibutuhkan dalam SEM PLS adalah (1) 10 kali jumlah indikator dalam model pengukuran formatif, (2) 10 kali jumlah model struktural (pengaruh langsung antara variabel laten). Hasil studi yang dilakukan oleh Chin melalui permodelan Monte Carlo menunjukan model SEM PLS membutuhkan minimal 20 pengamatan. Hasil studinya mengkonfirmasi konsistensi pada ukuran sampel besar.

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Salam,

Sofyan Yamin

@2017 Desember, Bogor

Model Pengukuran Dan Model Struktural dalam SEM PLS

Tulisan berasal dari:
Jorg Henseler, Christian M. Ringle and Rudolf R. Sinkovics (2009),
The Use Of Partial Least Squares Path Modeling In International Marketing, New Challenges to International Marketing Advances in International Marketing, Volume 20, 277–319

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PLS path models are formally defined by two sets of linear equations: the inner model and the outer model. The inner model specifies the relationships between unobserved or latent variables, whereas the outer model specifies the relationships between a latent variable and its observed or manifest variables. The various literatures do not always employ the same terminology. For instance, publications addressing CBSEM (e.g., Rigdon, 1998) often refer to structural models and measurement models or (observed) indicator variables; whereas those focusing on PLS path modeling (e.g., Lohmo¨ller, 1989) use the terms inner model and outer model or manifest variables for similar elements of the causal model. 

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Model SEM PLS secara formal terdiri atas 2 set persamaan linier yaitu inner model dan outer model. Inner model menspesifikasikan hubungan antara variabel laten dengan variable manifest atau indikator sedangkan model outer menspesifikasikan hubungan antara variabel laten dengan variabe laten lainnya. Beberapa literatur tidak menggunakan terminologi inner model dan outer model akan tetapi model pengukuran dan model struktural. Akan tetapi dalam model PLS ini disebutkan inner model yang mempunyai makna sama dengan model struktural dan outer model yang sama dengan model struktural.

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Structural equation models usually involve latent variables with multiple indicators. The measurement model or outer model specifies the relationship between indicators and latent variables. The direction of path relationships per measurement model and, thus, the causality between the latent variable and its indicators are either described by a reflective or a formative mode. The reflective measurement model has its roots in classical test theory and psychometrics (Nunnally & Bernstein, 1994). Each indicator represents an error-afflicted measurement of the latent variable. The direction of causality is from the construct to the indicators; thus, observed measures are assumed to reflect variation in the latent variable. In other words, changes in the construct are expected to be manifested in changes in all of its indicators

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Model pengukuran ada 2 (dua) jenis yaitu model pengukuran reflektif dan formatif. Model pengukuran reflektif berakar dari teori pengukuran klasik. Setiap indikator mewakili pengukuran variabel laten. Arah kausalitas mengalir dari variable laten/ konstrak ke indikator pengukur. indikator atau indikator pengukur diasumsikan menggambarkan varians dalam variabe laten. Setiap perubahan dalam variabe laten maka diharapkan akan memberikan perubahan pada indikatornya.

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Although the inclusion of formative measures in CBSEM has been well documented (e.g., Jo¨reskog & Goldberger, 1975; MacCallum & Browne, 1993; Jo¨reskog & Sorbom, 1996), analysts usually encounter identification problems. As a sort of ad-hoc remedy, formative indicators could be modeled in CBSEM by re-specifying the formative indicators as exogenous latent variables with single indicators, fixed unit loadings, and a fixed measurement error (Williams, Edwards, & Vandenberg, 2003). In contrast, similar problems do not arise in PLS path modeling. The PLS path modeling algorithm – is equally well suited for SEM with reflective and/or formative measurement models. The only problematic issue, however, is connected to manifest variables’ critical level of multicollinearity in formative measurement models.

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Model pengukuran formatif dalam SEM Kovarians menghadapi permasalahan identifikasi. Meskipun beberapa referensi para ahli model pengukuran formatif masih dapat dilakukan dalam SEM Kovarians dengan menspesifikasikan ulang indikator formative sebagai variabel laten tunggal, memberikan bobot tetap pada nilai loadingnya (fixed unit loading) dan error pengukuran tetap. Problem ini tidak terjadi dalam SEM PLS. Algoritma dalam SEM PLS mengakomodasi untuk model pengukuran reflektif dan formatif. hanya saja permasalahan model formatif dalam SEM PLS adalah terkait multikolinieritas.

