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Changes for page Measuring Instruments

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

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Author
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1 -XWiki.Mathieu
1 +XWiki.haoranwang
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 -
7 -== DECIDE Framework ==
8 -
5 += Frameworks=
6 +=== DECIDE Framework===
9 9  **D**etermine the goals
10 10  
11 11  * What are the high-level goals of the evaluation?
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33 33  **E**valuate, analyze, interpret and present the data
34 34   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.
35 35  
36 -== IMPACT Framework ==
34 +=== IMPACT Framework===
37 37  **I**ntention: Clarify objectives and hypotheses/claims
38 38  **M**etrics & Measures: What, how and why
39 39  **P**eople: Target group & participants
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42 42  **T**echnologies: Hardware and software
43 43  
44 44  
45 -== Formative Evaluation ==
43 +===Formative Evaluation===
46 46  Focus on the various processes of the human-technology interaction
47 47  Derive open questions from the design specification.
48 48  
49 -== Summative Evaluation ==
47 +===Summative Evaluation===
50 50  Focuses on the overall effects of the human-technology interaction
51 51  Specify research questions and hypotheses based on claims.
52 52  
53 -
54 -= Data =
55 -
56 -== Qualitative Data ==
51 +===Data===
52 +====Qualitative Data====
57 57  Explore, discover, instruct
54 +
58 58  * Understand and interpret interactions
59 59  * Gain insight into views and perspectives
60 60  * Open-ended, like interviews and participant observations
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61 61  * Try to identify patterns, features, themes
62 62  * Study groups tend to be smaller
63 63  
64 -== Quantitative Data ==
61 +====Quantitative Data====
65 65  Describe, explain, predict
66 66  * Measure outcomes, test hypotheses, and make predictions
67 67  * Precise measurements
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68 68  * Identify statistical relationships
69 69  * Larger number of participants
70 70  
71 -= Experimental Design =
72 -== Within-subjects ==
68 +===Experimental Design===
69 +====Within-subjects====
73 73  Each participant, all conditions
74 74  * Few subjects needed
75 75  * Reduced variability
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76 76  * More statistical power
77 77  * Practice/fatigue effects
78 78  
79 -== Between-subjects ==
76 +====Between-subjects====
80 80  Each participant, one condition
81 81  * Simplicity
82 82  * Less chance of practice/fatigue effects