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	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">JSCIM</journal-id>
			<journal-title-group>
				<journal-title>Journal of Sasang Constitution and Immune Medicine</journal-title>
				<abbrev-journal-title>J Sasang Constitut &amp; Immuno Med</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="epub">2287-786X</issn>
			<publisher>
				<publisher-name>The Society of Sasang Constitution and Immune Medicine</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="publisher-id">HSSSBH_2026_v38n1_14</article-id>
			<article-id pub-id-type="doi">10.7730/JSCM.2026.38.1.14</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Original Article</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>A study on the Development of Discriminant Function and Reliability Analysis for a Short-Form Sasang Constitution Questionnaire for Patient(SSCQ-P short form 40)</article-title>
				<trans-title-group xml:lang="ko">
					<trans-title>사상체질진단 설문검사(SSCQ-P short form 40)의 판별함수 개발 및 신뢰도 분석 연구</trans-title>
				</trans-title-group>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<name-alternatives>
						<name name-style="western" xml:lang="en">
							<surname>Jeon</surname>
							<given-names>Soo Hyung</given-names>
						</name>
						<name name-style="eastern" xml:lang="ko">
							<surname>전</surname>
							<given-names>수형</given-names>
						</name>
					</name-alternatives>
					<xref ref-type="aff" rid="A1">1</xref>
				</contrib>
				<contrib contrib-type="author" corresp="yes">
					<name-alternatives>
						<name name-style="western" xml:lang="en">
							<surname>Kim</surname>
							<given-names>Jong Won</given-names>
						</name>
						<name name-style="eastern" xml:lang="ko">
							<surname>김</surname>
							<given-names>종원</given-names>
						</name>
					</name-alternatives>
					<xref ref-type="aff" rid="A1">1</xref>
				</contrib>
			</contrib-group>
			<aff-alternatives id="A1">
				<aff xml:lang="en">
					Dept. of Sasang Constitutional Medicine, College of Korean Medicine, Dong Eui Univ.
					<label>1</label>
				</aff>
				<aff xml:lang="ko">
					동의대학교 한의과대학 사상체질과
					<label>1</label>
				</aff>
			</aff-alternatives>
			<author-notes>
				<corresp id="cor1">
					Jong Won Kim 
					<label>*</label>
					<email>jwonkim@deu.ac.kr</email>
					 Dept. of Sasang Constitutional Medicine, Dongeui University Korean Medical Hospital, 62 Yangjeong-ro, Busanjin-gu, Busan, Republic of Korea. Tel: +82-51-850-8640; Fax: +82-51-850-8744
				</corresp>
			</author-notes>
			<history>
				<date date-type="received">
					<day>13</day>
					<month>01</month>
					<year>2026</year>
				</date>
				<date date-type="revised">
					<day>15</day>
					<month>01</month>
					<year>2026</year>
				</date>
				<date date-type="accepted">
					<day>28</day>
					<month>01</month>
					<year>2026</year>
				</date>
			</history>
			<pub-date pub-type="ppub">
				<day>31</day>
				<month>03</month>
				<year>2026</year>
			</pub-date>
			<volume>38</volume>
			<issue>1</issue>
			<fpage>14</fpage>
			<lpage>30</lpage>
			<permissions>
				<copyright-statement>© 2026 The Society of Sasang Constitution and Immune Medicine. All rights reserved.</copyright-statement>
				<copyright-year>2026</copyright-year>
				<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">
					<license-p>This is an open access article distributed under the terms of the Creative Commons attribution Non-commercial License (http://creativecommons.org/licenses/by-nc/3.0/).</license-p>
				</license>
			</permissions>
			<abstract>
				<sec>
					<title>Objectives</title>
					<p>The purpose of this study was to develop a discriminant function for the SSCQ-P short form using new data and to investigate whether the number of items could be further reduced through reliability analysis.</p>
				</sec>
				<sec>
					<title>Methods</title>
					<p>The analysis included survey data from 282 patients who visited an outpatient clinic at a Korean medicine hospital between February 2023 and December 2024. Linear discriminant analysis was performed using all 40 items to obtain the discriminant function coefficients and calculate accuracy of the discriminant function. To verify the accuracy of SSCQ-P short form, the total dataset(n=282) was divided into training data and test data in a ratio of 3:1 and model evaluation was performed. To evaluate the internal consistency of the items by constitution, item analysis was conducted using Cronbach's alpha. To reduce the number of questions, a stepwise variable selection method was used.</p>
				</sec>
				<sec>
					<title>Results</title>
					<p>The accuracy of the discriminant function for all participants was 84.4%. The accuracy of the discriminant function for training was 86.9% and the accuracy of the discriminant function for test was 78.6%. Fifteen items were selected using the stepwise variable selection method, and the accuracy was 79.4%.</p>
				</sec>
				<sec>
					<title>Conclusion</title>
					<p>As a result of item analysis using cronbach's alpha, the number of items related to the Taeyangin was reduced from 10 to 6, and the number of items related to the Soyangin was reduced from 10 to 9.</p>
				</sec>
			</abstract>
			<trans-abstract xml:lang="ko"/>
			<kwd-group kwd-group-type="author" xml:lang="en">
				<kwd>Discriminant Function</kwd>
				<kwd>Reliability Analysis</kwd>
				<kwd>Cronbach's Alpha</kwd>
				<kwd>Sasang Constitution</kwd>
				<kwd>SSCQ-P</kwd>
			</kwd-group>
			<custom-meta-group>
				<custom-meta>
					<meta-name>publisher</meta-name>
					<meta-value>Jun-Hee Lee</meta-value>
				</custom-meta>
				<custom-meta>
					<meta-name>editor-in-chief</meta-name>
					<meta-value>Jong-Won Kim</meta-value>
				</custom-meta>
				<custom-meta>
					<meta-name>published-by</meta-name>
					<meta-value>The Society of Sasang Constitution and Immune Medicine</meta-value>
				</custom-meta>
				<custom-meta>
					<meta-name>journal-url</meta-name>
					<meta-value>https://journal.kci.go.kr/JSCIM</meta-value>
				</custom-meta>
			</custom-meta-group>
		</article-meta>
	</front>
	<body>
		<sec sec-type="intro">
			<title>Ⅰ. 서론</title>
			<p>사상의학을 임상에 적용함에 있어서 무엇보다도 중요한 것은 정확한 체질을 진단하는 일이다. 타고난 장부 기능에 차이가 있어서 생리적ㆍ병리적 특성이 체질마다 다르기 때문에 정확한 체질 진단이 선행되지 않고서는 치료 효과를 기대하기 어렵다.</p>
			<p>그동안 객관적인 진단을 위한 많은 연구가 진행되어 왔지만 현재로서 네 가지 진단 요소를 가장 잘 반영할 수 있는 방법은 설문지를 활용한 방법이다. 개발된 여러 설문지 중에서 신뢰도와 타당도 연구가 이루어진 대표적인 설문지는 QSCC, QSCCⅡ, QSCCⅡ+, SSCQ-P, SSCQ-P short form, KS-15 등이 있다.</p>