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PLS path modeling does not provide any global goodness-of-fit criterion. As a consequence, Chin (1998) has put forward a catalog of criteria to assess partial model structures. A systematic application of these criteria is a two-step process, encompassing (1) the assessment of the outer model and (2) the assessment of the inner model. At the beginning of the two step process, model assessment focuses on the measurement models. A systematic evaluation of PLS estimates reveals the measurement reliability and validity according to certain criteria that are associated with formatve and reflective outer model. It only makes sense to evaluate the inner path model estimates when the calculated latent variable scores show evidence of sufficient reliability and validity.
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Model SEM PLS tidakempunyai kecocokan model secara menyeluruh (goodness of fit). sebagai akibatnya maka Chin menyarankan  untuk mengevaluasi model PLS dalam 2 (dua) tahap: 1) menguji atau evaluasi model pengukuran dan 2) evaluasi model struktural. Untuk model pengukuran memberitahukan informasi reliabilitas dan validitas sesuai dengan kriteria tertentu baik pengukuran reflektif atau formatif.
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Reflective measurement models should be assessed with regard to their reliability and validity. Usually, the first criterion which is checked is internal consistency reliability. The traditional criterion for internal consistency is Cronbach’s a (Cronbach, 1951), which provides an estimate for the reliability based on the indicator intercorrelations. While Cronbach’s a assumes that all indicators are equally reliable, PLS prioritizes indicators according to their reliability, resulting in a more reliable composite. As Cronbach’s a tends to provide a severe underestimation of the internal consistency reliability of latent variables in PLS path models, it is more appropriate to apply a different measure, the composite reliability rc (Werts, Linn, & Jo¨reskog, 1974). The composite reliability takes into account that indicators have different loadings, and can be interpreted in the same way as Cronbach’s a. No matter which particular reliability coefficient is used, an internal consistency reliability value above 0.7 in early stages of research and values above 0.8 or 0.9 in more advanced stages of research are regarded as satisfactory (Nunnally & Bernstein, 1994), whereas a value below 0.6 indicates a lack of reliability.
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Untuk model pengukuran reflektif maka memerlukan pemeriksaan reliabilitas dan validitas. Kriteria pertama adalah  reliabilitas konsistensi internal. metode umumnya yang digunakan adalah Cronbach's Alpha. Ukuran ini mengasumsikan bahwa semua indikator mempunyai tingkat keandalan/reliable/bobot yang sama sehingga menghasilkan taksiran reliabilitas yang yang kurang tepat. Oleh karena itu maka disarankan mempunytai ukuran yang lainnya adalah composite reliability (CR) yang dikembangkan oleh (Werts. Linn. & Joreskog, 1974). CR mengukur tingkat reliabilitas berdasarkan loadings yang berbeda dan dapat diinterpretasikan sama seperti Cronbach's Alpha. Nilai CR yang diharapkan adalah lebih dari 0,70 untuk studi  awal atau 0.80 dan 0.90 untuk studi lanjutan. Sedangkan nilai CR dibawah 0.60 menunjukan tingkat reliabilitas yang lemah.
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As the reliability of indicators varies, the reliability of each indicator should be assessed. Researchers postulate that a latent variable should explain a substantial part of each indicator’s variance (usually at least 50%). Accordingly, the absolute correlations between a construct and each of its manifest variables (i.e. the absolute standardized outer loadings) should be p higher than 0.7 ( 0.5). Moreover, some psychometrists (e.g., Churchill, 1979) recommend eliminating reflective indicators from measurement models if their outer standardized loadings are smaller than 0.4. Taking into account PLS’ characteristic of consistency at large, one should be careful when eliminating indicators. Only if an indicator’s reliability is low and eliminating this indicator goes along with a substantial increase of composite reliability, it makes sense to discard this indicator.
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Pemeriksaan reliabilitas setiap indikator perlu dilakukan dengan melihat standardized loadings. Nilai minimal yang diharapkan adalah 0.70 atau 0.50. saran dari Churchill (1979) adalah nilai loading kurang dari 0.40 dihlangkan dalam model pengukuran dengan ketentuan adalah menghilangkan sebuah indikator yang rendah dan  meningkatkan secara substansi nilai komposit reliabilitas.

