Tools to collect existing data include: Research Journals - Unlike newspapers and magazines, research journals are intended for an academic or technical audience, not general readers. A journal is a scholarly publication containing articles written by researchers, professors, and other experts. Surveys - A survey is a data collection tool for gathering information from a sample population, with the intention of generalizing the results to a larger population.
Surveys have a variety of purposes and can be carried out in many ways depending on the objectives to be achieved. Pros Easy to administer. There subsists a greater accuracy with results. It is a universally accepted practice. It diffuses the situation of an unwillingness of respondents to administer a report. It is appropriate for certain situations.
It cannot be relied upon. Bias may arise. It is expensive to administer. Its validity cannot be predicted accurately. What are the best Data Collection Tools for Observation?
The best tools for Observation are: Checklists - state-specific criteria, allow users to gather information and make judgments about what they should know in relation to the outcomes.
They offer systematic ways of collecting data about specific behaviors, knowledge, and skills. Direct observation - This is an observational study method of collecting evaluative information.
The evaluator watches the subject in his or her usual environment without altering that environment. Pros Information obtained is usually very detailed. Cost-effective when compared to one-on-one interviews. It reflects speed and efficiency in the supply of results. Cons Lacking depth in covering the nitty-gritty of a subject matter. Bias might still be evident. Requires interviewer training The researcher has very little control over the outcome.
A few vocal voices can drown out the rest. Difficulty in assembling an all-inclusive group. The best tools for tackling Focus groups are: Two-Way - One group watches another group answer the questions posed by the moderator. After listening to what the other group has to offer, the group that listens are able to facilitate more discussion and could potentially draw different conclusions.
The main positive of the dueling-moderator focus group is to facilitate new ideas by introducing new ways of thinking and varying viewpoints. Pros Encourage participants to give responses. It stimulates a deeper connection between participants. The relative anonymity of respondents increases participation.
It improves the richness of the data collected. Cons It costs the most out of all the top 7. It's the most time-consuming. The best tools for combination research are: Online Survey - The two tools combined here are online interviews and the use of questionnaires. This is a questionnaire that the target audience can complete over the Internet. It is timely, effective and efficient.
Especially since the data to be collected is quantitative in nature. Dual-Moderator - The two tools combined here are focus groups and structured questionnaires. The structured questioners give a direction as to where the research is headed while two moderators take charge of proceedings. Whilst one ensures the focus group session progresses smoothly, the other makes sure that the topics in question are all covered.
Dual-moderator focus groups typically result in a more productive session and essentially leads to an optimum collection of data. Extensive Analytics Form Analytics, a feature in formplus helps you view the number of respondents, unique visits, total visits, abandonment rate, and average time spent before submission. Embed Survey Form on Your Website Copy the link to your form and embed as an iframe which will automatically load as your website loads, or as a popup which opens once the respondent clicks on the link.
Geolocation Support The geolocation feature on Formplus lets you ascertain where individual responses are coming. Multi-Select feature This feature helps to conserve horizontal space as it allows you to put multiple options in one field.
Register or sign up on Formplus builder : Start creating your preferred questionnaire or survey by signing up with either your Google, Facebook or Email account. Create Online Questionnaire or Survey for Free 2. If yes, just copy and paste it to the CSS option. Edit your survey questionnaire settings for your specific needs Choose where you choose to store your files and responses. View your Responses to the Survey Questionnaire Toggle with the presentation of your summary from the options.
Allow Formplus Analytics to interpret your Survey Questionnaire Data With online form builder analytics, a business can determine; The number of times the survey questionnaire was filled The number of customers reached Abandonment Rate: The rate at which customers exit the form without submitting. The type of device used by the customer to complete the survey questionnaire. A clear attainable goal would, for example, mirror a clear reason as to why something is happening. If possible offer a range of answers with choice options and ratings.
