Pillar stringclasses 5
values | Sub Pillar stringlengths 6 33 | Quality criteria to meet stringlengths 31 429 |
|---|---|---|
Design & Planning | Clear objectives | The assessment scope, objectives and/or sub-objectives (topics to assess, geographical areas, population groups and sectors) are clearly expressed |
Design & Planning | Clear objectives | The assessment objectives include cross cutting thematics (sex, age, protection, environment, disabilities, etc.) |
Design & Planning | Clear objectives | The assessment scope, objectives and/or sub-objectives take into account existing information (e.g. assessment registry, initial secondary data review, operational learning from past assessments) |
Design & Planning | Clear objectives | The levels of analysis to cover are clearly identified (e.g. descriptive, explanatory, interpretive, anticipatory, prescriptive) |
Design & Planning | Clear objectives | The decisions, response phase or funding documents (e.g. DREF, Emergency Appeal, FAD, etc.) the assessment will inform are clearly expressed |
Design & Planning | Key stakeholder's involvement | Relevant decisions makers, subject matter, sector and context experts were involved in defining the assessment objectives and the information needs |
Design & Planning | Analysis Framework | The assessment's data collection and analysis are guided by the IFRC analysis framework |
Design & Planning | Data collection and analysis plan | An analysis and/or a data collection plan clearly identifies the information needs or indicators to assess/measure, how to obtain the information and where to get the information from |
Design & Planning | Quality assurance | Measures are in place to mitigate the impact of cognitive baises on the quality of the analysis (e.g. use of different structured analytical techniques, diversity of analytical lenses, peer reviews, team members complementarity, etc.) |
Design & Planning | Risk mitigation | The assessment design is based on a "do no harm" analysis and considers socio cultural norms, preferred communication channels, potential safety risks to affected populations/enumerators, PSEA and identifies relevant mitigation measures. |
Data Collation & Collection | Source diversity | Multiple data collection techniques and/or sources are used to gather data (e.g. secondary data review, Key Informant Interviews, Direct Observations, Household Survey, Satelitte imagery, fly over, expert judgement elicitation, etc.) |
Data Collation & Collection | Data diversity | Multiple data types are collected/collated (e.g. Qualitative, quantitative, audio, video, photoes, satelitte imagery, etc.) |
Data Collation & Collection | Source diversity | Affected people are directly consulted to assess humanitarian issues/unmet needs (rather than through their representatives or subject matter experts). |
Data Collation & Collection | Quality assurance | Data collectors (secondary data review officers, field assessment teams, etc.) are trained and guidance/instructions are in place to minimize error/biases (e.g. training on assessment objectives, criteria for selection of relevant documents, training on interview techniques, regular debriefing with assessment teams, i... |
Data Collation & Collection | Relevance | The covered geographical areas, population groups and sectors match the objectives and scope of the assessment |
Data Collation & Collection | Qualilty assurance | Strategies and measures are in place to prevent and treat basic data errors, safely store/share data and metadata |
Data Collation & Collection | Ethics | Where relevant, data protection measures are in place to protect the identity of respondents and handle sensitive data (e.g. informed consent, safe storage, password protection, data anonymization, etc.) |
Data Collation & Collection | Relevance | Primary and/or secondary data on the operationnal environment is collected (Context, shocks, displacement, casualties, humanitarian access) |
Data Collation & Collection | Relevance | Primary and/or secondary data on humanitarian consequences is collected (impact, humanitarian conditions, key priorities) |
Data Collation & Collection | Relevance | Primary and/or secondary data on ongoing response and capacities is collected |
Data Preparation & Exploration | Structured data | All data is organized and grouped based on the categories of the analysis framework and/or the analysis and data collection plan |
Data Preparation & Exploration | Exploratory analysis | Exploratory visualizations and/or basic analyses are conducted for each categories of the analysis framework and/or the analysis and data collection plan |
Data Preparation & Exploration | Information gaps | Information gaps limiting the scope and feasibility of the analysis are assessed and documented |