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For the assessment of validity, two validity subtypes are usually examined: the convergent validity and the discriminant validity. Convergent validity signifies that a set of indicators represents one and the same underlying construct, which can be demonstrated through their unidimensionality. Fornell and Larcker (1981) suggest using the average variance extracted (AVE) as a criterion of convergent validity. An AVE value of at least 0.5 indicates sufficient convergent validity, meaning that a latent variable is able to explain more than half of the variance of its indicators on average (e.g., Go¨tz, Liehr-Gobbers, & Krafft, 2009). Discriminant validity is a rather complementary concept: Two conceptually different concepts should exhibit sufficient difference (i.e. the joint set of indicators is expected not to be unidimensional). In PLS path modeling, two measures of discriminant validity have been put forward: The Fornell–Larcker criterion and the cross-loadings. The Fornell–Larcker criterion (Fornell & Larcker, 1981) postulates that a latent variable shares more variance with its assigned indicators than with any other latent variable
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Konvergen validitas menjelaskan sejumlah indikator mengukur konsep atau variable yang sama (variable laten). Validitas konvergen terlihat dari tingginya korelasi antara indikator dalam mengukur variable laten tersebut. Konvergen validity terlihat dari nilai AVE dengan nilai minimum yang diharapkan adalah 0.50 yang berarti variable laten mampu menjelaskan 50% atau setengah variasi indikatornya secara rata-rata. Ukuran validitas lainnya yaitu validitas discriminan yang merupakan pelengkap dari validitas konvergen. Ukuran ini ditunjukan oleh rendahnya korelasi antara indikator yang mengukur variable laten yang berbeda, Menurut Fornelll-Lacker kriteria yang digunakan adalah cross loading.    

Salam,

Sofyan Yamin

1812 1825 2356

@ Bogor Desember 2017 

Mengevaluasi Model Pengukuran Formatif dalam SEM PLS

Sumber:
Marko Sarstedt, Christian M. Ringle, Donna Smith, Russell Reams, Joseph F. Hair (2014),
Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers, Journal of Family Business Strategy, Elsevier.


Based on the t-values, the significance of the weights may be determined to make the following decisions: If the weight is statistically significant, the indicator is retained.  If the weight is non-significant but the indicator’s loading is 0.50 or higher, the indicator is still retained, provided that theory and expert judgement support its inclusion. If the weight is non-significant and the loading is low (i.e., below 0.50), the indicator should be deleted from the measurement  model.
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Ada 2 (dua) hal penting mengevaluasi kualitas model pengukuran formative yaitu signifikannya  nilai weight indikator pengukur dan collinearity. Nilai signifikan weight dapat dilihat dari nilai t statistik dimana rule of thumb nilai t statistik diatas 1,96 (signifikan).  Meskipun nilai weight untuk suatu indikator pengukur tidak signifikan dan nilai weight tersebut diatas 0,50 maka indikator tersebut tetap dipertahankan dengan dukungan ekspert/ ahli dan pendekatan teori. Jika nilai weight tidak signifikan dan nilai loading indikator tersebut kurang dari 0.50 maka sebaiknya dihilangkan dari model pengukuran.

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To assess the level of collinearity among the formative indicators, the researcher should compute each item’s variance inflation factor (VIF). For this purpose, the researcher must run a multiple regression of each indicator of the formatively measured construct on all the other measurement items of the same construct.
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Untuk menguji kolinearitas antara indikator dalam model pengukuran formative maka dapat dilakukan dengan melihat nilai VIF. Caranya adalah peneliti melakukan  analisis regresi berganda untuk setiap indikator tersebut.

Kesmpilan:

Evaluasi model pengukuran formative adalah dengan melihat signifikansi weight dari indikator dan kolinearitas antara indikator yang menyusun atau membentuk model pengukuran formative.

