{"id":29237,"date":"2025-07-21T10:20:43","date_gmt":"2025-07-21T14:20:43","guid":{"rendered":"https:\/\/positiveexperience.org\/?p=29237"},"modified":"2025-07-21T10:20:43","modified_gmt":"2025-07-21T14:20:43","slug":"collecting-what-matters-practical-strategies-for-meaningful-data","status":"publish","type":"post","link":"https:\/\/positiveexperience.org\/blog\/collecting-what-matters-practical-strategies-for-meaningful-data\/","title":{"rendered":"Collecting what matters \u2013 practical strategies for meaningful data"},"content":{"rendered":"<div class=\"wpb-content-wrapper\">[vc_row remove_bottom_col_margin=&#8221;true&#8221;][vc_column][vc_column_text css=&#8221;&#8221;]\n<h2>What&#8217;s next: collecting meaningful data<\/h2>\n<p>In Part 1 of our \u201cUsing Data\u201d blog series, we focused on <a href=\"https:\/\/positiveexperience.org\/blog\/making-data-doable-and-useful-a-practical-guide-for-organizations-of-any-size\/\">making data doable<\/a> by starting small and aligning with purpose. In Part 2, we emphasized <a href=\"https:\/\/positiveexperience.org\/blog\/asking-the-right-questions-letting-purpose-lead-the-way-towards-focused-data-collection-and-analysis\/\">asking the <em>right<\/em> questions<\/a> that lead to action. But, what if the data you need to answer those questions isn\u2019t already being collected?<\/p>\n<p>That\u2019s where many teams get stuck. The good news? Collecting meaningful data doesn\u2019t have to be overwhelming \u2013 even if your time, staff, or budget are limited.[\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Start with the goal in mind<\/strong><\/h2>\n<p>Before jumping into data collection, get clear on how you\u2019ll know whether your question has been answered. If you\u2019re doing this in the context of quality improvement (QI), one tool that can help is an aim statement. A good aim statement includes:<\/p>\n<ul>\n<li>What you&#8217;re trying to improve<\/li>\n<li>By how much<\/li>\n<li>For whom<\/li>\n<li>And by when<\/li>\n<\/ul>\n<p>This kind of clarity helps you figure out what success looks like\u2014and what data you need to collect. It prevents you from over-collecting or gathering data that doesn\u2019t tie back to your goals.<\/p>\n<p>Here\u2019s an example of a good aim statement:<\/p>\n<p><em>Over the next 6 months, we will begin collecting data related to the Four Building Blocks of HOPE to strengthen family-centered planning and build staff confidence in applying the HOPE framework.<\/em><\/p>\n<p>This statement is time-bound, specific, and tied to a clear outcome. And it helps you identify what data is needed, such as family responses related to the <a href=\"https:\/\/positiveexperience.org\/resource\/the-four-building-block-of-hope\/\">Four Building Blocks of HOPE<\/a>, staff self-assessments on confidence using the HOPE framework, and the number of treatment goals connected to the Building Blocks.[\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Use mixed methods to see the whole picture<\/strong><\/h2>\n<p>It\u2019s common for teams (or funders) to gravitate toward numbers. Numbers feel concrete. But qualitative data \u2013 such as stories, feedback, and reflections \u2013 are just as valuable.<\/p>\n<p>In fact, combining quantitative and qualitative data gives you a fuller picture:<\/p>\n<ul>\n<li>Quantitative data shows <em>what<\/em> is happening.<\/li>\n<li>Qualitative data helps you understand <em>why<\/em>.<\/li>\n<\/ul>\n<p>For example:<\/p>\n<ul>\n<li>A survey might show a drop in participation.<\/li>\n<li>Follow-up interviews might reveal it\u2019s due to scheduling conflicts or unclear communication.<\/li>\n<\/ul>\n<p>Together, these data types can deepen your insight and lead to more informed decisions.<\/p>\n<p>At the HOPE National Resource Center, some of our richest insights during our implementation evaluation survey, listening sessions, and key informant interviews came from stories. One organization shared how staff started using more family-centered language after reviewing their intake forms. Others described internal culture shifts sparked by small training changes. These examples added depth and context to the survey data, helping us see what real-world implementation looks like.[\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Don\u2019t go it alone<\/strong><\/h2>\n<p>You don\u2019t have to collect everything yourself. Many organizations are already collecting data for grants, strategic plans, or compliance efforts. See what you or your partners are already doing:<\/p>\n<ul>\n<li>Are there existing surveys you can adapt or build on?