Survey outlook should be attractive and Inviting - An attractive-looking survey encourages a higher number of recipients to respond to the survey. Check out Formplus builder for colorful options to integrate into your survey design. You could use images and videos to keep participants glued to their screens. Assure Respondents about the safety of their data - You want your respondents to be assured whilst disclosing details of their personal information to you.
It's your duty to inform the respondents that the data they provide is confidential and only collected for the purpose of research. Ensure your survey can be completed in record time - Ideally, in a typical survey, users should be able to respond in seconds.
It is pertinent to note that they, the respondents, are doing you a favor. Don't stress them. Be brief and get straight to the point. Do a trial survey - Preview your survey before sending out your surveys to the intended respondents. Make a trial version which you'll send to a few individuals. Based on their responses, you can draw inferences and decide whether or not your survey is ready for the big time. Attach a reward upon completion for users - Give your respondents something to look forward to at the end of the survey.
Think of it as a penny for their troubles. It could well be the encouragement they need to not abandon the survey midway. Top Survey Templates For Data Collection Customer Satisfaction Survey Template On the template, you can collect data to measure customer's satisfaction over key areas like the commodity purchase and the level of service they received. Demographic Survey Template With this template, you would be able to measure, with accuracy, the ratio of male to female, age range and a number of unemployed persons in a particular country as well as obtain their personal details such as names and addresses.
Online Questionnaire Template The online questionnaire template houses the respondent's data as well as educational qualification to collect information to be used for academic research. Student Data Sheet Form Template The template is a data sheet containing all the relevant information of a student. Interview Consent Form Template This online interview consent form template allows interviewee sign off their consent to use the interview data for research or report for journalist.
What is best data collection method for qualitative data? Ans: Combination Research The best data collection method for a researcher for gathering qualitative data which generally is data relying on the feelings, opinions and beliefs of the respondents would be Combination Research. Hospitals, which tend to have more developed data collection systems, serve only a small fraction of the country's population. As a result, no one setting within the health care system can capture data on race, ethnicity, and language for every individual.
Health information technology Health IT may have the potential to improve the collection and exchange of self-reported race, ethnicity, and language data, as these data could be included, for example, in an individual's personal health record PHR and then utilized in electronic health record EHR and other data systems.
While substantial resources were devoted to this technology in the American Recovery and Reinvestment Act of , 2 it will take time to develop the infrastructure necessary to fully implement and support Health IT Blumenthal, Thus, the consideration of other avenues of data collection and exchange is essential to the subcommittee's task.
Until data are better integrated across entities, some redundancy will remain in the collection of race, ethnicity, and language data from patients and enrollees, and equivalently stratified data will remain unavailable for comparison purposes unless entities adopt a nationally standardized approach. Methods should be considered for incorporating these data into currently operational data flows, with careful attention to concerns regarding efficiency and patient privacy.
Because hospitals tend to have information systems for data collection and reporting, staff who are used to collecting registration and admissions data, and an organizational culture that is familiar with the tools of quality improvement, they are relatively well positioned to collect patients' demographic data. In addition, hospitals have a history of collecting race data. With the passage of the Civil Rights Act of 3 and Medicare legislation in , 4 there was a legislative mandate for equal access to and desegregation of hospitals Reynolds, Therefore it is not surprising that more than 89 percent of hospitals report collecting race and ethnicity data, and 79 percent report collecting data on primary language AHA, This culture of data collection has limitations, however.
Historically, the data were never intended for quality improvement purposes, but to allow analysis to ensure compliance with civil rights provisions. Additionally, hospital data collection practices are less than systematic as the categories collected vary by hospital, and hospitals obtain the information in various ways e.
Furthermore, compared with the number of people who are insured or visit an ambulatory care provider, a relatively small number of people are hospitalized in any one year Figure Thus, while hospitals are an important component of the health care system and represent a major percentage of health care expenditures, they are only one element of the system for collecting and reporting race, ethnicity, and language data.
Hospitals also face challenges associated with collecting accurate data and using these data for quality improvement and reduction of disparities. A National Public Health and Hospitals Institute NPHHI survey asked hospitals that collected race and ethnicity data whether they used the data to assess and compare quality of care, utilization of health services, health outcomes, or patient satisfaction across their different patient populations.