			<p>본 연구는 2023년 2월부터 2024년 12월까지 수집한 Short Form 282건의 데이터를 사용하여 판별함수를 개발하고 신뢰도 분석을 진행하였으며, 단계적 변수선택법을 통해 15문항을 선정한 결과를 보고하고자 한다.</p>
		</sec>
		<sec sec-type="methods">
			<title>Ⅱ. 연구방법</title>
			<sec>
				<title>1. 연구대상</title>
				<p>본 연구는 2023년 2월부터 2024년 12월까지 동의대학교 부속한방병원 사상체질과 외래를 방문한 환자 및 건강인 중 사상체질전문의 2인의 체질결과가 일치한 총 282명이 작성한 사상체질진단 설문검사(SSCQ-P short form 40)지를 분석 대상으로 하였다. 본 연구는 동의대학교 부속한방병원 임상시험심사위원회의 승인(DH-2025-24)하에 진행되었다.</p>
			</sec>
			<sec>
				<title>2. 연구방법</title>
				<sec>
					<title>1) 사상체질진단</title>
					<p>임상경험이 20년 이상인 사상체질전문의 2인이 체형기상, 용모사기, 성질재간, 병증약리 등의 체질진단 기준에 근거한 진찰과 내원객이 작성한 설문지를 참고로 독립적으로 체질진단을 실시하였고, 그 중 체질 결과가 일치된 사람을 대상으로 하였다.</p>
				</sec>
				<sec>
					<title>2) 사상체질진단 설문검사(SSCQ-P short form 40)</title>
					<p>환자용 사상체질설문지(SSCQ-P) 축소화 연구를 통해 각 체질별로 10개 문항을 선정하여 총 40문항으로 구성되어 있다. 용모 관련 문항 11개, 체형 관련 문항 11개, 성격 관련 문항 13개, 증상 관련 문항 5개를 차례대로 읽으면서 각 문항마다 “그렇다/보통이다/아니다” 중에 하나를 선택하게 하는 자기보고식 검사이다. 진단정확도는 데이터에 따라 42.65%에서 66.18%이다.</p>
					<table-wrap id="T1" position="float">
						<label>Table 1.</label>
						<caption>
							<title>Questions of SSCQ-P Short Form 40</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Category</th>
									<th>Subclass</th>
									<th>Questions</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>Category</td>
									<td>Countenance(Face)</td>
									<td>Q1 ~ Q11</td>
								</tr>
								<tr>
									<td>Category</td>
									<td>Figure(Body)</td>
									<td>Q12 ~ Q22</td>
								</tr>
								<tr>
									<td>Category</td>
									<td>Personality</td>
									<td>Q23 ~ Q35</td>
								</tr>
								<tr>
									<td>Category</td>
									<td>Symptoms</td>
									<td>Q36 ~ Q40</td>
								</tr>
								<tr>
									<td>Constitution</td>
									<td>TY</td>
									<td>Q1, Q2, Q8, Q10, Q12, Q15, Q21, Q26, Q36, Q38</td>
								</tr>
								<tr>
									<td>Constitution</td>
									<td>SY</td>
									<td>Q3, Q7, Q11, Q13, Q16, Q19, Q23, Q27, Q31, Q34</td>
								</tr>
								<tr>
									<td>Constitution</td>
									<td>TE</td>
									<td>Q4, Q5, Q9, Q17, Q24, Q30, Q33, Q35, Q37, Q39</td>
								</tr>
								<tr>
									<td>Constitution</td>
									<td>SE</td>
									<td>Q6, Q14, Q18, Q20, Q22, Q25, Q28, Q29, Q32, Q40</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<p>TY: Taeyangin, SY: Soyangin, TE: Taeeumin, SE: Soeumin</p>
						</table-wrap-foot>
					</table-wrap>
				</sec>
			</sec>
			<sec>
				<title>3. 통계분석</title>
				<p>전체 문항을 이용한 판별식을 개발하기 위하여 선형판별분석을 실시하였고, 문항 일치도 분석은 크론바흐 알파를, 단축형 판별함수를 활용한 정분류율을 위한 문항 선택은 단계적 변수선택법(stepwise method)을 사용하였다. 통계 분석 패키지는 IBM SPSS Statistics 25.0을 이용하였다.</p>
			</sec>
		</sec>
		<sec sec-type="results">
			<title>Ⅲ. 연구결과</title>
			<sec>
				<title>1. 연구대상자의 일반적 특성</title>
				<p>대상자는 남성 131명, 여성 151명 총 282명이다. 평균 연령은 남성 36.38세, 여성 41.24세이다. 체질 분포는 태음인이 116명(41.1%)으로 가장 많고 소음인 90명(31.9%), 소양인 69명(24.5%), 태양인 7명(2.5%) 순이다. 평균 몸무게, 키, 체질량지수 모두 태음인이 가장 높고, 체질량지수는 태음인이 다른 세 체질에 비해 통계적으로 유의하게 높았다.</p>
				<table-wrap id="T2" position="float">
					<label>Table 2.</label>
					<caption>
						<title>General Characteristics of the Participants</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Variable</th>
								<th>Group</th>
								<th>N</th>
								<th>%</th>
								<th>Mean</th>
								<th>SD</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Sex</td>
								<td>Male</td>
								<td>131</td>
								<td>46.45</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Sex</td>
								<td>Female</td>
								<td>151</td>
								<td>53.55</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Age(years)</td>
								<td>Male</td>
								<td>130</td>
								<td>46.59</td>
								<td>36.38</td>
								<td>18.93</td>
							</tr>
							<tr>
								<td>Age(years)</td>
								<td>Female</td>
								<td>149</td>
								<td>53.41</td>
								<td>41.24</td>
								<td>18.58</td>
							</tr>
							<tr>
								<td>Constitution Type</td>
								<td>TY</td>
								<td>7</td>
								<td>2.5</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Constitution Type</td>
								<td>SY</td>
								<td>69</td>
								<td>24.5</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Constitution Type</td>
								<td>TE</td>
								<td>116</td>
								<td>41.1</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Constitution Type</td>
								<td>SE</td>
								<td>90</td>
								<td>31.9</td>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>Weight(kg)</td>
								<td>TY</td>
								<td>7</td>
								<td/>
								<td>57.29</td>
								<td>8.75</td>
							</tr>
							<tr>
								<td>Weight(kg)</td>
								<td>SY</td>
								<td>67</td>
								<td/>
								<td>59.19</td>
								<td>10.98</td>
							</tr>
							<tr>
								<td>Weight(kg)</td>
								<td>TE</td>
								<td>113</td>
								<td/>
								<td>70.69</td>
								<td>12.17</td>
							</tr>
							<tr>
								<td>Weight(kg)</td>
								<td>SE</td>
								<td>89</td>
								<td/>
								<td>55.80</td>
								<td>8.96</td>
							</tr>
							<tr>
								<td>Weight(kg)</td>
								<td>Total</td>
								<td>276</td>
								<td/>
								<td>62.76</td>
								<td>12.73</td>
							</tr>
							<tr>
								<td>Height(cm)</td>
								<td>TY</td>
								<td>7</td>
								<td/>
								<td>164.00</td>
								<td>6.30</td>
							</tr>
							<tr>
								<td>Height(cm)</td>
								<td>SY</td>
								<td>67</td>
								<td/>
								<td>164.91</td>
								<td>7.87</td>
							</tr>
							<tr>
								<td>Height(cm)</td>
								<td>TE</td>
								<td>115</td>
								<td/>
								<td>167.49</td>
								<td>8.76</td>
							</tr>
							<tr>
								<td>Height(cm)</td>
								<td>SE</td>
								<td>90</td>
								<td/>
								<td>163.99</td>
								<td>8.14</td>
							</tr>
							<tr>
								<td>Height(cm)</td>
								<td>Total</td>
								<td>279</td>
								<td/>
								<td>165.65</td>
								<td>8.41</td>
							</tr>
							<tr>
								<td>BMI(kg/㎡)</td>
								<td>TY(a)</td>
								<td>7</td>
								<td/>
								<td>21.18</td>
								<td>2.47</td>
							</tr>
							<tr>
								<td>BMI(kg/㎡)</td>
								<td>SY(b)</td>
								<td>67</td>
								<td/>
								<td>21.62</td>
								<td>2.79</td>
							</tr>
							<tr>
								<td>BMI(kg/㎡)</td>
								<td>TE(c)</td>
								<td>113</td>
