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mat:
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 left:5.99%;width:88.2%;height:10.0%'>Brown Bag Objectives</div>
 <div v:shape=3D"_x0000_s9219" class=3DB>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:23.0%;left:9.73%;width:94.56%;height:6.5%'><span style=3D'font-size:88=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.96%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:88%'>To introduce interested colleagues to conjoint </s=
pan></span><span
 style=3D'position:absolute;top:28.0%;left:9.73%;width:84.45%;height:6.5%'>=
<span
 style=3D'font-size:88%'>analysis.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:34.25%;left:9.73%;width:95.31%;height:6.5%'><span style=3D'font-size:8=
8%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.92%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:88%'>Identify situations where conjoint analysis can </=
span></span><span
 style=3D'position:absolute;top:39.0%;left:9.73%;width:84.45%;height:6.5%'>=
<span
 style=3D'font-size:88%'>facilitate attaining research objectives.&#13;</sp=
an></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:45.25%;left:9.73%;width:98.5%;height:6.5%'><span style=3D'font-size:88=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.8%;top:.22em;font-family:Wingdings;font-size:80%'>n</span=
></span><span
 style=3D'font-size:88%'>Identify how to get access to the SPSS Conjoint </=
span></span><span
 style=3D'position:absolute;top:50.24%;left:9.73%;width:84.45%;height:6.5%'=
><span
 style=3D'font-size:88%'>software at ISU.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:56.49%;left:9.73%;width:93.25%;height:6.5%'><span style=3D'font-size:8=
8%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-4.01%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:88%'>Walk through the basic steps of conducting a </spa=
n></span><span
 style=3D'position:absolute;top:61.5%;left:9.73%;width:84.45%;height:6.5%'>=
<span
 style=3D'font-size:88%'>conjoint analysis.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0";position:absolute;top:67.75%;
 left:5.99%;width:88.2%;height:6.5%'><span style=3D'position:absolute;top:0=
%;
 left:4.24%;width:95.75%;height:100.0%'><span style=3D'font-size:88%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-4.43%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:88%'>Discuss interpretation of conjoint results.&#13;</=
span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:74.0%;left:9.73%;width:87.26%;height:6.5%'><span style=3D'font-size:88=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-4.29%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:88%'>Identify useful citations to aid in your own </spa=
n></span><span
 style=3D'position:absolute;top:79.0%;left:9.73%;width:84.45%;height:6.5%'>=
<span
 style=3D'font-size:88%'>analyses.</span></span></div>
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%'>So,
 What the Heck Is Conjoint </span></span><span style=3D'position:absolute;
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1%'>Analysis?</span></span></div>
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 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
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7%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-6.17%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
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-size:
 57%'> is a set of statistical techniques </span></span><span style=3D'posi=
tion:
 absolute;top:26.25%;left:9.73%;width:58.42%;height:3.75%'><span
 style=3D'font-size:57%'>for measuring preferences about the attributes of =
</span></span><span
 style=3D'position:absolute;top:28.99%;left:9.73%;width:51.87%;height:3.75%=
'><span
 style=3D'font-size:57%'>some object (e.g., a product or service).&#13;</sp=
an></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:32.75%;left:9.73%;width:59.73%;height:3.75%'><span style=3D'font-size:=
57%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-6.26%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:57%'>We use conjoint analysis to answer questions such =
</span></span><span