Data Preparation & Exploration | Key assumptions | Key assumptions are identified and strategies are in place to mitigate their impact (e.g. further research on specific topics, section on further information needs, communicating clearly on uncertainty and confidence levels, section on limitations, etc.) |
Data Preparation & Exploration | Documentation | Manipulations, changes to the original data and discarded data/information are documented and monitored |
Data Analysis & Sense Making | Descriptive analysis | Issues/unmet needs are detailed |
Data Analysis & Sense Making | Descriptive analysis | Issues/unmet needs are prioritized/ranked |
Data Analysis & Sense Making | Descriptive analysis | Current issues/unmet needs are compared to earlier/pre crisis situation/conditions to establish trends (speed, rate and direction of change) |
Data Analysis & Sense Making | Explanatory analysis | Main drivers, causes and/or underlying factors behind issues/unmet needs are detailed and prioritized/ranked |
Data Analysis & Sense Making | Interpretive analysis | The severity of some/all issues/unmet needs is detailed and prioritized/ranked |
Data Analysis & Sense Making | Anticipatory analysis | Hazard, threats and risks severity are detailed and prioritized/ranked |
Data Analysis & Sense Making | Anticipatory analysis | Multiple crisis scenarios and/or needs forecasts are detailed and prioritized/ranked |
Data Analysis & Sense Making | Prescriptive analysis | Response objectives and programmes are detailed and address the priority issues/unmet needs |
Data Analysis & Sense Making | Prescriptive analysis | Response options and modalities are assessed and prioritized/ranked |
Data Analysis & Sense Making | Prescriptive analysis | Operational constraints and/or opportunities are assessed/discussed |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population in the assessed geographical areas is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population affected by the shock and/or living in the affected areas is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The humanitarian profile of the entire crisis and/or the assessed geographical area is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population in need is identified/estimated and broken down by geographical areas and/or affected groups |
Data Analysis & Sense Making | Humanitarian Population Figures | The entire population count is broken down by severity class (e.g. none, of concern, in moderate need, in severe needs, in critical needs), by geographical areas and/or affected groups |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population in need facing humanitarian access barriers is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population vulnerable or at risk of future threats or hazards is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population reached by on going assistance is identified/estimated |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population not reached or insufficiently reached by assistance is identified/estimated (response gap) |
Data Analysis & Sense Making | Humanitarian Population Figures | The total population to target is identified/estimated |
Data Analysis & Sense Making | Analytical Rigor | Comparisons between geographical areas and/or population groups (sex and age, affected groups, vulnerable groups, groups with specific needs, etc.) and/or sectors are systematic and consistent across the assessment |
Data Analysis & Sense Making | Analytical Rigor | The assessment triangulates/corroborates the information obtained from different sources, data collection techniques and/or data types. |
Data Analysis & Sense Making | Analytical Rigor | The amount, type of evidence and sources behind each results/findings is clearly expressed, assessed and discussed (information gaps)
|
Data Analysis & Sense Making | Analytical Rigor | Alternative views, rival explanations, contrary information, outliers or dissenting explanations or interpretations are clearly expressed |
Data Analysis & Sense Making | Analytical Rigor | Structured Analytical Techniques are used across the analysis process (Key assumption checklist, information gap assessment, problem tree, confidence scale, analysis framework, etc.) |
Data Analysis & Sense Making | Analytical Rigor | Confidence levels in or trustworthiness of main results/findings are clearly expressed |
Data Analysis & Sense Making | Analytical Rigor | The representativity (confidence levels, margin of errors) and/or the transferability of results/findings to other geographical areas or population groups are clearly expressed |
Data Analysis & Sense Making | Analytical Rigor | The methods/approaches used to estimate humanitarian population figures are clearly expressed |
Data Analysis & Sense Making | Analytical Rigor | Cross cutting analyses (e.g. gender, age, disability, protection, etc.) are applied trhoughout the process |