								<td/>
								<td>25.04</td>
								<td>3.10</td>
							</tr>
							<tr>
								<td>BMI(kg/㎡)</td>
								<td>SE(d)</td>
								<td>88</td>
								<td/>
								<td>20.58</td>
								<td>2.06</td>
							</tr>
							<tr>
								<td>BMI(kg/㎡)</td>
								<td>Total</td>
								<td>275</td>
								<td/>
								<td>22.68</td>
								<td>3.37</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>BMI: Body mass index; SD: Standard deviation. For BMI, F=50.14 (p&lt;0.001), Tukey: c&gt;a,b,d.</p>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec>
				<title>2. 전체 대상자에 대한 판별함수개발</title>
				<p>사상체질진단을 위한 체질판별식을 개발하기 위하여 선형판별분석방법에 따라 3개의 판별함수를 적용하였다. 3개 판별함수의 계수는 Table 3과 같고 정분류율은 84.4%로 나타났다.</p>
				<table-wrap id="T3" position="float">
					<label>Table 3.</label>
					<caption>
						<title>Coefficients of the three Discriminant Function for all Participants</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Variable</th>
								<th>DF1</th>
								<th>DF2</th>
								<th>DF3</th>
								<th>Variable</th>
								<th>DF1</th>
								<th>DF2</th>
								<th>DF3</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Q1</td>
								<td>0.082</td>
								<td>-0.019</td>
								<td>0.869</td>
								<td>Q21</td>
								<td>-0.220</td>
								<td>-0.124</td>
								<td>0.222</td>
							</tr>
							<tr>
								<td>Q2</td>
								<td>-0.266</td>
								<td>-0.138</td>
								<td>-0.157</td>
								<td>Q22</td>
								<td>-0.117</td>
								<td>0.182</td>
								<td>0.517</td>
							</tr>
							<tr>
								<td>Q3</td>
								<td>0.841</td>
								<td>-0.064</td>
								<td>0.013</td>
								<td>Q23</td>
								<td>0.370</td>
								<td>-0.091</td>
								<td>-0.487</td>
							</tr>
							<tr>
								<td>Q4</td>
								<td>-0.113</td>
								<td>-0.404</td>
								<td>-0.066</td>
								<td>Q24</td>
								<td>-0.090</td>
								<td>0.111</td>
								<td>0.236</td>
							</tr>
							<tr>
								<td>Q5</td>
								<td>-0.353</td>
								<td>-0.234</td>
								<td>-0.363</td>
								<td>Q25</td>
								<td>-0.046</td>
								<td>0.103</td>
								<td>0.025</td>
							</tr>
							<tr>
								<td>Q6</td>
								<td>-0.136</td>
								<td>0.000</td>
								<td>-0.505</td>
								<td>Q26</td>
								<td>0.105</td>
								<td>0.009</td>
								<td>0.060</td>
							</tr>
							<tr>
								<td>Q7</td>
								<td>0.306</td>
								<td>0.157</td>
								<td>-0.007</td>
								<td>Q27</td>
								<td>0.227</td>
								<td>0.269</td>
								<td>-0.008</td>
							</tr>
							<tr>
								<td>Q8</td>
								<td>0.038</td>
								<td>0.018</td>
								<td>0.237</td>
								<td>Q28</td>
								<td>-0.135</td>
								<td>0.409</td>
								<td>0.438</td>
							</tr>
							<tr>
								<td>Q9</td>
								<td>-0.007</td>
								<td>-0.084</td>
								<td>0.061</td>
								<td>Q29</td>
								<td>0.063</td>
								<td>0.234</td>
								<td>-0.279</td>
							</tr>
							<tr>
								<td>Q10</td>
								<td>0.180</td>
								<td>-0.124</td>
								<td>0.304</td>
								<td>Q30</td>
								<td>-0.069</td>
								<td>0.215</td>
								<td>-0.213</td>
							</tr>
							<tr>
								<td>Q11</td>
								<td>0.216</td>
								<td>0.225</td>
								<td>-0.011</td>
								<td>Q31</td>
								<td>0.168</td>
								<td>0.025</td>
								<td>-0.025</td>
							</tr>
							<tr>
								<td>Q12</td>
								<td>0.104</td>
								<td>0.383</td>
								<td>-0.267</td>
								<td>Q32</td>
								<td>-0.111</td>
								<td>0.123</td>
								<td>0.033</td>
							</tr>
							<tr>
								<td>Q13</td>
								<td>0.202</td>
								<td>0.185</td>
								<td>-0.039</td>
								<td>Q33</td>
								<td>0.009</td>
								<td>-0.057</td>
								<td>-0.102</td>
							</tr>
							<tr>
								<td>Q14</td>
								<td>-0.201</td>
								<td>-0.034</td>
								<td>0.068</td>
								<td>Q34</td>
								<td>-0.195</td>
								<td>0.087</td>
								<td>-0.340</td>
							</tr>
							<tr>
								<td>Q15</td>
								<td>-0.105</td>
								<td>-0.133</td>
								<td>0.035</td>
								<td>Q35</td>
								<td>0.005</td>
								<td>-0.041</td>
								<td>0.469</td>
							</tr>
							<tr>
								<td>Q16</td>
								<td>-0.061</td>
								<td>-0.049</td>
								<td>0.503</td>
								<td>Q36</td>
								<td>0.091</td>
								<td>0.047</td>
								<td>0.406</td>
							</tr>
							<tr>
								<td>Q17</td>
								<td>0.238</td>
								<td>-0.524</td>
								<td>-0.309</td>
								<td>Q37</td>
								<td>0.225</td>
								<td>-0.292</td>
								<td>-0.239</td>
							</tr>
							<tr>
								<td>Q18</td>
								<td>-0.279</td>
								<td>0.259</td>
								<td>0.179</td>
								<td>Q38</td>
								<td>0.125</td>
								<td>0.027</td>
								<td>0.111</td>
							</tr>
							<tr>
								<td>Q19</td>
								<td>0.312</td>
								<td>-0.043</td>
								<td>-0.439</td>
								<td>Q39</td>
								<td>-0.079</td>
								<td>-0.527</td>
								<td>-0.176</td>
							</tr>
							<tr>
								<td>Q20</td>
								<td>0.065</td>
								<td>-0.024</td>
								<td>0.000</td>
								<td>Q40</td>
								<td>0.102</td>
								<td>0.383</td>
								<td>0.065</td>
							</tr>
							<tr>
								<td>Constant Term</td>
								<td>-3.614</td>
								<td>-0.967</td>
								<td>-2.205</td>
								<td/>
								<td/>
								<td/>
								<td/>
							</tr>
							<tr>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
								<td>DF: Discriminant Function</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>DF: Discriminant Function</p>
					</table-wrap-foot>
				</table-wrap>
				<table-wrap id="T4" position="float">
					<label>Table 4.</label>
					<caption>
						<title>Accuracy of the Discriminant Function for all Participants</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Actual</th>
								<th>Measure</th>
								<th>Constitution</th>
								<th>TY</th>
								<th>SY</th>
								<th>TE</th>
								<th>SE</th>
								<th>Total</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TY</td>
								<td>6</td>
								<td>1</td>
								<td>0</td>
								<td>0</td>
								<td>7</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SY</td>
								<td>1</td>
								<td>56</td>
								<td>6</td>
								<td>6</td>
								<td>69</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TE</td>
								<td>0</td>
								<td>3</td>