 style=3D'position:absolute;top:35.5%;left:9.73%;width:53.55%;height:3.75%'=
><span
 style=3D'font-size:57%'>as, &#8220;What product attributes are important o=
r </span></span><span
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'><span
 style=3D'font-size:57%'>unimportant to respondents?&#8221; or<br>
  </span></span><span style=3D'position:absolute;top:41.25%;left:9.73%;
 width:60.11%;height:3.75%'><span style=3D'font-size:57%'>&#8220;What level=
s of
 the product attributes are the most </span></span><span style=3D'position:=
absolute;
 top:44.0%;left:9.73%;width:61.04%;height:3.75%'><span style=3D'font-size:5=
7%'>or
 least desirable ones in the respondent&#8217;s mind? or </span></span><span
 style=3D'position:absolute;top:47.0%;left:9.73%;width:57.67%;height:3.75%'=
><span
 style=3D'font-size:57%'>&#8220;What is the market share preference for lea=
ding </span></span><span
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'><span
 style=3D'font-size:57%'>competitor&#8217;s products versus competitive exi=
sting </span></span><span
 style=3D'position:absolute;top:52.5%;left:9.73%;width:51.87%;height:3.75%'=
><span
 style=3D'font-size:57%'>(or proposed) products?&#8221; &#13;</span></span>=
</div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
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57%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-6.66%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:57%'>In a medical context, a drug company might be </sp=
an></span><span
 style=3D'position:absolute;top:59.0%;left:9.73%;width:51.87%;height:3.75%'=
><span
 style=3D'font-size:57%'>interested in the utility of different feature </s=
pan></span><span
 style=3D'position:absolute;top:61.75%;left:9.73%;width:61.04%;height:3.75%=
'><span
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><span
 style=3D'font-size:57%'>side effects, price) for a drug under development.=
&#13;</span></span></div>
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57%'><span
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n:
 absolute;left:-6.62%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:57%'>The virtue of conjoint analysis is that it asks th=
e </span></span><span
 style=3D'position:absolute;top:71.0%;left:9.73%;width:60.48%;height:3.75%'=
><span
 style=3D'font-size:57%'>respondent to make choices in the same fashion as =
</span></span><span
 style=3D'position:absolute;top:74.0%;left:9.73%;width:57.86%;height:3.75%'=
><span
 style=3D'font-size:57%'>the respondent presumably does &#8211; by trading =
off </span></span><span
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'><span
 style=3D'font-size:57%'>features, one against another (e.g., price vs </sp=
an></span><span
 style=3D'position:absolute;top:79.75%;left:9.73%;width:51.87%;height:3.75%=
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 top:3.5%;left:.74%;width:98.68%;height:9.25%'><span style=3D'font-size:91%=
'>What
 Does Conjoint Offer That Is </span></span><span style=3D'position:absolute;
 top:12.25%;left:5.99%;width:88.2%;height:9.25%'><span style=3D'font-size:9=
1%'>&#8220;Better?&#8221;</span></span></div>
 <div v:shape=3D"_x0000_s11267" class=3DB>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:23.5%;left:9.73%;width:94.56%;height:3.75%'><span style=3D'font-size:5=
0%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.96%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:50%'>The identification of attribute importance has lon=
g been
 a consideration of great </span></span><span style=3D'position:absolute;
 top:26.25%;left:9.73%;width:91.57%;height:3.75%'><span style=3D'font-size:=
50%'>interest
 for social scientist and practitioner decision-makers across disciplines.&=
#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:29.75%;left:9.73%;width:95.31%;height:3.75%'><span style=3D'font-size:=
50%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.92%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:50%'>Traditionally, the way that researchers have ident=
ified
 attribute &#8220;importance&#8221; has </span></span><span style=3D'positi=
on:
 absolute;top:32.75%;left:9.73%;width:99.43%;height:3.75%'><span
 style=3D'font-size:50%'>been by either self-explicated methods or ordinary=