Salam,

Sofyan Yamin

0812 1825 2356

@ Bogor Desember 2017

Mengapa Harus Analisis SEM PLS

Sumber:
Jorg Henseler, Christian M. Ringle and Rudolf R. Sinkovics (2009),
The Use Of Partial Least Squares Path Modeling In International Marketing, New Challenges to International Marketing, Advances in International Marketing, Volume 20, 277–319
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Many researchers argue that the goal of their studies is in line with particular strengths of PLS path modeling. The most important motivations are exploration and prediction, as PLS path modeling is recommended in an early stage of theoretical development in order to test and validate exploratory models. Another powerful feature of PLS path modeling is that it is suitable for prediction-oriented research. Thereby, the methodology assists researchers who focus on the explanation of endogenous constructs.
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Motivasi penggunaan analisis PLS adalah studi eksplorasi dan prediksi. SEM PLS juga direkomendasikan sebagai analisis yang dapat digunakan untuk model pengembangan teori tahap awal sebagai analisis untuk pengujian atau model validasi eksplorasi. Analisis ini memfokuskan pada penjelasan varians variable laten endogen.

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The characteristics of PLS path modeling, which researchers regard as relevant for their studies on international marketing, can be summarized as follows:

1. PLS delivers latent variable scores, i.e. proxies of the constructs, which are measured by one or several indicators (manifest variables).
2. PLS path modeling avoids small sample size problems and can therefore be applied in some situations when other methods cannot.
3. PLS path modeling can estimate very complex models with many latent and manifest variables.
4. PLS path modeling has less stringent assumptions about the distribution of variables and error terms.
5. PLS can handle both reflective and formative measurement models.

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Adapun karakterisrik model SEM PLS adalah:

1. SEM PLS menghasilkan skor variable laten. skor ini merupakan proksi skor variable laten yang dapat diukur oleh 1 atau beberapa indikator pengukur. 
2. SEM PLS dapat digunakan untuk ukuran sample penelitian yang terbatas/ kecil
3. SEM PLS dapat bekerja untuk model kompleks dengan bantak variabel laten dan  indikator
4. SEM PLS mempunyai  asumsi yang lebih longgar dari distribusi data dan distribusi variable error
5. SEM PLS dapat menangani model pengukuran reflektif dan formatif.

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Salam,
Sofyan Yamin
@ Desember 2017
di Bogor.

Perbedaan SEM Kovarians dan SEM PLS

Tulisan ini berasal dari :
Joe F. Hair, Christian M. Ringle, and Marko Sarstedt (2011), Journal of Marketing Theory and Practice, vol. 19, no. 2 (spring 2011), pp. 139–151.

dengan Judul:  PLS-SEM: Indeed a Silver Bullet

The philosophical distinction between CB‑SEM and PLS‑SEM is straightforward. If the research objective is theory testing and confirmation, then the appropriate method is CB‑SEM. In contrast, if the research objective is prediction and theory development, then the appropriate method is PLS‑SEM. Conceptually and practically, PLS‑SEM is similar to using multiple regression analysis. The primary objective is to maximize explained variance in the dependent constructs but additionally to evaluate the data quality on the basis of measurement model characteristics. 

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Intinya: Perbedaan SEM Kovarians dan SEM PLS sangat jelas. jika tujuan penelitian adalah pengujian teori atau konfirmasi model berdasarkan teori yang kuat maka sebaiknya analisis statistik yang digunakan  dengan pendekatan SEM Kovarians alias dengan software Lisrel/Amos. akan tetapi bila tujuan penelitian adalah prediksi atau pengembangan teori atau studi eksplorasi maka tepat menggunakan SEM PLS. secara konsep SEM PLS seperti menggunakan analisis regresi berganda. tujuannya adalah memaksimumkan varians variabel laten dependen akan tetapi tambahannya dalam SEM PLS adalah mengevaluasi kualitas model pengukuran.

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Given PLS‑SEM’s ability to work efficiently with a much wider range of sample sizes and increased model complexity, and its less restrictive assumptions about the data, it can address a broader range of problems than CB‑SEM. Moreover, because the constructs’ measurement properties are less restrictive with PLS‑SEM, constructs with fewer items (e.g., one or two) can be used than those that CB‑SEM requires. Overall, when measurement or model properties restrict the use of CB-SEM or when the emphasis is more on exploration than confirmation, PLS‑SEM is an attractive alternative to

CB‑SEM and often more appropriate.

