Technoplasmosis (n.) — A pervasive, adaptive process by which the architectures, affordances, and embedded value systems of technological artefacts permeate and recalibrate the cognitive schemas, evaluative criteria, and behavioral repertoires of their human users and host institutions. This phenomenon operates through continuous feedback between human practice and technological mediation, progressively aligning perception, judgment, and decision-making with the implicit imperatives of the technology itself.

In this paper we present the different steps and considerations for a qualitative evaluation of results in experimental designs. Methodological guidelines are offered for each stage of a study, from formulation of the research goals, through data collection and data analysis, to the interpretation of a potential effect of the intervention. In addition, there is ample attention for ensuring the rigor of the research. The presented guidelines are developed and illustrated using an empirical example, in which a constructivist grounded theory approach was applied to evaluate the effect of empathy prompts on the motivation to adhere to COVID-19 regulations.

Unlike previous research, we were unable to show that higher monetary incentives were more effective for increasing response rates. An AUD$20 unconditional incentive may be no more effective than a lesser amount for encouraging prostate cancer survivors to participate in research involving long questionnaires.

OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

Invented claim about social security fraud due to misunderstood statistics

The indicators in this dashboard are a compilation of existing indicators and results that UNICEF uses across multiple programming areas and aproaches. This list has been vetted and compiled by UNICEF's SBC team in collaboration with the sectors and cross-sectorial teams in the organization.

HealthMeasures consists of PROMIS, Neuro-QoL, ASCQ-Me, and NIH Toolbox. These four precise, flexible, and comprehensive measurement systems assess physical, mental, and social health, symptoms, well-being and life satisfaction; along with sensory, motor, and cognitive function.

100+ Items, 14 Mechanisms, 1 Journey Our goal with BCS is to offer a systematic yet adaptable methodology that makes it easier for product teams to capture the important details necessary for effective behavior change. To allow for that, we have chosen to focus on 14 Behavioral Science mechanisms as opposed to focusing on individual nudges which may or may not generalize to the unique context.

This article examines cognitive, affective, and behavioral measures of motivation and reviews their use throughout the discipline of experimental social psychology. We distinguish between two dimensions of motivation (outcome-focused motivation and process-focused motivation). We discuss circumstances under which measures may help distinguish between different dimensions of motivation, as well as circumstances under which measures may capture different dimensions of motivation in similar ways.

3:40 - 40 participants gives a 15% margin of error and 95% confidence level (binary metrics)

Big five trait scores for 307,313 people from many different countries.

And remember, keeping screeners under 12 questions is the magic number to prevent attrition.

“significant” p-value ≠ “significant” finding: The significance of statistical evidence for the true X (i.e., statistical significance of the p-value for the estimate of the true X) says absolutely nothing about the practical/scientific significance of the true X. That is, significance of evidence is not evidence of significance. Increasing your sample size in no way increases the practical/scientific significance of your practical/scientific hypothesis. “significant” p-value = “discernible” finding: The significance of statistical evidence for the true X does tell us how well the estimate can discern the true X. That is, significance of evidence is evidence of discernibility. Increasing your sample size does increase how well your finding can discern your practical/scientific hypothesis.

Opt-in samples are about half as accurate as probability-based panels

This is SenseMaker in its most simple form, usually structured to have an open (non-hypothesis) question (commonly referred to as a ‘prompting question’) to collect a micro-narrative at the start. This is then followed by a range of triads (triangles), dyads (sliders), stones canvases, free text questions and multiple choice questions. The reason or value for using Sensemaker: Open free text questions are used at the beginning as a way of scanning for diversity of narratives and experiences. This is a way to remain open to ‘unknown unknowns’. The narrative is then followed by signifier questions that allow the respondent to add layers of meaning and codification to the narrative (or experience) in order to allow for mixed methods analysis, to map and explore patterns.

Info, research, questionnaires/scales, info on application to specific topics

With this thread, you’ll learn 9 lessons: 1. The Data Quality Framework 2. The Participant Relationship 3. Perfect introductions 4. Instructions that work 5. Helpful signposting 6. An enjoyable experience 7. Getting feedback 8. Progressive piloting 9. Types of quality control

Qualtrics recommendation is to use the commitment request as that performed the best. However, the textual and factual attention checks also performed better than the control.

Our results showed that while most respondents understand why attention checks are conducted, a nonnegligible proportion of respondents evaluated them as controlling or annoying. Most respondents passed the attention check; however, among those who failed the test, 61% seem to have failed the task deliberately. These findings reinforce that noncompliance is a serious concern with attention check instruments. The results of our experiment showed that more respondents passed the attention check if a comprehensible reason was given.

A survey’s completion rate is one of its most important data quality measures. There are quite a few published studies examining web survey completion rate through experimental approaches. In this study, we expand the existing literature by examining the predictors of web survey completion rate using 25,080 real-world web surveys conducted by a single online panel. Our findings are consistent with the literature on some dimensions, such as finding a negative relationship between completion rate and survey length and question difficulty. Also, surveys without progress bars have higher completion rates than surveys with progress bars. This study also generates new insights into survey design features, such as the impact of the first question type and length on completion rate. More: https://twitter.com/nielsmede/status/1576234663341064192?s=20&t=kSwdGBBuVv1yiqo1lE4vbw

The Data Playbook is 120 exercises, games, scenarios, slides and checklists to assist you and your teams on your data journey. The social learning content is designed for teams to have discussions and activities across the data lifecycle in short 30 minute to 1 hour sessions.

0:00 Introduction 1:10 Principle 1: Identify the Objects 2:01 Principle 2: Identify the Numbers 3:04 Principle 3: Identify the Verbs 5:52 Principle 4: Set Bounds on Numbers 7:25 Principle 5: Build a Dashboard

Broadly, these feedback surveys can be categorised into five groups: the pointless; the self-important; the immoral; the demanding; and the downright weird:

EMERGE (Evidence-based Measures of Empowerment for Research on Gender Equality) is a project focused on gender equality and empowerment measures to monitor and evaluate health programs and to track progress on UN Sustainable Development Goal (SDG) 5: To Achieve Gender Equality and Empower All Girls. As reported by UN Women (2018), only 2 of the 14 SDG 5 indicators have accepted methodologies for measurement and data widely available. Of the remaining 12, 9 are indicators for which data are collected and available in only a limited number of countries. This assessment suggests notable measurement gaps in the state of gender equality and empowerment worldwide. EMERGE aims to improve the science of gender equality and empowerment measurement by identifying these gaps through the compilation and psychometric evaluation of available measures and supporting scientifically rigorous measure development research in India.

The Meta-Analysis Learning Information Center (MALIC) believes in equitably providing cutting-edge and up-to-date techniques in meta-analysis to researchers in the social sciences, particularly those in education and STEM education.

awful example of landing page!

For HCI survey research broadly, we recommend using a question similar to the first question in [2]’s measure (as quoted in [3]) – “Are you…?” with three response options: “man,” “woman,” “something else: specify [text box]” – and allowing respondents to choose multiple options. This question will not identify all trans participants [3], but is inclusive to non-binary and trans people and will identify gender at a level necessary for most HCI research. To reduce trolling, we recommend providing the fill-in-theblank text box as a second step only for those respondents who choose the “something else” option.

I love it that one of my students suggested we change the default “Other (please specify“) option to “Not Listed (please specify)“ in a demographic survey. Explicitly *not* “othering“ participants while still asking for the info we want. Any implied failure is on us, not them.

40 participants is an appropriate number for most quantitative studies, but there are cases where you can recruit fewer users.