 least
 squares (OLS) regression to </span></span><span style=3D'position:absolute;
 top:35.5%;left:9.73%;width:100.0%;height:3.75%'><span style=3D'font-size:5=
0%'>identify
 the part-worths of specific attributes (the greater the part worth, the
 greater </span></span><span style=3D'position:absolute;top:38.25%;left:9.7=
3%;
 width:84.45%;height:3.75%'><span style=3D'font-size:50%'>the importance).&=
#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:42.0%;left:9.73%;width:84.45%;height:3.75%'><span style=3D'font-size:5=
0%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-4.43%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:50%'><i>Self-explicated methods work basically as follo=
ws:
 <br>
  </i></span></span><span style=3D'position:absolute;top:44.75%;left:9.73%;
 width:99.25%;height:3.75%'><span style=3D'font-size:50%'>First, respondents
 evaluate the levels of each attribute on a desirability scale (say 0-</spa=
n></span><span
 style=3D'position:absolute;top:47.5%;left:9.73%;width:95.69%;height:3.75%'=
><span
 style=3D'font-size:50%'>10) with the other attributes held constant where =
the
 most preferred level on the </span></span><span style=3D'position:absolute;
 top:50.5%;left:9.73%;width:101.12%;height:3.75%'><span style=3D'font-size:=
50%'>attribute
 may be assigned a value of 10 and the least preferred level assigned a val=
ue </span></span><span
 style=3D'position:absolute;top:53.25%;left:9.73%;width:84.45%;height:3.75%=
'><span
 style=3D'font-size:50%'>of 0.<br>
  </span></span><span style=3D'position:absolute;top:56.25%;left:9.73%;
 width:98.12%;height:3.75%'><span style=3D'font-size:50%'>Second, responden=
ts are
 asked to allocate 100 points across the attributes so as to </span></span>=
<span
 style=3D'position:absolute;top:59.0%;left:9.73%;width:84.45%;height:3.75%'=
><span
 style=3D'font-size:50%'>reflect their relative importance. <br>
  </span></span><span style=3D'position:absolute;top:61.75%;left:9.73%;
 width:93.63%;height:3.75%'><span style=3D'font-size:50%'>Third, part-worth=
s are
 obtained by multiplying their importance weight with the </span></span><sp=
an
 style=3D'position:absolute;top:64.75%;left:9.73%;width:84.45%;height:3.75%=
'><span
 style=3D'font-size:50%'>attribute-level desirability ratings (Green and
 Srinivasan 1990).&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:68.25%;left:9.73%;width:97.37%;height:3.75%'><span style=3D'font-size:=
50%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.84%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:50%'>OLS regression methods regress attribute ratings (=
say on
 a 1 to 7 scale) as IV&#8217;s on </span></span><span style=3D'position:abs=
olute;
 top:71.0%;left:9.73%;width:97.56%;height:3.75%'><span style=3D'font-size:5=
0%'>some
 DV. The highest standardized regression coefficients are often interpreted=
 as </span></span><span
 style=3D'position:absolute;top:74.0%;left:9.73%;width:84.45%;height:3.75%'=
><span
 style=3D'font-size:50%'>&#8220;importance&#8221; weights.&#13;</span></spa=
n></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:77.5%;left:9.73%;width:95.5%;height:3.75%'><span style=3D'font-size:50=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.92%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:50%'><b><i>Conjoint provides opportunities to attenuate=
 the
 limitations associated </i></b></span></span><span style=3D'position:absol=
ute;
 top:80.25%;left:9.73%;width:84.45%;height:3.75%'><span style=3D'font-size:=
50%'><b><i>with
 both self-explicated and regression techniques.&#13;</i></b></span></span>=
</div>
 <div style=3D'mso-line-spacing:"80 20 0";position:absolute;top:84.0%;left:=
5.99%;
 width:88.2%;height:3.75%'><span style=3D'position:absolute;top:0%;left:4.2=
4%;
 width:95.75%;height:100.0%'><span style=3D'font-size:50%;visibility:hidden=
'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-4.43%;font-family:Wingdings;font-size:80%'>n</span></span><=
span
 style=3D'font-size:50%;mso-special-format:lastCR;display:none'><b><i>&#13;=
</i></b></span></span></div>
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mat:
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 <div v:shape=3D"_x0000_s12290" class=3DT style=3D'position:absolute;top:7.=
5%;
 left:5.99%;width:88.2%;height:10.0%'>What Limitations?</div>
 <div v:shape=3D"_x0000_s12291" class=3DHB>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:23.25%;left:7.3%;width:41.01%;height:4.25%'><span style=3D'font-size:6=
4%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-9.13%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:64%'>OLS regression has a set of </span></span><span
 style=3D'position:absolute;top:26.5%;left:7.3%;width:49.06%;height:4.25%'>=
<span
 style=3D'font-size:64%'>assumptions that may not be valid in </span></span=
><span
 style=3D'position:absolute;top:29.75%;left:7.3%;width:48.12%;height:4.25%'=
><span
 style=3D'font-size:64%'>many research settings. Specifically, </span></spa=
n><span
 style=3D'position:absolute;top:33.0%;left:7.3%;width:45.69%;height:4.25%'>=
<span
 style=3D'font-size:64%'>potential nonlinear and interaction </span></span>=
<span
 style=3D'position:absolute;top:36.0%;left:7.3%;width:46.62%;height:4.25%'>=
<span
 style=3D'font-size:64%'>effects can make the interpretation </span></span>=
<span
 style=3D'position:absolute;top:39.25%;left:7.3%;width:41.01%;height:4.25%'=
><span
 style=3D'font-size:64%'>of regression coefficients as </span></span><span
 style=3D'position:absolute;top:42.5%;left:7.3%;width:42.13%;height:4.25%'>=
<span
 style=3D'font-size:64%'>importance weights invalid (see </span></span><span
 style=3D'position:absolute;top:45.75%;left:7.3%;width:48.12%;height:4.25%'=
><span
 style=3D'font-size:64%'>Taylor 1997, 2001 for discussions of </span></span=
><span
 style=3D'position:absolute;top:48.75%;left:7.3%;width:41.01%;height:4.25%'=
><span
 style=3D'font-size:64%'>this issue).&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:52.75%;left:7.3%;width:41.01%;height:4.25%'><span style=3D'font-size:6=
4%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-9.13%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:64%'>Self-explicated techniques also </span></span><span
 style=3D'position:absolute;top:56.0%;left:7.3%;width:46.44%;height:4.25%'>=
<span
 style=3D'font-size:64%'>suffer from a number of limitations </span></span>=
<span
 style=3D'position:absolute;top:59.25%;left:7.3%;width:41.01%;height:4.25%'=
><span
 style=3D'font-size:64%'>(see Table 1).&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:63.24%;left:7.3%;width:47.56%;height:4.25%'><span style=3D'font-size:6=
4%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-7.87%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:64%'>Most of the reasons discussed favor </span></span>=
<span
 style=3D'position:absolute;top:66.5%;left:7.3%;width:46.62%;height:4.25%'>=
<span
 style=3D'font-size:64%'>conjoint measurement especially in </span></span><=
span
 style=3D'position:absolute;top:69.75%;left:7.3%;width:47.19%;height:4.25%'=
><span
 style=3D'font-size:64%'>terms of predictive validity, as least </span></sp=
an><span
 style=3D'position:absolute;top:72.75%;left:7.3%;width:47.94%;height:4.25%'=
><span
 style=3D'font-size:64%'>as long as there are a small number </span></span>=
<span
 style=3D'position:absolute;top:76.0%;left:7.3%;width:44.38%;height:4.25%'>=
<span
 style=3D'font-size:64%'>of attributes (</span><span style=3D'font-family:T=
ahoma;
 mso-bidi-font-family:Tahoma;font-size:64%'>&#8804; 6). However, this </spa=
n></span><span
 style=3D'position:absolute;top:79.25%;left:7.3%;width:41.01%;height:4.25%'=
><span
 style=3D'mso-bidi-font-family:Tahoma;font-size:64%'>remains an open issue =
in the
 </span></span><span style=3D'position:absolute;top:82.5%;left:7.3%;width:4=
1.01%;
 height:4.25%'><span style=3D'mso-bidi-font-family:Tahoma;font-size:64%'>li=
terature.</span></span></div>
 </div>
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%'><![endif]><v:shapetype
  id=3D"_x0000_t202" coordsize=3D"21600,21600" o:spt=3D"202" path=3D"m,l,21=
600r21600,l21600,xe">
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none;
  v-text-anchor:top-baseline' filled=3D"f" fillcolor=3D"#f30 [4]" stroked=
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  <v:fill color2=3D"#720000 [0]"/>
