Re: [問題] SEM用PLS跑的問題與優缺點?
: 不知道是否有其他比較強(多)的證據或文獻可以證明 resample size 設越大越好?
:
: 以下引用 chin(2001)的 PLS-Graph User's Guide 內容片段
: (已有 MIS 領域不錯的 Journal paper 引用)
: The default Bootstrap options are 100 resamples with each sample consisting of the same number of
: cases as your original sample set. The bootstrap procedure samples with replacement from your
: original sample set. It continues to sample until it reaches the number of cases you specify (or the
: default). This procedure is repeated until it reaches the number of bootstrap resamples you specify (or
: the default of 100). In general, resamples of 200 tend to provide reasonable standard error estimates.
:
: 以下是快速找到的幾篇 MIS papers
: Resample = 100
: Henry, R.M., McCray, G.E., Purvis, R.L. Roberts, T.L. (2007) "Exploiting Organizational Knowledge in Developing IS Project Cost and Schedule Estimates: An Empirical Study", Information & Management, Vol. 44 No.6, pp.598-612.
:
: Resample = 500
: Ko, D., Kirsch, J.L., King, W.R. (2005) "Antecedents of knowledge transfer from consultants to clients in enterprise system implementations", MIS Quarterly, Vol. 29 No.1, pp.59-85.
:
: Resample = 100 & 500
: Goodhue, D., Lewis, W., and Thompson, R., (2007) "Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS With Product Indicators", Information Systems Research, Vol. 18 No.2, pp.211-227.
:
: 也許 Goodhue et al.(2007) 這篇是答案, 但我找不到 pdf 檔可以看(汗)
: → bmka:這個問題沒那麼複雜吧,先把bootstrap方法原理弄懂 05/12 23:01
: → bmka:resample 數目當然越大越好,至於要多大,那要看data distribut 05/12 23:03
: → bmka:跑久一點不會吃虧的 05/12 23:04
對於我來說 PLS 只是一個工具而已
我只要知道如何使用及瞭解它的假設及限制, 而能產出 outcome 並解讀就可以了
如同您會操作電腦, 但您知道半導體是如何製造的嗎? 畢竟電腦只是一個工具而已
也許您只是站在純數學的觀點來看, 認為 resample 設越大越好
但這樣反而太過操弄統計這個工具了, 這樣統計的結果真的就是事實的結果嗎?
如果您可以提供文獻證明 resample 設越大越好, 那我也可以修正我原來的看法.
若如您所言, 對於 resample 設越大越好, 我一個合理的懷疑
那麼這許多作研究的學者應該會有人提到這點, 但是並沒有 ...
至少我看過的 papers 沒人提到此點
而且我相信這些學者的電腦應該不會太差, resample 設100萬也不是問題才對
所以我認為這並不是電腦執行速度的問題
我後來還是找到了 Goodhue et al.(2007) 這篇 pdf 檔 (ISR 在 MIS 排前五大期刊)
也許底下的片段可以解答您的問題, 所以我的建議還是設 500 比較恰當
因為這是大多數學者所使用的數值
It might be suggested that we should use bootstrapping
with 500 resamples (rather than 100). Five hundred
resamples is the usual recommendation when
using bootstrapping to estimate a parameter using a
single sample (Chin 1998). However, we draw 500
samples (500 researchers) from the same population
for each cell in our analysis, and use bootstrapping
with 100 resamples on each of those. This amounts to
50,000 resamples for each cell, and hence we expect
that moving from 50,000 to 250,000 resamples in each
cell would not affect the outcome.
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※ 編輯: danny789 來自: 122.254.33.185 (05/14 16:56)
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