								<td>98</td>
								<td>15</td>
								<td>116</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SE</td>
								<td>0</td>
								<td>3</td>
								<td>9</td>
								<td>78</td>
								<td>90</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TY</td>
								<td>85.7</td>
								<td>14.3</td>
								<td>0.0</td>
								<td>0.0</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SY</td>
								<td>1.4</td>
								<td>81.2</td>
								<td>8.7</td>
								<td>8.7</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TE</td>
								<td>0.0</td>
								<td>2.6</td>
								<td>84.5</td>
								<td>12.9</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SE</td>
								<td>0.0</td>
								<td>3.3</td>
								<td>10.0</td>
								<td>86.7</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
								<td>Accuracy = (6+56+98+78)/282*100 = 84.4%</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>Accuracy = (6+56+98+78)/282*100 = 84.4%</p>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec>
				<title>3. 데이터분할 후 모형 평가</title>
				<p>전체 데이터 282건을 학습용(70%, n=198)과 평가용(30%, n=84)으로 나누고 학습용 데이터를 사용하여 선형판별함수 모델을 학습시켰다. 학습용 정분류율은 86.9%이고 평가용 정분류율은 78.6%로 나타났다.</p>
				<table-wrap id="T5" position="float">
					<label>Table 5.</label>
					<caption>
						<title>Coefficients of three Discriminant Functions for Training</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Variable</th>
								<th>DFL1</th>
								<th>DFL2</th>
								<th>DFL3</th>
								<th>Variable</th>
								<th>DFL1</th>
								<th>DFL2</th>
								<th>DFL3</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Q1</td>
								<td>0.2196202</td>
								<td>0.049586</td>
								<td>0.6923323</td>
								<td>Q21</td>
								<td>0.2116453</td>
								<td>-0.034948</td>
								<td>0.2081517</td>
							</tr>
							<tr>
								<td>Q2</td>
								<td>-0.12621</td>
								<td>-0.190916</td>
								<td>0.0514419</td>
								<td>Q22</td>
								<td>0.1087056</td>
								<td>0.0666448</td>
								<td>0.3053012</td>
							</tr>
							<tr>
								<td>Q3</td>
								<td>-0.677139</td>
								<td>-0.195526</td>
								<td>0.1836929</td>
								<td>Q23</td>
								<td>-0.622893</td>
								<td>0.006008</td>
								<td>-0.344858</td>
							</tr>
							<tr>
								<td>Q4</td>
								<td>0.2756295</td>
								<td>-0.501665</td>
								<td>-0.12366</td>
								<td>Q24</td>
								<td>0.1108975</td>
								<td>0.086969</td>
								<td>0.2153025</td>
							</tr>
							<tr>
								<td>Q5</td>
								<td>0.3443318</td>
								<td>-0.146193</td>
								<td>-0.153143</td>
								<td>Q25</td>
								<td>-0.025042</td>
								<td>0.2089822</td>
								<td>-0.360364</td>
							</tr>
							<tr>
								<td>Q6</td>
								<td>0.1609379</td>
								<td>-0.018196</td>
								<td>-0.560885</td>
								<td>Q26</td>
								<td>0.0384799</td>
								<td>-0.091715</td>
								<td>-0.073041</td>
							</tr>
							<tr>
								<td>Q7</td>
								<td>-0.226238</td>
								<td>0.2191479</td>
								<td>-0.183211</td>
								<td>Q27</td>
								<td>-0.357117</td>
								<td>0.2756859</td>
								<td>0.0632964</td>
							</tr>
							<tr>
								<td>Q8</td>
								<td>0.0344041</td>
								<td>0.079556</td>
								<td>0.3328394</td>
								<td>Q28</td>
								<td>0.1039466</td>
								<td>0.5180456</td>
								<td>0.551571</td>
							</tr>
							<tr>
								<td>Q9</td>
								<td>0.2303236</td>
								<td>0.0417972</td>
								<td>-0.154628</td>
								<td>Q29</td>
								<td>0.0273241</td>
								<td>0.0940337</td>
								<td>-0.116448</td>
							</tr>
							<tr>
								<td>Q10</td>
								<td>-0.135532</td>
								<td>-0.128264</td>
								<td>0.2195886</td>
								<td>Q30</td>
								<td>-0.115548</td>
								<td>0.2614018</td>
								<td>-0.139139</td>
							</tr>
							<tr>
								<td>Q11</td>
								<td>-0.423178</td>
								<td>0.1319141</td>
								<td>-0.168235</td>
								<td>Q31</td>
								<td>-0.149641</td>
								<td>-0.010587</td>
								<td>-0.024201</td>
							</tr>
							<tr>
								<td>Q12</td>
								<td>-0.210933</td>
								<td>0.4819029</td>
								<td>-0.513869</td>
								<td>Q32</td>
								<td>0.1548365</td>
								<td>0.1695518</td>
								<td>0.0405587</td>
							</tr>
							<tr>
								<td>Q13</td>
								<td>-0.215139</td>
								<td>0.2188395</td>
								<td>0.0350755</td>
								<td>Q33</td>
								<td>-0.129466</td>
								<td>-0.203522</td>
								<td>0.0074281</td>
							</tr>
							<tr>
								<td>Q14</td>
								<td>0.2861439</td>
								<td>0.0250708</td>
								<td>-0.146061</td>
								<td>Q34</td>
								<td>0.3154634</td>
								<td>-0.025805</td>
								<td>-0.268099</td>
							</tr>
							<tr>
								<td>Q15</td>
								<td>-0.094152</td>
								<td>-0.109513</td>
								<td>-0.174905</td>
								<td>Q35</td>
								<td>-0.16998</td>
								<td>-0.195765</td>
								<td>0.6352525</td>
							</tr>
							<tr>
								<td>Q16</td>
								<td>0.202707</td>
								<td>-0.053532</td>
								<td>0.2774345</td>
								<td>Q36</td>
								<td>-0.174149</td>
								<td>0.0731443</td>
								<td>0.5093651</td>
							</tr>
							<tr>
								<td>Q17</td>
								<td>-0.364003</td>
								<td>-0.447554</td>
								<td>-0.390525</td>
								<td>Q37</td>
								<td>-0.274508</td>
								<td>-0.292071</td>
								<td>-0.298731</td>
							</tr>
							<tr>
								<td>Q18</td>
								<td>0.3449157</td>
								<td>0.3877926</td>
								<td>0.1592707</td>
								<td>Q38</td>
								<td>-0.182925</td>
								<td>-0.015525</td>
								<td>0.1047682</td>
							</tr>
							<tr>
								<td>Q19</td>
								<td>-0.321478</td>
								<td>0.0296956</td>
								<td>-0.319128</td>
								<td>Q39</td>
								<td>0.1131206</td>
								<td>-0.451588</td>
								<td>-0.293957</td>
							</tr>
							<tr>
								<td>Q20</td>
								<td>-0.077914</td>
								<td>0.0450123</td>
								<td>0.120498</td>
								<td>Q40</td>
								<td>-0.195525</td>
								<td>0.4118175</td>
								<td>-0.116348</td>
							</tr>
							<tr>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
								<td>DFL: Discriminant Function for Learning</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>DFL: Discriminant Function for Learning</p>
					</table-wrap-foot>
				</table-wrap>
				<table-wrap id="T6" position="float">