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 <div v:shape=3D"_x0000_s12294" class=3DO style=3D'mso-char-wrap:0;mso-kins=
oku-overflow:
 0'><span style=3D'position:absolute;top:77.25%;left:53.55%;width:44.56%;
 height:3.25%'><span style=3D'font-family:Tahoma;font-size:78%;color:#FFFFC=
C;
 mso-color-index:3'>Please note that most of the advantages of<br>
  </span></span><span style=3D'position:absolute;top:80.5%;left:53.55%;
 width:41.76%;height:3.25%'><span style=3D'font-family:Tahoma;font-size:78%;
 color:#FFFFCC;mso-color-index:3'>conjoint relate to statistical issues,
 while<br>
  </span></span><span style=3D'position:absolute;top:83.5%;left:53.55%;
 width:43.44%;height:3.25%'><span style=3D'font-family:Tahoma;font-size:78%;
 color:#FFFFCC;mso-color-index:3'>most of the advantages of self-explicated
 <br>
  </span></span><span style=3D'position:absolute;top:86.5%;left:53.55%;
 width:40.82%;height:3.25%'><span style=3D'font-family:Tahoma;font-size:78%;
 color:#FFFFCC;mso-color-index:3'>methods involve issues of convenience.</s=
pan></span></div>
 <div v:shape=3D"_x0000_s12295" class=3DO style=3D'mso-char-wrap:0;mso-kins=
oku-overflow:
 0;position:absolute;top:69.0%;left:53.37%;width:37.45%;height:3.0%'><span
 style=3D'font-family:Tahoma;font-size:67%'>Source: Sattler and Hensel-Born=
er
 (2001).</span></div>
</p:slide></div>

</body>

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 <div v:shape=3D"_x0000_s15362" class=3DT style=3D'position:absolute;top:7.=
5%;
 left:5.99%;width:88.2%;height:10.0%'>Forms of Conjoint Analysis</div>
 <div v:shape=3D"_x0000_s15363" class=3DB>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:23.25%;left:9.73%;width:88.57%;height:4.25%'><span style=3D'font-size:=
56%;
 color:#FFFFCC;mso-color-index:3'><span style=3D'mso-special-format:bullet;
 color:#FFCC66;mso-color-index:6;position:absolute;left:-4.22%;top:.22em;
 font-family:Wingdings;font-size:80%'>n</span></span><span style=3D'font-si=
ze:
 56%;color:#FFFFCC;mso-color-index:3'><b>Adaptive conjoint</b></span><span
 style=3D'font-size:56%'> studies typically present subjects with pairs of =
</span></span><span
 style=3D'position:absolute;top:26.5%;left:9.73%;width:96.62%;height:4.25%'=
><span
 style=3D'font-size:56%'>alternatives, often based on only a subset of
 attributes. The subject thus </span></span><span style=3D'position:absolut=
e;
 top:29.75%;left:9.73%;width:101.12%;height:4.25%'><span style=3D'font-size=
:56%'>faces
 a relatively simple task of choosing the preferred alternative described <=
/span></span><span
 style=3D'position:absolute;top:33.0%;left:9.73%;width:94.75%;height:4.25%'=
><span
 style=3D'font-size:56%'>by a limited set of attributes. The attributes and
 levels presented in the </span></span><span style=3D'position:absolute;
 top:36.0%;left:9.73%;width:100.18%;height:4.25%'><span style=3D'font-size:=
56%'>pairings
 are based on initial attribute ratings and previous responses by the </spa=
n></span><span
 style=3D'position:absolute;top:39.25%;left:9.73%;width:100.37%;height:4.25=
%'><span
 style=3D'font-size:56%'>subject (thus the &#8220;adaptive&#8221; nature of=
 the
 study). Note that respondents do </span></span><span style=3D'position:abs=
olute;
 top:42.5%;left:9.73%;width:84.45%;height:4.25%'><span style=3D'font-size:5=
6%'>not
 view complete product descriptions in this method.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:46.5%;left:9.73%;width:86.89%;height:4.25%'><span style=3D'font-size:5=
6%;
 color:#FFFFCC;mso-color-index:3'><span style=3D'mso-special-format:bullet;
 color:#FFCC66;mso-color-index:6;position:absolute;left:-4.31%;top:.22em;
 font-family:Wingdings;font-size:80%'>n</span></span><span style=3D'font-si=
ze:
 56%;color:#FFFFCC;mso-color-index:3'><b>Choice-based conjoint</b></span><s=
pan
 style=3D'font-size:56%'> presents a pair, or larger set of product </span>=
</span><span
 style=3D'position:absolute;top:49.75%;left:9.73%;width:97.37%;height:4.25%=
'><span
 style=3D'font-size:56%'>alternatives to a subject, who chooses one product=
 (or
 no products) from </span></span><span style=3D'position:absolute;top:52.75=
%;
 left:9.73%;width:100.37%;height:4.25%'><span style=3D'font-size:56%'>the s=
et.
