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        <title>COMMunication&lt;br /&gt;RESearch.NET - c:mrm:2026:schedule</title>
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       <dc:date>2026-04-17T09:02:20+00:00</dc:date>
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        <dc:date>2026-03-19T12:03:53+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>additional_lecture_note_week03</title>
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        <description>variables (변인)

	*  변인은 측정할 수 있는 아이디어, 개념, 구성을 말한다. 
	*  연구에 포함되는 아이디어, 개념, 구성을 흔히 concepts 라고 부른다. 
	*  이를 설명하고 조사한 내용을 흔히 conceptualization이라고 (개념화라고) 부른다.</description>
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        <title>building_hypotheses</title>
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        <description>Research Idea:

	*  How relating other influencers affect users&#039; experiences?
		*  search results

	*  How relating other influencers affect users&#039; experience of youtube viewing
		*  search results

	*  watching influencers&#039; content and users&#039; behavior change
		*  search results
		*  The effect of social media influencers&#039; on teenagers Behavior: an empirical study using cognitive map technique
			*  not a good example of building hypotheses</description>
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        <title>finding_hypothesis</title>
        <link>http://www.commres.net/c/mrm/2026/schedule/finding_hypothesis?rev=1773888344&amp;do=diff</link>
        <description>김혜인

저는 게임과 데이터 분야에 관심이 있습니다. 본 과제에서는 게임이 사람에게 어떤 영향을 미치는지에 대해 조사하였습니다.

게이미피케이션(Gamification)을 적용한 수업의 학습몰입 및 수업참여도 분석</description>
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        <dc:format>text/html</dc:format>
        <dc:date>2026-03-31T11:06:53+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>lecture_note_w04</title>
        <link>http://www.commres.net/c/mrm/2026/schedule/lecture_note_w04?rev=1774955213&amp;do=diff</link>
        <description>Is 7 point scale like Likert number-measured?

	*  숫자로 측정된 변인의 가장 큰 특징은 그것이 INTERVAL 이라는 점입니다. 
	*  인터벌은 . . . . 

Credibility of News Stories

What is credibility? 
g32 . . . . .



뉴스기사의 신뢰도에 관한 질문을 만들어 봅니다. $ \bar{X}_{self} &lt; \bar{X}_{others} $$ \bar{X}_{self}-\bar{X}_{local} &lt; \bar{X}_{self} - \bar{X}_{nation} $$ \bar{X}_{\text{the third person effect not perceived}} &lt; \bar{X}_{\text{perceived}} $$ \bar{X}_{\text{non perceiver}} &gt; \bar{X}_{\text{negative perceiver}} $…</description>
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        <dc:date>2026-04-16T01:16:24+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>w07_note</title>
        <link>http://www.commres.net/c/mrm/2026/schedule/w07_note?rev=1776302184&amp;do=diff</link>
        <description>*  R
		*  pnorm, qnorm, rnorm, dnorm
		*  pt, qt, rt, dt
		*   pf, qf, rf, df  나중에

	*  variance (var())
		*  SS / df

	*  standard deviation (sd())
		*  sqrt(SS/df)
		*  68, 95, 99 % 

	*  sampling distribution 앞부분 pnorm, qnorm 만 보고 t-test summary 문서를 볼 것 (동일)</description>
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