					<label>Table 6.</label>
					<caption>
						<title>Accuracy of the Discriminant Function for Training</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Actual</th>
								<th>Measure</th>
								<th>Constitution</th>
								<th>TY</th>
								<th>SY</th>
								<th>TE</th>
								<th>SE</th>
								<th>Total</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TY</td>
								<td>5</td>
								<td>0</td>
								<td>0</td>
								<td>0</td>
								<td>5</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SY</td>
								<td>1</td>
								<td>42</td>
								<td>3</td>
								<td>2</td>
								<td>48</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TE</td>
								<td>1</td>
								<td>1</td>
								<td>69</td>
								<td>11</td>
								<td>82</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SE</td>
								<td>0</td>
								<td>1</td>
								<td>6</td>
								<td>56</td>
								<td>63</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TY</td>
								<td>100.0</td>
								<td>0.0</td>
								<td>0.0</td>
								<td>0.0</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SY</td>
								<td>2.1</td>
								<td>87.5</td>
								<td>6.3</td>
								<td>4.2</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TE</td>
								<td>1.2</td>
								<td>1.2</td>
								<td>84.1</td>
								<td>13.4</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SE</td>
								<td>0.0</td>
								<td>1.6</td>
								<td>9.5</td>
								<td>88.9</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
								<td>Accuracy = (5+42+69+56)/198*100=86.9%</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>Accuracy = (5+42+69+56)/198*100 = 86.9%</p>
					</table-wrap-foot>
				</table-wrap>
				<table-wrap id="T7" position="float">
					<label>Table 7.</label>
					<caption>
						<title>Accuracy of the Discriminant Function for Test</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Actual</th>
								<th>Measure</th>
								<th>Constitution</th>
								<th>TY</th>
								<th>SY</th>
								<th>TE</th>
								<th>SE</th>
								<th>Total</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TY</td>
								<td>2</td>
								<td>0</td>
								<td>0</td>
								<td>0</td>
								<td>2</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SY</td>
								<td>1</td>
								<td>13</td>
								<td>5</td>
								<td>2</td>
								<td>21</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TE</td>
								<td>1</td>
								<td>0</td>
								<td>29</td>
								<td>4</td>
								<td>34</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SE</td>
								<td>0</td>
								<td>1</td>
								<td>4</td>
								<td>22</td>
								<td>27</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TY</td>
								<td>100.0</td>
								<td>0.0</td>
								<td>0.0</td>
								<td>0.0</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SY</td>
								<td>4.8</td>
								<td>61.9</td>
								<td>23.8</td>
								<td>9.5</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TE</td>
								<td>2.9</td>
								<td>0.0</td>
								<td>85.3</td>
								<td>11.8</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SE</td>
								<td>0.0</td>
								<td>3.7</td>
								<td>14.8</td>
								<td>81.5</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
								<td>Accuracy = (2+13+29+22)/84*100=78.6%</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>Accuracy = (2+13+29+22)/84*100 = 78.6%</p>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec>
				<title>4. 크론바흐 알파를 이용한 문항분석</title>
				<p>체질별 문항의 내적일관성 평가를 위하여 크론바흐 알파를 이용한 문항분석을 수행하였다.</p>
				<sec>
					<title>1) 태양인 문항 분석결과</title>
					<p>태양인 10개 전체 문항에 대한 크론바흐 알파 값은 0.600이고, Q36, Q21, Q12, Q38을 제거한 6개 문항의 크론바흐 알파는 0.712이다.</p>
					<table-wrap id="T8" position="float">
						<label>Table 8.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the all TY Items</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.600</td>
									<td>10</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q1</td>
									<td/>
									<td>19.71</td>
									<td>9.582</td>
									<td>0.547</td>
									<td>0.508</td>
								</tr>
								<tr>
									<td>Q2</td>
									<td/>
									<td>19.84</td>
									<td>9.520</td>
									<td>0.524</td>
									<td>0.510</td>
								</tr>
								<tr>
									<td>Q8</td>
									<td/>
									<td>19.85</td>
									<td>9.503</td>
									<td>0.526</td>
									<td>0.510</td>
								</tr>
								<tr>
									<td>Q10</td>
									<td/>
									<td>19.85</td>
									<td>9.989</td>
									<td>0.409</td>
									<td>0.541</td>
								</tr>
								<tr>
									<td>Q12</td>
									<td/>
									<td>19.72</td>
									<td>11.461</td>
									<td>0.111</td>
									<td>0.613</td>
								</tr>
								<tr>
									<td>Q15</td>
									<td/>
									<td>19.85</td>
									<td>10.758</td>
									<td>0.222</td>
									<td>0.588</td>
								</tr>
								<tr>
									<td>Q21</td>
									<td/>
									<td>19.65</td>
									<td>11.801</td>
									<td>0.057</td>
									<td>0.623</td>
								</tr>
								<tr>
									<td>Q26</td>
									<td/>
									<td>19.63</td>
									<td>10.790</td>
									<td>0.282</td>
									<td>0.573</td>
								</tr>
								<tr>
									<td>Q36</td>
									<td/>
									<td>19.48</td>
									<td>12.051</td>
									<td>0.007</td>
									<td>0.633</td>
								</tr>
								<tr>
									<td>Q38</td>
									<td/>
									<td>19.83</td>
									<td>11.264</td>
									<td>0.132</td>
									<td>0.610</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<p>TY: Taeyangin</p>
						</table-wrap-foot>
					</table-wrap>
					<table-wrap id="T9" position="float">
						<label>Table 9.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the 6 TY Items after Q36, Q21, Q12, Q38 removal</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.712</td>
									<td>6</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q1</td>
									<td/>
									<td>10.65</td>
									<td>6.050</td>
									<td>0.570</td>
									<td>0.635</td>
								</tr>
								<tr>
									<td>Q2</td>
									<td/>
									<td>10.79</td>
									<td>5.967</td>
									<td>0.554</td>
									<td>0.638</td>
								</tr>
								<tr>
									<td>Q8</td>
									<td/>
									<td>10.79</td>
									<td>6.043</td>
									<td>0.527</td>
									<td>0.646</td>
