Prepare a newsletter article where you identify and discuss the growing risks associated with adoption of mobile applications to deliver digital government services. Your audience is a group of federal agency managers and contractors who receive a monthly newsletter on "Best Practices for Digital Government." Your article will appear in a special issue dedicated to addressing the adoption and deployment of mobile applications for digital government services. Review https://www.dhs.gov/sites/default/files/publications/2019_mobilesecurity_rd_programguide_vol3.pdf to learn what the Department of Homeland Security's researchers have found in regards to security risks and mobile apps.The readers of the newsletter may not be familiar with mobile applications for digital government services or digital government services, so you should consider explaining these concepts and giving examples. The primary purpose of your newsletter is to identify and discuss the growing risks associated with adoption of mobile applications to deliver digital government services.Your newsletter article should be 5 to 7 paragraphs long and may contain bullet points if appropriate.Provide in-text citations and references for 3 or more authoritative sources. Put the reference list at the end of your posting.Remeber to make the article interesting and engaging

Week 5 Discussion Measurement and Data CollectionData collection requires evaluators to consider a wide and diverse variety of data sources. These include both primary and secondary data sources. Data collection can significantly impact your evaluation project. Evaluators sometimes overlook problems where secondary data is involved and overlook the fact that data for comparison purposes can affect validity. The types of data in the Learning Resources are not meant to be exhaustive but are the most common types in program evaluation.To prepare for this Discussion, choose one of the cases provided, review the specified Learning Resources, and complete the corresponding prompts.Case 1Steven Levitt, the author of Freakonomics, is exemplary in his ability to make data tell “stories.” Students of public administration and organizational behavior are familiar with the famous Hawthorne effect. Levitt and List’s (2009) reanalysis of the original Hawthorne study (which uses incomplete, archived, and secondary data) shows how management programs that were developed to increase worker productivity can be evaluated or reevaluated, even though the experiments were conducted almost a century ago.Post by Day 3 a description of at least two data collection problems that the reanalysis of Levitt and List might bring to the Hawthorne effect.Respond by Day 5 to two colleagues and explain whether the problems have to do with design or data collection. What other data collection problems could, in your view, also be important? Explain.Case 2Barbara Geddes (1990) points out the pitfalls of selecting cases, units, or observations purely on the basis of the dependent variable. Post by Day 3 a brief explanation of the relevance of Barbara Geddes’ argument for program evaluation. Then, explain when a scenario of choosing cases solely on the dependent variable might be permissible. Provide a rationale for your explanations.Respond by Day 5 to two colleagues and explain in what ways you agree or disagree with the permissibility of the identified scenarios. Justify your reaction.Note: Within the Week 5 Forum, use the appropriate thread for your selected case:For Case 1, use the “Week 5 Discussion - Case 1” thread.For Case 2, use the “Week 5 Discussion - Case 2” thread.Week 5 Learning Resources This page contains the Learning Resources for this week. Be sure to scroll down the page to see all of this week's assigned Learning Resources.Required ResourcesReadingsLangbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe. Chapter 7, “Designing Useful Surveys for Evaluation” (pp. 209–238) McDavid, J. C., Huse, I., & Hawthorn, L. R. L. (2019). Program evaluation and performance measurement: An introduction to practice (3rd ed.). Thousand Oaks, CA: Sage. Chapter 4, “Measurement for Program Evaluation and Performance Monitoring” (pp. 161-199) Geddes, B. (1990). How the cases you choose affect the answers you get: Selection bias in comparative politics. Political Analysis, 2(1), 131–150. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.372.5896&rep=rep1&type=pdfLevitt, S., & List, J. (2009). Was there really a Hawthorne effect at the Hawthorne plant? An analysis of the original illumination experiments. Retrieved from http://www.nber.org/papers/w15016.pdfUrban Institute. (2014). Outcome indicators project. Retrieved from http://www.urban.org/center/cnp/projects/outcomeindicators.cfmOptional ResourcesNote: Consult the following resources based on your interest and relevance to your project:Bamberger, M. (2010). Reconstructuring baseline data for impact evaluation and results measurement. Retrieved from http://siteresources.worldbank.org/INTPOVERTY/Resources/335642-1276521901256/premnoteME4.pdfParnaby, P. (2006). Evaluation through surveys [Blog post]. Retrieved from http://www.idea.org/blog/2006/04/01/evaluation-through-surveys/Rutgers, New Jersey Agricultural Experiment Station. (2014). Developing a survey instrument. Retrieved from http://njaes.rutgers.edu/evaluation/resources/survey-instrument.aspMEASURE Evaluation. (n.d.). Secondary analysis of data. Retrieved February 24, 2015, from http://www.cpc.unc.edu/measure/our-work/secondary-analysis/secondary-analysis-of-dataZeitlin, A. (2014). Sampling and sample size [PowerPoint slides]. Retrieved from http://www.povertyactionlab.org/sites/default/files/2.%20Sampling%20and%20Sample%20Size_AFZ3.pdf