 This process more closely matches actual purchase situations. Here </span>=
</span><span
 style=3D'position:absolute;top:56.0%;left:9.73%;width:95.5%;height:4.25%'>=
<span
 style=3D'font-size:56%'>again, different subjects tend to see different pr=
oduct
 sets if there are a </span></span><span style=3D'position:absolute;top:59.=
25%;
 left:9.73%;width:96.06%;height:4.25%'><span style=3D'font-size:56%'>large =
number
 of attributes, so the analysis is performed at the subgroup </span></span>=
<span
 style=3D'position:absolute;top:62.5%;left:9.73%;width:84.45%;height:4.25%'=
><span
 style=3D'font-size:56%'>and not individual level.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:66.5%;left:9.73%;width:95.5%;height:4.25%'><span style=3D'font-size:56=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.92%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:56%'>In </span><span style=3D'font-size:56%;color:#FFFF=
CC;
 mso-color-index:3'><b>full profile conjoint</b></span><span style=3D'font-=
size:
 56%'> the subject ranks or rates a set of alternatives, </span></span><span
 style=3D'position:absolute;top:69.75%;left:9.73%;width:91.94%;height:4.25%=
'><span
 style=3D'font-size:56%'>each described by a full attribute set (presenting=
 a
 full profile of each </span></span><span style=3D'position:absolute;top:72=
.75%;
 left:9.73%;width:84.45%;height:4.25%'><span style=3D'font-size:56%'>altern=
ative).
 </span><span style=3D'font-size:56%'><b><i><u>SPSS currently supports ONLY=
 this
 method.&#13;</u></i></b></span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:76.75%;left:9.73%;width:90.44%;height:4.25%'><span style=3D'font-size:=
56%;
 color:#FFFFCC;mso-color-index:3'><span style=3D'mso-special-format:bullet;
 color:#FFCC66;mso-color-index:6;position:absolute;left:-4.14%;top:.22em;
 font-family:Wingdings;font-size:80%'>n</span></span><span style=3D'font-si=
ze:
 56%;color:#FFFFCC;mso-color-index:3'><b>Hierarchical Bayesian models</b></=
span><span
 style=3D'font-size:56%'> are relatively recent, which permit </span></span=
><span
 style=3D'position:absolute;top:80.0%;left:9.73%;width:91.38%;height:4.25%'=
><span
 style=3D'font-size:56%'>individual subject estimates, treating subject as a
 random factor. We </span></span><span style=3D'position:absolute;top:83.25=
%;
 left:9.73%;width:84.45%;height:4.25%'><span style=3D'font-size:56%'>haven&=
#8217;t
 looked at this yet.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0";position:absolute;top:87.25%;
 left:5.99%;width:88.2%;height:4.25%'><span style=3D'position:absolute;
 top:0%;left:4.24%;width:95.75%;height:100.0%'><span style=3D'font-size:56%;
 visibility:hidden'><span style=3D'mso-special-format:bullet;color:#FFCC66;
 mso-color-index:6;position:absolute;left:-4.43%;font-family:Wingdings;
 font-size:80%'>n</span></span><span style=3D'font-size:56%;mso-special-for=
mat:
 lastCR;display:none'>&#13;</span></span></div>
 </div>
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ly:Tahoma;
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3;</span></div>
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75%;
 left:0%;width:100.0%;height:9.25%'><span style=3D'font-size:91%'>Variants =
of the
 Full Profile Method</span></div>
 <div v:shape=3D"_x0000_s16387" class=3DB>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:23.25%;left:9.73%;width:86.51%;height:4.75%'><span style=3D'font-size:=