								</tr>
								<tr>
									<td>Q10</td>
									<td/>
									<td>10.80</td>
									<td>6.338</td>
									<td>0.434</td>
									<td>0.676</td>
								</tr>
								<tr>
									<td>Q15</td>
									<td/>
									<td>10.80</td>
									<td>6.694</td>
									<td>0.306</td>
									<td>0.717</td>
								</tr>
								<tr>
									<td>Q26</td>
									<td/>
									<td>10.57</td>
									<td>7.035</td>
									<td>0.294</td>
									<td>0.715</td>
								</tr>
							</tbody>
						</table>
					</table-wrap>
				</sec>
				<sec>
					<title>2) 소양인 문항 분석결과</title>
					<p>소양인 10개 전체 문항에 대한 크론바흐 알파 값은 0.781이고 Q31을 삭제한 9개 문항의 크론바흐 알파는 0.790이다.</p>
					<table-wrap id="T10" position="float">
						<label>Table 10.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the all SY Items</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.781</td>
									<td>10</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q3</td>
									<td/>
									<td>19.32</td>
									<td>15.750</td>
									<td>0.651</td>
									<td>0.735</td>
								</tr>
								<tr>
									<td>Q7</td>
									<td/>
									<td>19.33</td>
									<td>16.741</td>
									<td>0.469</td>
									<td>0.760</td>
								</tr>
								<tr>
									<td>Q11</td>
									<td/>
									<td>19.28</td>
									<td>16.880</td>
									<td>0.486</td>
									<td>0.758</td>
								</tr>
								<tr>
									<td>Q13</td>
									<td/>
									<td>19.32</td>
									<td>17.783</td>
									<td>0.354</td>
									<td>0.773</td>
								</tr>
								<tr>
									<td>Q16</td>
									<td/>
									<td>19.38</td>
									<td>17.347</td>
									<td>0.405</td>
									<td>0.768</td>
								</tr>
								<tr>
									<td>Q19</td>
									<td/>
									<td>19.74</td>
									<td>17.059</td>
									<td>0.414</td>
									<td>0.767</td>
								</tr>
								<tr>
									<td>Q23</td>
									<td/>
									<td>19.33</td>
									<td>16.287</td>
									<td>0.515</td>
									<td>0.753</td>
								</tr>
								<tr>
									<td>Q27</td>
									<td/>
									<td>19.67</td>
									<td>16.406</td>
									<td>0.543</td>
									<td>0.750</td>
								</tr>
								<tr>
									<td>Q31</td>
									<td/>
									<td>19.45</td>
									<td>18.305</td>
									<td>0.224</td>
									<td>0.790</td>
								</tr>
								<tr>
									<td>Q34</td>
									<td/>
									<td>19.72</td>
									<td>17.300</td>
									<td>0.436</td>
									<td>0.764</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<p>SY: Soyangin</p>
						</table-wrap-foot>
					</table-wrap>
					<table-wrap id="T11" position="float">
						<label>Table 11.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the 9 SY Items after Q31 removal</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.790</td>
									<td>9</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q3</td>
									<td/>
									<td>17.16</td>
									<td>13.805</td>
									<td>0.657</td>
									<td>0.744</td>
								</tr>
								<tr>
									<td>Q7</td>
									<td/>
									<td>17.16</td>
									<td>14.827</td>
									<td>0.457</td>
									<td>0.773</td>
								</tr>
								<tr>
									<td>Q11</td>
									<td/>
									<td>17.12</td>
									<td>14.766</td>
									<td>0.511</td>
									<td>0.766</td>
								</tr>
								<tr>
									<td>Q13</td>
									<td/>
									<td>17.15</td>
									<td>15.764</td>
									<td>0.351</td>
									<td>0.786</td>
								</tr>
								<tr>
									<td>Q16</td>
									<td/>
									<td>17.22</td>
									<td>15.366</td>
									<td>0.400</td>
									<td>0.780</td>
								</tr>
								<tr>
									<td>Q19</td>
									<td/>
									<td>17.58</td>
									<td>14.964</td>
									<td>0.430</td>
									<td>0.777</td>
								</tr>
								<tr>
									<td>Q23</td>
									<td/>
									<td>17.17</td>
									<td>14.324</td>
									<td>0.517</td>
									<td>0.764</td>
								</tr>
								<tr>
									<td>Q27</td>
									<td/>
									<td>17.51</td>
									<td>14.479</td>
									<td>0.538</td>
									<td>0.762</td>
								</tr>
								<tr>
									<td>Q34</td>
									<td/>
									<td>17.55</td>
									<td>15.288</td>
									<td>0.436</td>
									<td>0.776</td>
								</tr>
							</tbody>
						</table>
					</table-wrap>
				</sec>
				<sec>
					<title>3) 태음인 문항 분석결과</title>
					<p>태음인 10개 전체 문항에 대한 크론바흐 알파 값은 0.686이다. 항목이 제거될 경우 0.686보다 큰 항목이 없어 문항 수를 더 줄이지 않았다.</p>
					<table-wrap id="T12" position="float">
						<label>Table 12.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the all TE Items</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.686</td>
									<td>10</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q4</td>
									<td/>
									<td>17.47</td>
									<td>12.706</td>
									<td>0.453</td>
									<td>0.643</td>
								</tr>
								<tr>
									<td>Q5</td>
									<td/>
									<td>17.40</td>
									<td>12.811</td>
									<td>0.429</td>
									<td>0.647</td>
								</tr>
								<tr>
									<td>Q9</td>
									<td/>
									<td>17.23</td>
									<td>13.466</td>
									<td>0.296</td>
									<td>0.672</td>
								</tr>
								<tr>
									<td>Q17</td>
									<td/>
									<td>17.39</td>
									<td>13.064</td>
									<td>0.378</td>
									<td>0.657</td>
								</tr>
								<tr>
									<td>Q24</td>
									<td/>
									<td>17.59</td>
									<td>13.595</td>
									<td>0.285</td>
									<td>0.674</td>
								</tr>
								<tr>
									<td>Q30</td>
									<td/>
									<td>17.57</td>
									<td>13.257</td>
									<td>0.359</td>
									<td>0.660</td>
								</tr>
								<tr>
									<td>Q33</td>
									<td/>
									<td>17.45</td>
									<td>13.038</td>
									<td>0.343</td>
									<td>0.663</td>
								</tr>
								<tr>
									<td>Q35</td>
									<td/>
									<td>17.51</td>
									<td>13.525</td>
									<td>0.338</td>
									<td>0.664</td>
								</tr>
								<tr>
									<td>Q37</td>
									<td/>
									<td>17.72</td>
									<td>13.688</td>
									<td>0.265</td>
									<td>0.677</td>
								</tr>
								<tr>
									<td>Q39</td>
									<td/>
									<td>17.43</td>
									<td>13.320</td>
									<td>0.312</td>
									<td>0.669</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<p>TE: Taeeumin</p>
						</table-wrap-foot>
					</table-wrap>