Fewer than one in five hospitals that collected these data used them for any of these purposes Regenstein and Sickler, Additionally, only half of hospitals that collected data on primary language maintained a database of patients' primary languages that they could track over time Hasnain-Wynia et al.
Many of the above challenges can be attributed largely to the many staff and departments or units that need to be engaged in the process to ensure systematic data collection and use.
Hospitals have multiple pathways inpatient, outpatient, ED, urgent care through which patients enter the system. For example, the ED is the source of 45 percent of all hospital admissions Healthcare Financial Management Association, Ideally, these systems would be made interoperable through the development of interfaces that would make it possible to relay the data across different systems. A Robert Wood Johnson Foundation initiative to reduce disparities in cardiac care required participating hospitals to systematically collect race, ethnicity, and language data and use the data to stratify quality measures.
The ten hospitals in the collaborative initially cited the data collection requirement as one of the greatest challenges of the program, yet once they focused their efforts on these goals, they were able to bring together key stakeholders within each institution, implement needed IT changes, and train staff.
As a result, they successfully began data collection within a relatively short time Siegel et al. Other hospitals not part of this initiative are also successfully collecting race, ethnicity, and language data and linking them to quality measures Weinick et al.
Data collected at the hospital level are useful both for assessing the quality of hospital-provided services and, if shared with other entities, for facilitating analyses of quality across multiple settings. Box provides an example of a statewide initiative to collect standardized race, ethnicity, and language data.
CHCs are front-line providers of care for underserved and disadvantaged groups Taylor, and therefore are good settings for implementing quality improvement strategies aimed at reducing racial and ethnic disparities in care.
Yet while CHCs serve diverse patient populations and, as organizations, understand the importance of demographic data for improving the quality of care, the accuracy of the race, ethnicity, and language data they collect may be limited Maizlish and Herrera, More than 87 percent of surveyed CHCs reported inquiring about a patient's need for language services, and 73 percent reported recording this information in the patient record Gallegos et al.
Box Statewide Race and Ethnicity Data Collection: Massachusetts In January , all Massachusetts hospitals were required to begin collecting race and ethnicity data from every patient with an inpatient stay, an observation unit stay, or an emergency department visit.
These data are included in the electronic discharge data each hospital submits to the state's Division of Health Care Finance and Policy. As part of this effort, a standardized set of reporting categories was created and train-the-trainer sessions were held across the state.
A report on this initiative notes:. Like hospitals, CHCs face challenges to collecting data, such as the need to train staff, the need to modify existing Health IT systems, and the need to ensure interoperability between the practice management systems where demographic data are collected and recorded and the EHR systems where the demographic data can be linked to clinical data for quality improvement purposes.
Collection of demographic data can also increase the burden of data entry for staff, particularly for those CHCs that still use paper forms to collect these data from patients Chin et al. Limited resources both financial and human and a high-need patient population present ongoing challenges to CHCs in their data collection and quality improvement efforts Box Because 40 percent of CHCs' patient populations are uninsured and because CHCs generally have a poor payer mix Manatt Health Solutions and RSM McGladrey, ; National Association of Community Health Centers, , they gain relatively less revenue than private physician practices from quality improvement interventions that lead to the delivery of more services Chin et al.
Even with increases in federal funding, CHCs struggle to meet the rising demand for care along with demands to increase quality reporting, reduce disparities, and develop EHR systems Hurley et al. The structure and capabilities of primary and specialty care entities vary tremendously, ranging from large groups or health centers with highly structured staff and advanced information systems to solo physician practices with correspondingly small staff. The ability and motivation of these entities to collect and effectively use race, ethnicity, and language data consequently also vary given the investments in Health IT systems and staff training required for these functions.
At the same time, these settings have direct contact with patients, ideally as part of an ongoing caregiving relationship. Thus, they are well suited to explaining the reasons for collecting these data, as well as using the data to assess health care needs and patterns of disparities. Physician practices, however, are less likely than hospitals or CHCs to collect race, ethnicity, and language data from patients Nerenz et al.