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SEM PLS lebih fleksible dalam asumsi dibandingkan dengan SEM Kovarians seperti dapat bekerja dengan ukuran sampel yang fleksible dan model yang kompleks. seringkali permasalahan tersebut terjadi dalam SEM kovarians. Selain itu keunggulan PLS SEM adalah dapat menggunakan indikator pengukuran lebih sedikit misal 1 atau 2 indikator. Secara keseluruhan jika asumsi SEM Kovarians tidak dapat terpenuhi maka alternatif menggunakan SEM PLS lebih dianjurkan.
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Kesimpulan:

1. SEM PLS dan SEM Kovarians adalah analisis statistik yang saling melengkapi
2. SEM PLS digunakan jika pemenuhan ukuran sample serta model yang kompleks yang seringkali dalam SEM Kovarians mengalami masalah dalam taksiran model
3. SEM PLS dapat menggunakan indikator pengukuran 1 atau 2. yang berarti taksiran parameter model dengan indikator pengukur sedikit tidak masalah dalam SEM PLS
4. Analisis SEM PLS seperti halnya analisis regresi berganda dengan tujuan yang sama hanya dalam SEM PLS menambahkan evaluasi model pengukuran.

Salam,
Sofyan Yamin
@Bandung Desember 2017
0812 1823 2356

Asumsi SEM Kovarians - LISREL

Tulisan ini berasal dari :
Joe F. Hair, Christian M. Ringle, and Marko Sarstedt (2011), Journal of Marketing Theory and Practice, vol. 19, no. 2 (spring 2011), pp. 139–151.

dengan Judul:  PLS-SEM: Indeed a Silver Bullet

CB‑SEM develops a theoretical covariance matrix based on a specified set of structural equations. The technique focuses on estimating a set of model parameters in such a way that the difference between the theoretical covariance matrix and the estimated covariance matrix is minimized (e.g., Rigdon 1998). The CB‑SEM model estimation requires a set of assumptions to be fulfilled, including the multivariate normality of data, minimum sample size, and so forth (e.g., Diamantopoulos and Siguaw 2000).

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Analisis SEM Kovarians (Lisrel, AMOS, dan lainnya) didasarkan pada matrik kovarians yang disusun dari model persamaan yang dihipotesiskan oleh peneliti. Teknis analisisnya memfokuskan pada estimasi sejumlah parameter model dengan suatu cara tertentu sehingga menghasilkan matrik kovarians taksiran yang meminimumkan antara matrik kovarians model teori dengan matrik kovarians hasil taksiran. Analisis SEM kovarians membutuhkan sejumlah asumsi yang harus dipenuhi yaitu data berdistribusi normal multivariat, minimum ukuran sampel dan lainnya.

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But if CB‑SEM assumptions cannot be met, or the research objective is prediction rather than confirmation of structural relationships, then variance-based PLS‑SEM is the preferred method. In comparison with CB‑SEM results, which can be highly imprecise when the assumptions are violated, PLS‑SEM often provides more robust estimations of the structural model (e.g., Lohmöller 1989; Reinartz, Haenlein, and Henseler 2009; Ringle et al. 2009; Wold 1982).  

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Akan tetapi bila  asumsi SEM Kovarians tidak dapat terpenuhi  atau tujuan penelitian bergeser menjadi prediksi dibandingkan konfirmasi hubungan struktural antara variable laten maka analisis SEM PLS dapat menjadi alternatif yang dapat digunakan.  Bila menggunakan analisis SEM Kovarians yang tentu akan menghasilkan taksiran model yang "kurang tepat" maka alternatif penggunaan SEM PLS lebih baik digunakan. Selain itu SEM PLS menghasilkan taksiran parameter yang lebih "robust" dalam persamaan model.

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Kesimpulan:

1. Asumsi SEM Kovarians (Lisrel, Amos) adalah data berdistribusi normal multivariat dan ada ketentuan jumlah sampel minimal.
2. Apabila aumsi SEM kovarians tidak terpenuhi dan tujuan penelitian bergeser menjadi prediksi dibandingkan konfirmasi maka analisis SEM PLS dapat dilakukan.
3. SEM PLS tetap menghasilkan taksiran parameter yang "robust" meskipun data tidak berdistribusi normal atau jumlah sample terbatas.

Sofyan Yamin

0812 1825 2356

ditulis di Bandung

Desember 2017