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Changes for page Measuring Instruments

Last modified by Mathieu Jung-Muller on 2022/04/04 13:37
From version Icon 8.1 Icon
edited by Haoran Wang
on 2022/03/17 10:03
Change comment: There is no comment for this version
To version Icon 9.1
edited by Mathieu Jung-Muller
on 2022/04/04 13:37
Change comment: There is no comment for this version

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Author
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1 -XWiki.haoranwang
1 +XWiki.Mathieu
Content
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2 2  
3 3  Evaluation is an important part of product design and it can last from the beginning to the very end. In the Human-Computer Interaction field, product evaluation can help researchers to identify good and bad designs, determine how usable features are, discover new features that were neglected before, and compare design choices to assist us in making decisions.
4 4  
5 -= Frameworks=
6 -=== DECIDE Framework===
5 += Frameworks =
6 +
7 +== DECIDE Framework ==
8 +
7 7  **D**etermine the goals
8 8  
9 9  * What are the high-level goals of the evaluation?
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31 31  **E**valuate, analyze, interpret and present the data
32 32   How data is evaluated, analyzed, interpreted, and presented. To make the results reliable and valid, we have to consider biases, reliability, validity, scope, and ecological validity.
33 33  
34 -=== IMPACT Framework===
36 +== IMPACT Framework ==
35 35  **I**ntention: Clarify objectives and hypotheses/claims
36 36  **M**etrics & Measures: What, how and why
37 37  **P**eople: Target group & participants
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40 40  **T**echnologies: Hardware and software
41 41  
42 42  
43 -===Formative Evaluation===
45 +== Formative Evaluation ==
44 44  Focus on the various processes of the human-technology interaction
45 45  Derive open questions from the design specification.
46 46  
47 -===Summative Evaluation===
49 +== Summative Evaluation ==
48 48  Focuses on the overall effects of the human-technology interaction
49 49  Specify research questions and hypotheses based on claims.
50 50  
51 -===Data===
52 -====Qualitative Data====
53 -Explore, discover, instruct
54 54  
54 += Data =
55 +
56 +== Qualitative Data ==
57 +Explore, discover, instruct
55 55  * Understand and interpret interactions
56 56  * Gain insight into views and perspectives
57 57  * Open-ended, like interviews and participant observations
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58 58  * Try to identify patterns, features, themes
59 59  * Study groups tend to be smaller
60 60  
61 -====Quantitative Data====
64 +== Quantitative Data ==
62 62  Describe, explain, predict
63 63  * Measure outcomes, test hypotheses, and make predictions
64 64  * Precise measurements
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65 65  * Identify statistical relationships
66 66  * Larger number of participants
67 67  
68 -===Experimental Design===
69 -====Within-subjects====
71 += Experimental Design =
72 +== Within-subjects ==
70 70  Each participant, all conditions
71 71  * Few subjects needed
72 72  * Reduced variability
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73 73  * More statistical power
74 74  * Practice/fatigue effects
75 75  
76 -====Between-subjects====
79 +== Between-subjects ==
77 77  Each participant, one condition
78 78  * Simplicity
79 79  * Less chance of practice/fatigue effects