Medical groups may believe either that it is unnecessary to collect these data or that collecting them would offend patients Nerenz and Darling, Physician practices may not see the utility of the data and may believe that they should not bear the burden of collecting the data and linking them to quality measures Mutha et al. A number of physicians and practice managers interviewed in thought it was illegal to collect these data, and many did not understand how the data would be used Hasnain-Wynia, However, most of the interviewees physicians, nurse managers, and practice managers indicated that they thought it would not be problematic to collect these data from their patients if they could explain why the data were being collected and how they would be used Box Indeed, Henry Ford Medical Group has collected race and ethnicity data for more than twenty years, and the Palo Alto Medical Foundation, a multispecialty provider group with several clinics, has recently begun to collect race and ethnicity data for use in analyses of disparities Palaniappan et al.
Primary care sites typically do not have structured information available about care provided at other locations, so their ability to analyze data on quality of care by race, ethnicity, and language is generally limited to measures involving routine prevention and primary care. Physician practices with EHR systems tend to use the system for administrative rather than quality improvement purposes Shields et al.
Data on race, ethnicity, and language need collected in these settings could be useful throughout the health care system if mechanisms were in place for sharing the data with other entities e. Multispecialty group practices, which provide a range of primary care, specialty care, inpatient care, and other services, may be in a strong position to collect race, ethnicity, and language data because they have regular contact with large numbers of patients over long periods of time, can place the data collection in the context of improvement of care rather than administration of health insurance benefits, and typically have the necessary staff and other forms of infrastructure e.
Quantitative data refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analyzed mathematically. Qualitative data are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc. As you might expect, quantitative and qualitative information needs to be analyzed differently. Quantitative data are typically collected directly as numbers. Some examples include:.
Data can also be collected in forms other than numbers, and turned into quantitative data for analysis. Researchers can count the number of times an event is documented in interviews or records, for instance, or assign numbers to the levels of intensity of an observed event or behavior.
For instance, community initiatives often want to document the amount and intensity of environmental changes they bring about — the new programs and policies that result from their efforts.
Quantitative data is usually subjected to statistical procedures such as calculating the mean or average number of times an event or behavior occurs per day, month, year. Various kinds of quantitative analysis can indicate changes in a dependent variable related to — frequency, duration, timing when particular things happen , intensity, level, etc. They can allow you to compare those changes to one another, to changes in another variable, or to changes in another population.
They might be able to tell you, at a particular degree of reliability, whether those changes are likely to have been caused by your intervention or program, or by another factor, known or unknown. And they can identify relationships among different variables, which may or may not mean that one causes another. A number may tell you how well a student did on a test; the look on her face after seeing her grade, however, may tell you even more about the effect of that result on her.
And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the test. Qualitative data can sometimes be changed into numbers, usually by counting the number of times specific things occur in the course of observations or interviews, or by assigning numbers or ratings to dimensions e.
The challenges of translating qualitative into quantitative data have to do with the human factor. Furthermore, the numbers say nothing about why people reported the way they did. One may dislike the program because of the content, the facilitator, the time of day, etc. Where one person might see a change in program he considers important another may omit it due to perceived unimportance. Quantitative analysis is considered to be objective — without any human bias attached to it — because it depends on the comparison of numbers according to mathematical computations.
Be aware, however, that quantitative analysis is influenced by a number of subjective factors as well. Part of the answer here is that not every organization — particularly small community-based or non-governmental ones — will necessarily have extensive resources to conduct a formal evaluation. They may have to be content with less formal evaluations, which can still be extremely helpful in providing direction for a program or intervention. An informal evaluation will involve some data gathering and analysis.
This data collection and sensemaking is critical to an initiative and its future success, and has a number of advantages. The level of significance of a statistical result is the level of confidence you can have in the answer you get. Thus, if data analysis finds that the independent variable the intervention influenced the dependent variable at the.