63%;
 color:#FFFFCC;mso-color-index:3'><span style=3D'mso-special-format:bullet;
 color:#FFCC66;mso-color-index:6;position:absolute;left:-4.32%;top:.22em;
 font-family:Wingdings;font-size:80%'>n</span></span><span style=3D'font-si=
ze:
 63%;color:#FFFFCC;mso-color-index:3'><b>Method 1:</b></span><span
 style=3D'font-size:63%'> </span><span style=3D'font-size:63%'><i>Give them=
 the
 full treatment!</i></span><span style=3D'font-size:63%'> If the number of =
</span></span><span
 style=3D'position:absolute;top:26.75%;left:9.73%;width:93.25%;height:4.75%=
'><span
 style=3D'font-size:63%'>attributes and attribute levels are not too large,=
 it is
 possible to </span></span><span style=3D'position:absolute;top:30.25%;
 left:9.73%;width:98.12%;height:4.75%'><span style=3D'font-size:63%'>present
 respondents with all the product alternatives and have the </span></span><=
span
 style=3D'position:absolute;top:34.0%;left:9.73%;width:96.06%;height:4.75%'=
><span
 style=3D'font-size:63%'>respondent rank them. However, this is cumbersome,
 difficult for </span></span><span style=3D'position:absolute;top:37.5%;
 left:9.73%;width:95.13%;height:4.75%'><span style=3D'font-size:63%'>respon=
dents,
 and requires a very small number of attributes and </span></span><span
 style=3D'position:absolute;top:41.0%;left:9.73%;width:84.45%;height:4.75%'=
><span
 style=3D'font-size:63%'>levels.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:45.5%;left:9.73%;width:95.31%;height:4.75%'><span style=3D'font-size:6=
3%;
 color:#FFFFCC;mso-color-index:3'><span style=3D'mso-special-format:bullet;
 color:#FFCC66;mso-color-index:6;position:absolute;left:-3.92%;top:.22em;
 font-family:Wingdings;font-size:80%'>n</span></span><span style=3D'font-si=
ze:
 63%;color:#FFFFCC;mso-color-index:3'><b>Method 2:</b></span><span
 style=3D'font-size:63%'> </span><span style=3D'font-size:63%'><i>Fractional
 Factorial Designs (FFD)</i></span><span style=3D'font-size:63%'>.<span
 style=3D'mso-spacerun:yes'>&nbsp; </span>This is way cool! </span></span><=
span
 style=3D'position:absolute;top:49.0%;left:9.73%;width:96.06%;height:4.75%'=
><span
 style=3D'font-size:63%'>Instead of presenting respondents with ALL possible
 alternatives, </span></span><span style=3D'position:absolute;top:52.75%;
 left:9.73%;width:84.45%;height:4.75%'><span style=3D'font-size:63%'>you ca=
n use
 FFDs to present s suitably chosen fraction of </span></span><span
 style=3D'position:absolute;top:56.25%;left:9.73%;width:94.38%;height:4.75%=
'><span
 style=3D'font-size:63%'>alternatives. SPSS Conjoint will generate orthogon=
al
 main-effect </span></span><span style=3D'position:absolute;top:59.75%;
 left:9.73%;width:84.45%;height:4.75%'><span style=3D'font-size:63%'>FFDs f=
or
 this purpose.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:64.25%;left:9.73%;width:96.62%;height:4.75%'><span style=3D'font-size:=
63%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.87%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:63%'>Please note that with FFDs you can only assess main
 effects (i.e., </span></span><span style=3D'position:absolute;top:67.75%;
 left:9.73%;width:100.74%;height:4.75%'><span style=3D'font-size:63%'>you c=
annot
 identify interactions &#8211; price for example is often a culprit </span>=