				</sec>
				<sec>
					<title>4) 소음인 문항 분석결과</title>
					<p>소음인 10개 전체 문항에 대한 크론바흐 알파 값은 0.740이다. 항목이 제거될 경우 0.740보다 큰 항목이 없어 문항 수를 더 줄이지 않았다.</p>
					<table-wrap id="T13" position="float">
						<label>Table 13.</label>
						<caption>
							<title>Reliability Statistics and Item - Total Statistics for the all SE Items</title>
						</caption>
						<table frame="hsides" rules="groups">
							<thead>
								<tr>
									<th>Cronbach's Alpha</th>
									<th>N of Items</th>
									<th>Scale Mean if Item Deleted</th>
									<th>Scale Variance if Item Deleted</th>
									<th>Corrected Item-Total Correlation</th>
									<th>Cronbach's Alpha if Item Deleted</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td>0.740</td>
									<td>10</td>
									<td/>
									<td/>
									<td/>
									<td/>
								</tr>
								<tr>
									<td>Q6</td>
									<td/>
									<td>17.91</td>
									<td>15.438</td>
									<td>0.493</td>
									<td>0.705</td>
								</tr>
								<tr>
									<td>Q14</td>
									<td/>
									<td>17.61</td>
									<td>16.060</td>
									<td>0.343</td>
									<td>0.728</td>
								</tr>
								<tr>
									<td>Q18</td>
									<td/>
									<td>17.74</td>
									<td>16.113</td>
									<td>0.325</td>
									<td>0.731</td>
								</tr>
								<tr>
									<td>Q20</td>
									<td/>
									<td>17.57</td>
									<td>15.506</td>
									<td>0.475</td>
									<td>0.708</td>
								</tr>
								<tr>
									<td>Q22</td>
									<td/>
									<td>17.75</td>
									<td>16.310</td>
									<td>0.326</td>
									<td>0.730</td>
								</tr>
								<tr>
									<td>Q25</td>
									<td/>
									<td>17.68</td>
									<td>16.445</td>
									<td>0.319</td>
									<td>0.730</td>
								</tr>
								<tr>
									<td>Q28</td>
									<td/>
									<td>17.84</td>
									<td>15.262</td>
									<td>0.472</td>
									<td>0.708</td>
								</tr>
								<tr>
									<td>Q29</td>
									<td/>
									<td>17.77</td>
									<td>15.459</td>
									<td>0.471</td>
									<td>0.708</td>
								</tr>
								<tr>
									<td>Q32</td>
									<td/>
									<td>17.59</td>
									<td>15.261</td>
									<td>0.519</td>
									<td>0.701</td>
								</tr>
								<tr>
									<td>Q40</td>
									<td/>
									<td>17.63</td>
									<td>16.219</td>
									<td>0.282</td>
									<td>0.738</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<p>SE: Soeumin</p>
						</table-wrap-foot>
					</table-wrap>
				</sec>
			</sec>
			<sec>
				<title>5. 단축형 판별함수를 활용한 정분류율</title>
				<p>단계적 변수선택법(stepwise method)을 통하여 체질 판별에 유리한 15문항을 선택하였다. 선택된 15문항은 태양인 2개, 소양인 3개, 태음인 5개, 소음인 5개였고, 정분류율은 79.4%였다.</p>
				<table-wrap id="T14" position="float">
					<label>Table 14.</label>
					<caption>
						<title>Coefficients of three Discriminant Functions reduced to 15 Items</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Category</th>
								<th>Items(Constitution)</th>
								<th>DF1</th>
								<th>DF2</th>
								<th>DF3</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q1(TY) Decisive</td>
								<td>0.033</td>
								<td>-0.004</td>
								<td>1.189</td>
							</tr>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q3(SY) Nimble and courageous</td>
								<td>1.043</td>
								<td>0.215</td>
								<td>-0.198</td>
							</tr>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q4(TE) Taciturn and quiet</td>
								<td>-0.124</td>
								<td>-0.398</td>
								<td>-0.042</td>
							</tr>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q5(TE) Dignified</td>
								<td>-0.299</td>
								<td>-0.315</td>
								<td>-0.190</td>
							</tr>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q6(SE) Gentle</td>
								<td>-0.110</td>
								<td>0.090</td>
								<td>-0.854</td>
							</tr>
							<tr>
								<td>Countenance (Face)</td>
								<td>Q11(SY) Talkative and rash</td>
								<td>0.292</td>
								<td>0.226</td>
								<td>0.105</td>
							</tr>
							<tr>
								<td>Figure (Body)</td>
								<td>Q12(TY) Slender waist</td>
								<td>0.098</td>
								<td>0.438</td>
								<td>-0.338</td>
							</tr>
							<tr>
								<td>Figure (Body)</td>
								<td>Q17(TE) Strong parts? Abdomen</td>
								<td>0.276</td>
								<td>-0.457</td>
								<td>-0.102</td>
							</tr>
							<tr>
								<td>Figure (Body)</td>
								<td>Q18(SE) Strong parts? Buttocks</td>
								<td>-0.347</td>
								<td>0.238</td>
								<td>0.310</td>
							</tr>
							<tr>
								<td>Figure (Body)</td>
								<td>Q22(SE) Tender and soft</td>
								<td>-0.208</td>
								<td>0.222</td>
								<td>0.562</td>
							</tr>
							<tr>
								<td>Personality</td>
								<td>Q23(SY) I make decisions easily</td>
								<td>0.551</td>
								<td>-0.045</td>
								<td>-0.490</td>
							</tr>
							<tr>
								<td>Personality</td>
								<td>Q28(SE) I am introverted</td>
								<td>-0.248</td>
								<td>0.473</td>
								<td>0.315</td>
							</tr>
							<tr>
								<td>Symptoms</td>
								<td>Q37(TE) I like meat</td>
								<td>0.113</td>
								<td>-0.344</td>
								<td>-0.068</td>
							</tr>
							<tr>
								<td>Symptoms</td>
								<td>Q39(TE) I feel refreshed when I sweat</td>
								<td>0.030</td>
								<td>-0.490</td>
								<td>-0.007</td>
							</tr>
							<tr>
								<td>Symptoms</td>
								<td>Q40(SE) I have cold hands and feet</td>
								<td>-0.049</td>
								<td>0.435</td>
								<td>0.038</td>
							</tr>
							<tr>
								<td/>
								<td>Constant Term</td>
								<td>-2.771</td>
								<td>-0.791</td>
								<td>-0.611</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>TY: Taeyangin, SY: Soyangin, TE: Taeeumin, SE: Soeumin. DF: Discriminant Function</p>
					</table-wrap-foot>
				</table-wrap>
				<table-wrap id="T15" position="float">
					<label>Table 15.</label>
					<caption>
						<title>Accuracy of the Discriminant Functions reduced to 15 Items</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>Actual</th>
								<th>Measure</th>
								<th>Constitution</th>
								<th>TY</th>
								<th>SY</th>
								<th>TE</th>
								<th>SE</th>
								<th>Total</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TY</td>
								<td>3</td>
								<td>3</td>
								<td>1</td>
								<td>0</td>
								<td>7</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SY</td>
