XWiki - Foundation.Human Factors.Measuring Instruments

For the evaluation of a prototype, there are several frameworks that can be followed, starting with DECIDE[1]. Decide stands for:

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**D**etermine the goals

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**E**xplore the questions

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**C**hoose evaluation approach and methods

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**I**dentify practical issues

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**D**ecide about ethical issues

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**E**valuate, analyze, interpret, present data

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First, we would have to determine the high-level goals for the study and the motivation behind them, since they can influence how we approach it. Then, we choose the evaluation approach, the methods that will be used, whether these are based on quantitative or qualitative data, and the process of data collecting, analysis, and presentation. At the same time, any practical issues, such as participants, budget, or schedule, are identified and a pilot study is performed if needed. It is important to adhere to any ethical procedures that are in place, to ensure the participant knows their rights and is protected. Finally, the evaluation of the data takes place, where it is determined whether the results are reliable, valid, without bias, unrelated to the environment and can generalize well.

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Another framework used is IMPACT[2]:

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== IMPACT ==

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**I**ntention: Present the objectives and claims

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**M**easures and metrics: "What, how, and why"

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**P**eople: Define the participants

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**A**ctivities: Use cases in activities

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**C**ontext: Social, ethical, physical, etc. environment definition

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**T**echnologies: Hardware and software

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= Evaluation methods =

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== Formative evaluation ==

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- Open-ended evaluation of the design

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- E.g. How will the users respond to the new design?

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== Summative evaluation ==

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- Focus on the overall effect

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- Summarizes if the objective is reached

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- E.g. Are the participants happier when working with design X in comparison to design Y?

== Data ==

- Qualitative: Explore, discover, instruct

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- Quantitative: Describe, explain, predict

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- Subjective quantitative

== Statistics ==

- Descriptive: Describe the dataset, e.g. mean time on task

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- Inferential: Using a sample to infer about a population, e.g. predicted mean time on task based on user characteristics.

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== Experiment Design: Conditions ==

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=== Within Subjects (Repeated Measures) ===

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Each participant is subjected to all the test conditions.

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Fewer subjects needed and reduces variance in the Can be difficult to setup due to subjects fatiguing, learning about the setup or simply not having enough time.

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=== Between Subjects (Between Groups) ===

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One subject undergoes only one test. Simple to execute, but results in significant variance due to inter-subject differences in characteristics.

== Lenses ==

- Lense means to take different perspectives looking at your system

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