</span><span
 style=3D'position:absolute;top:71.25%;left:9.73%;width:84.45%;height:4.75%=
'><span
 style=3D'font-size:63%'>that interacts with quality in this regard).&#13;<=
/span></span></div>
 <div style=3D'mso-line-spacing:"80 20 0"'><span style=3D'position:absolute;
 top:75.75%;left:9.73%;width:96.25%;height:4.75%'><span style=3D'font-size:=
63%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-3.89%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:63%'>So, if you can live with this, you are ready to mo=
ve on
 to figuring </span></span><span style=3D'position:absolute;top:79.25%;
 left:9.73%;width:84.45%;height:4.75%'><span style=3D'font-size:63%'>out HO=
W to
 run a conjoint analysis using SPSS.</span></span></div>
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5%;
 left:5.99%;width:88.2%;height:10.0%'>So, How Do You Do It?</div>
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 <div style=3D'mso-line-spacing:"90 20 0"'><span style=3D'position:absolute;
 top:23.5%;left:9.73%;width:41.38%;height:5.5%'><span style=3D'font-size:86=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-9.04%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:86%'>We will not try to cover </span></span><span
 style=3D'position:absolute;top:28.24%;left:9.73%;width:44.75%;height:5.5%'=
><span
 style=3D'font-size:86%'>every theoretical detail in </span></span><span
 style=3D'position:absolute;top:33.0%;left:9.73%;width:38.57%;height:5.5%'>=
<span
 style=3D'font-size:86%'>today&#8217;s introductory </span></span><span
 style=3D'position:absolute;top:38.0%;left:9.73%;width:38.57%;height:5.5%'>=
<span
 style=3D'font-size:86%'>presentation.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"90 20 0"'><span style=3D'position:absolute;
 top:43.75%;left:9.73%;width:40.44%;height:5.5%'><span style=3D'font-size:8=
6%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-9.25%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:86%'>Please see Gustafsson, </span></span><span
 style=3D'position:absolute;top:48.5%;left:9.73%;width:38.76%;height:5.5%'>=
<span
 style=3D'font-size:86%'>Herrmann, and Huber </span></span><span
 style=3D'position:absolute;top:53.25%;left:9.73%;width:38.57%;height:5.5%'=
><span
 style=3D'font-size:86%'>(2001) for a detailed </span></span><span
 style=3D'position:absolute;top:58.25%;left:9.73%;width:39.32%;height:5.5%'=
><span
 style=3D'font-size:86%'>treatise covering most </span></span><span
 style=3D'position:absolute;top:63.0%;left:9.73%;width:44.56%;height:5.5%'>=
<span
 style=3D'font-size:86%'>relevant issues related to </span></span><span
 style=3D'position:absolute;top:67.75%;left:9.73%;width:38.57%;height:5.5%'=
><span
 style=3D'font-size:86%'>this methodology.&#13;</span></span></div>
 <div style=3D'mso-line-spacing:"90 20 0"'><span style=3D'position:absolute;
 top:73.5%;left:9.73%;width:40.44%;height:5.5%'><span style=3D'font-size:86=
%'><span
 style=3D'mso-special-format:bullet;color:#FFCC66;mso-color-index:6;positio=
n:
 absolute;left:-9.25%;top:.22em;font-family:Wingdings;font-size:80%'>n</spa=
n></span><span
 style=3D'font-size:86%'>Springer, ISBN: 3-540-</span></span><span
 style=3D'position:absolute;top:78.25%;left:9.73%;width:38.57%;height:5.5%'=
><span
 style=3D'font-size:86%'>42323-0.</span></span></div>
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