Data Analysis & Sense Making | Analytical Rigor | The analysis team is mixed in terms of institutional backgrounds, sectoral expertise, disciplines, and gender. |
Communication & DIssemination | Effective communication | The report follows a logical structure with increasing levels of details ( e.g. executive summary/key findings, objectives, methodology, detailed results, annexes and tools, etc.) |
Communication & DIssemination | Effective communication | Results/findings are presented clearly and concisely and supported by evidence, transparent sources and well-reasoned arguments. |
Communication & DIssemination | Effective communication | Appropriate and effective visualizations (e.g. charts, maps, graphs, infographics) are used to illustrate and support the main results/findings |
Communication & DIssemination | Effective communication | Key assumptions, limitations and/or information gaps are made explicit |
Communication & DIssemination | Effective communication | Probabilities, confidence levels and uncertainty attached to main results/findings/figures are clearly expressed |
Communication & DIssemination | Effective communication | The assessment is transparent about the number and type of secondary sources collected and/or the number, type and location of respondents interviewed. |
Communication & DIssemination | Effective communication | Available annexes, tools data and technical documentation (e.g. methodological and processing notes, change log, anonymized datasets, questionnaires, coding taxonomy and definitions, scales definitions, methodology to estimate population in need, etc.) allows for the full reproduction of assessment results/findings |
Communication & DIssemination | Effective communication | Preliminary or final results are shared with response planners in advance of the response planning/decision-making process (6 points) |
Communication & DIssemination | Effective communication | The methodological section provides detailed explanations, tools and methodology for each step of the assessment process |
Communication & DIssemination | Effective communication | Unless withheld for security reasons, the assessment indicates which profiles, skills and expertise were involved at each step of the assessment process |
Assessment Quality Criteria Dataset
Overview
This dataset provides a structured framework for assessing the quality of humanitarian assessments, spanning the full assessment cycle, including design and planning, data collection, analysis, and dissemination. It offers detailed quality criteria across various pillars and sub-pillars to help ensure comprehensiveness, transparency, and rigor.
Dataset Structure
The dataset is organized into three main columns:
- Pillar: The main categories of the assessment process (e.g., Design & Planning, Data Collation & Collection, Data Preparation & Exploration, Data Analysis & Sense Making, Communication & Dissemination).
- Sub Pillar: Specific aspects or stages within each Pillar (e.g., Clear Objectives, Source Diversity, Analytical Rigor, Effective Communication).
- Quality Criteria to Meet: The individual criteria that need to be met for the assessment to be considered high-quality.
Use Cases
This dataset can be used by:
- Humanitarian organizations to benchmark their assessment processes.
- Analysts and researchers aiming to ensure methodological rigor in their studies.
- Developers building tools to evaluate the quality of humanitarian assessments.
- Educational purposes to teach best practices in humanitarian assessments.
Dataset Fields
| Column | Description |
|---|---|
| Pillar | The main phase or category of the assessment process. |
| Sub Pillar | Sub-category within each phase describing more specific areas of focus. |
| Quality Criteria to Meet | Detailed criteria to evaluate the quality of assessments under each pillar. |
Example Rows
| Pillar | Sub Pillar | Quality Criteria to Meet |
|---|---|---|
| Design & Planning | Clear Objectives | The assessment scope, objectives, and/or sub-objectives are clearly expressed. |
| Data Collation & Collection | Source Diversity | Multiple data collection techniques and/or sources are used to gather data. |
| Data Analysis & Sense Making | Descriptive Analysis | Issues/unmet needs are detailed. |
How to Use
- Download the dataset and load it into your analysis tool of choice (e.g., Python, Excel).
- Filter or query based on specific pillars or sub-pillars you are interested in.
- Use the quality criteria as a checklist or evaluation tool for your assessment process.
- Adapt the criteria as needed to fit your specific assessment context.
Usage
You can load this dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("Sfekih/ifrc_assessment_quality_framework")
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