								<td>0</td>
								<td>54</td>
								<td>8</td>
								<td>7</td>
								<td>69</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>TE</td>
								<td>0</td>
								<td>6</td>
								<td>95</td>
								<td>15</td>
								<td>116</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>Frequency</td>
								<td>SE</td>
								<td>0</td>
								<td>4</td>
								<td>14</td>
								<td>72</td>
								<td>90</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TY</td>
								<td>42.9</td>
								<td>42.9</td>
								<td>14.3</td>
								<td>0.0</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SY</td>
								<td>0.0</td>
								<td>78.3</td>
								<td>11.6</td>
								<td>10.1</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>TE</td>
								<td>0.0</td>
								<td>5.2</td>
								<td>81.9</td>
								<td>12.9</td>
								<td>100.0</td>
							</tr>
							<tr>
								<td>Actual</td>
								<td>%</td>
								<td>SE</td>
								<td>0.0</td>
								<td>4.4</td>
								<td>15.6</td>
								<td>80.0</td>
								<td>100.0</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<p>Accuracy = (3+54+95+72)/282*100 = 79.4%</p>
					</table-wrap-foot>
				</table-wrap>
			</sec>
		</sec>
		<sec sec-type="discussion">
			<title>Ⅳ. 고찰</title>
			<p>사상의학에서는 체질마다 타고난 성정과 장부의 기능 및 병리 기전이 다르기 때문에 병증의 치료에 앞서 정확한 체질진단이 선행되어야 한다. 그동안 객관적인 체질 진단을 위해 많은 연구가 진행되었는데 네 가지 진단 기준을 가장 잘 반영한 방법은 설문지를 이용한 방법이라고 생각된다.</p>
			<p>본 연구에서는 2023년 2월부터 2024년 12월까지 본원 외래를 방문하여 사상체질검사를 받은 환자 및 건강인 282명을 대상으로 Short Form 자료를 정리하여 통계 분석을 시행하였다.</p>
			<p>전체 자료에 대한 선형판별분석 결과 정분류율은 84.4%였고, 학습용과 평가용으로 데이터를 나누었을 때 각각 86.9%와 78.6%였다. 실제 새로운 데이터에 대한 예측 성능은 평가용 정분류율에 가깝다고 볼 수 있다.</p>
			<p>크론바흐 알파를 이용한 문항분석 결과 태양인 문항은 10개에서 6개로, 소양인 문항은 10개에서 9개로 줄이는 것을 고려할 수 있었다. 태음인과 소음인 문항은 내적 일관성이 비교적 양호하였다.</p>
			<p>또한 단계적 변수선택법으로 선별한 15문항을 이용한 판별함수의 정분류율은 79.4%였다. 40문항을 모두 사용했을 때보다 약간 낮았지만 임상에서 간편하게 사용할 수 있는 가능성을 보여준다.</p>
		</sec>
		<sec sec-type="conclusions">
			<title>Ⅴ. 결론</title>
			<p>1. 282건의 데이터로 선형판별분석한 결과 Short Form의 진단정확율은 84.4%이다.</p>
			<p>2. 학습용 70%, 평가용 30%로 데이터 분할 후 모형 평가 결과 학습용 정분류율은 86.9%이고 평가용 정분류율은 78.6%이다.</p>
			<p>3. 크론바흐 알파를 이용한 문항분석 결과 태양인 문항은 10개에서 6개로, 소양인 문항은 10개에서 9개로 줄이는 것을 고려할 수 있다.</p>
			<p>4. 단계적 변수선택법으로 15문항을 선정하였고 단축형 판별함수를 적용한 결과 정분류율은 79.4%로 임상에서 간편하게 사용할 수 있다.</p>
		</sec>
		<sec sec-type="ack">
			<title>Ⅵ. 감사의 말씀</title>
			<p>이 논문은 2025학년도 동의대학교 교내연구비에 의해 연구되었음(No. 202501000001).</p>
		</sec>
	</body>
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		<app-group>
			<app id="APP1">
				<title>사상체질진단 설문검사</title>
				<p>(SSCQ-P short form 40)</p>
				<sec>
					<title>설문에 답하는 요령</title>
					<p>다음 페이지의 40개 설문 문항을 차례대로 읽으면서,</p>
					<p>1. 각각의 문항에 가장 가까운 답을 “그렇다”, “보통이다”, “아니다” 중에서 한 개 선택하십시오.</p>
					<p>2. 체질이란 사람마다 서로 다르며, 좋거나 나쁜 것이 아닙니다.</p>
					<p>3. “편안하고 솔직한 마음”으로 응답해 주세요.</p>
					<p>4. 한 문항을 지나치게 오래 생각하지 말아주세요.</p>
				</sec>
				<table-wrap id="AT1" position="float">
					<label>Appendix Table 1.</label>
					<caption>
						<title>Respondent Information Form</title>
					</caption>
					<table frame="hsides" rules="groups">
						<thead>
							<tr>
								<th>이름</th>
								<th>성별</th>
								<th>나이</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td>■ 이 름:</td>
								<td>■ 성 별: 남 / 여</td>
								<td>■ 나 이: 만 세</td>
							</tr>
							<tr>
								<td>■ 몸무게: kg</td>
								<td>■ 키: cm</td>
								<td>■ BMI: kg/㎡</td>
							</tr>
							<tr>
								<td>■ 작성일: 년 월 일</td>
								<td/>
								<td/>
							</tr>
						</tbody>
					</table>
				</table-wrap>
				<p>본 검사는 귀하의 체질을 알아보기 위한 것입니다.</p>
				<sec>
					<title>용모</title>
					<p>(1) 과단성(카리스마적인 면)이 있다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(2) 인상이 뚜렷하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(3) 날쌔면서 용감하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(4) 과묵하면서 점잖다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(5) 듬직하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(6) 온순하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(7) 얼굴 옆모습이 앞으로 돌출형이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(8) 눈빛이 강하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(9) 귓불이 두툼하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(10) 목소리가 크고 우렁차다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(11) 말이 많아 경솔해보인다 ① 그렇다 ② 보통이다 ③ 아니다</p>
				</sec>
				<sec>
					<title>체형</title>
					<p>(12) 허리가 가늘다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(13) 골반이 좁다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(14) 가슴이 빈약하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(15) 등과 어깨이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(16) 가슴이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(17) 배(복부)다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(18) 엉덩이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(19) 가볍고 빠르다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(20) 얌전하고 조심성이 있다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(21) 희고 윤기 없다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(22) 연하고 부드럽다 ① 그렇다 ② 보통이다 ③ 아니다</p>
				</sec>
				<sec>
					<title>성격</title>
					<p>(23) 쉽게 결정한다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(24) 끈기있는 노력으로 일을 성취한다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(25) 흥분하지 않고 이성적으로 치밀하게 처리한다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(26) 남을 의식하지 않는다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(27) 활동적이며 적극적이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(28) 내성적이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(29) 꼼꼼하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(30) 속마음을 좀처럼 드러내지 않는다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(31) 자기 일보다 다른 사람의 일을 중히 여긴다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(32) 여성적이다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(33) 새로운 변화를 두려워한다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(34) 외모에 신경을 많이 쓴다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(35) 재물에 욕심이 많다 ① 그렇다 ② 보통이다 ③ 아니다</p>
				</sec>
				<sec>
					<title>증상</title>
					<p>(36) 음식물이 자주 위로 넘어온다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(37) 육식을 좋아한다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(38) 설사하는 경우가 매우 드물다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(39) 땀내면 상쾌하다 ① 그렇다 ② 보통이다 ③ 아니다</p>
					<p>(40) 손발이 차다 ① 그렇다 ② 보통이다 ③ 아니다</p>
				</sec>
				<p>검사결과 : □ 태양인 □ 소양인 □ 태음인 □ 소음인</p>
			</app>
		</app-group>
	</back>
</article>