Ideally, you should collect data for a period of time before you start your program or intervention in order to determine if there are any trends in the data before the onset of the intervention.
Which of these approaches you take depends on your research purposes. Both approaches are legitimate, but ongoing data collection and review can particularly lead to improvements in your work. Who should actually collect and analyze data also depends on the form of your evaluation. Analysis also could be accomplished by a participatory process. Another way analysis can be accomplished is by professionals or other trained individuals, depending upon the nature of the data to be analyzed, the methods of analysis, and the level of sophistication aimed at in the conclusions.
We've previously discussed designing an observational system to gather information. There are other excellent possibilities for analysis besides statistical procedures, however. A few include:. They may or may not be socially significant i. Among American teenagers, for instance, there is probably a fairly high correlation between an increase in body size and an understanding of algebra.
This is not because one causes the other, but rather the result of the fact that American schools tend to begin teaching algebra in the seventh, eighth, or ninth grades, a time when many , , and year-olds are naturally experiencing a growth spurt.
On the other hand, correlations can reveal important connections. A very high correlation between, for instance, the use of a particular medication and the onset of depression might lead to the withdrawal of that medication, or at least a study of its side effects, and increased awareness and caution among doctors who prescribe it.
A very high correlation between gang membership and having a parent with a substance abuse problem may not reveal a direct cause-and-effect relationship, but may tell you something important about who is more at risk for substance abuse. Not all important findings will necessarily tell you whether your program worked, or what is the most effective method. It might be obvious from your data collection, for instance, that, while violence or roadway injuries may not be seen as a problem citywide, they are much higher in one or more particular areas, or that the rates of diabetes are markedly higher for particular groups or those living in areas with greater disparities of income.
Probably the most common question that evaluation research is directed toward is whether the program being evaluated works or makes a difference. If your analysis shows that your program is ineffective or negative, however — or, for that matter, if a positive analysis leaves you wondering how to make your successful efforts still more successful — interpretation becomes more complex.
Are you using an absolutely wrong approach? Are you using an approach that could be effective, but is poorly implement? Are there barriers to success — of culture, experience, personal characteristics, systematic discrimination — present in the population from which participants are drawn? Are there particular components or elements you can change to make your program more effective, or should you start again from scratch?
What should you address to make a good program better? We define remote patient monitoring as the set of activities that meet four key criteria: 1 data on patients is collected remotely e. By making it possible to virtually perform medical activities that have traditionally been conducted in person, remote monitoring technologies have played a significant role in patient care during the Covid pandemic.
Joseph Health in Renton, Washington, started programs during the Covid pandemic in order to monitor vital sign and symptom data and assess the status of coronavirus patients. Other hospitals, such as Mayo Clinic in Rochester, Minnesota, are working to set up remote patient monitoring programs for non-Covid patients e.
New policies have recognized the importance of remote patient monitoring in this context. The U. Centers for Medicare and Medicaid Services expanded Medicare coverage for remote patient monitoring to include patients with acute conditions and new patients as well as existing patients. Moreover, the U. Food and Drug Administration issued a new policy allowing certain devices FDA-approved non-invasive devices used to monitor vital signs to be used in remote settings.
Nonetheless, these changes remain temporary: They have only been authorized for the duration of the Covid public health emergency. We hope that additional policies will be enacted to ensure that these programs can serve a variety of patients and conditions beyond the context of Covid These guidelines are drawn from our own experience managing remote-patient-monitoring programs, including one created specifically to care for Covid patients, and research on the drivers of clinical success of established programs.
The technology must be easy for both patients and clinicians to adopt and continue using. It is essential to provide both patients and clinicians with intuitive equipment and user interfaces as well as resources for trouble-shooting when needed. Clinicians should be able to easily explain the equipment to patients, and it should be easy for patients to set up and use.
The patient data generated by remote monitoring should also be simple to monitor and analyze. This need is illustrated by a trial that studied remote monitoring of patients with congestive heart failure.
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