<\/li>\n<li>Are others collecting similar information that you can share or align with?<\/li>\n<li>What grant or board requirements already guide your data efforts?<\/li>\n<\/ul>\n<p>Working together doesn\u2019t just reduce the burden. It builds consistency and shared momentum.[\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Be intentional about what you collect<\/strong><\/h2>\n<p>More data isn\u2019t always better. In fact, too much data can create confusion and burnout. Choose your data sources carefully:<\/p>\n<ul>\n<li>What will directly help you answer your question?<\/li>\n<li>What will be useful \u2013 not just interesting \u2013 for your team or stakeholders?<\/li>\n<li>What\u2019s realistic to collect, interpret, and use?<\/li>\n<\/ul>\n<p>You don\u2019t need to look at the same issue in ten different ways. One or two well-chosen methods can offer more clarity than a dozen loosely connected ones.[\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Small can be strategic<\/strong><\/h2>\n<p>Collecting meaningful data doesn\u2019t require a massive evaluation. Here are a few examples of small-scale evaluations:<\/p>\n<ul>\n<li>Tracking the completion of a new form<\/li>\n<li>A simple three-question post-training survey<\/li>\n<li>A focus group with staff or families<\/li>\n<li>A pre\/post checklist to track progress<\/li>\n<li>Open-ended reflections gathered at team meetings<\/li>\n<\/ul>\n<p>The key is to design tools that give you the information you need to continue evolving and improving.[\/vc_column_text]<div class=\"vcex-module vcex-divider vcex-divider-solid vcex-divider-center wpex-mx-auto wpex-block wpex-h-0 wpex-border-b wpex-border-solid wpex-border-main\"><\/div>[vc_column_text css=&#8221;&#8221;]\n<h2><strong>Our journey at HOPE: learning alongside you<\/strong><\/h2>\n<p>At the HOPE National Resource Center, we\u2019ve been on this journey, too. Over the past several months, we\u2019ve been listening, learning, and collecting data from the field to better understand what implementation of the HOPE framework looks like, and how to support it in real and practical ways.<\/p>\n<p>We learned that the HOPE framework is being implemented across diverse sectors \u2013 from healthcare to education to community organizations \u2013 and that training and internal culture change are the most common focus areas. Many organizations are adapting their own tools to reflect HOPE, embedding it into policies, environments, and even funding decisions.<\/p>\n<p>At the same time, many teams shared barriers: limited capacity, uncertainty about where to start, and challenges in choosing the right tools. Evaluation efforts often lean qualitative, and many people are still trying to figure out how to track the impact of their work in meaningful ways.<\/p>\n<p>This feedback is helping us shape our next steps.<\/p>\n<p>This month, we kicked off our <a href=\"https:\/\/positiveexperience.org\/train-with-us\/hope-innovation-network\/\">2025 HOPE Innovation Network (HIN) cohort<\/a>. This group of 11 organizations is working to deepen and evaluate their HOPE implementation. Each site is starting by crafting a clear aim statement to define what they\u2019re working toward and how they\u2019ll measure progress. These aim statements will guide their data collection and help them choose methods that are useful, realistic, and aligned with their goals.<\/p>\n<p>We\u2019re excited to walk alongside them as they begin this phase, and we\u2019ll be sharing what we learn together![\/vc_column_text][vc_column_text css=&#8221;&#8221;]\n<h2><strong>Bottom line: collect what you need, not everything you could<\/strong><\/h2>\n<p>You don\u2019t need to build a data system from scratch or track every possible angle. Start with what you want to learn. Let that shape your aim. Then choose just enough data \u2013 both numbers and stories \u2013 to help you get there.<\/p>\n<p>That\u2019s how you make data meaningful, manageable, and deeply useful.[\/vc_column_text][\/vc_column][\/vc_row]\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Learn how to make data collection meaningful, manageable, and useful to effectively promote positive childhood experiences in your organization.<\/p>\n","protected":false},"author":25,"featured_media":29238,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[266],"post_series":[],"class_list":["post-29237","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-hope-framework","entry","has-media"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Collecting what 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