Question

Imagine you, a Certified Health Education Specialist (CHES) has been assigned to a community health assessment planning group investigating obesity and its associated behavioral and non-behavioral contributors. The only available data for the community are those showing the overall prevalence of obesity. However, the group wants to collect data from different subpopulations (i.e., Caucasians, African Americans, Hispanic Americans, Asian Americans, children, and adults) in order to identify disparities in obesity prevalence. Assume multiple approaches (quantitative/or qualitative methods using primary and/or secondary data) may be used for program needs. The timeline for completion of the assessment from beginning to end is 3 months.

You are required to answer the following questions as they relate to the scenario.

1. Provide 1 detailed assessment question that you and your planning team would want to investigate based on the nature of the scenario.

2. Information needed that you and your planning team will need to help with addressing the assessment question.

3. From whom?

4.  How will it be collected (Quantitative or Qualitative; Primary or Secondary)

5.  When collected (General Timeline)

6. Analysis (How results for questions will be produced)

7. Listed below are four need assessment models:

a) Epidemiological Model  b) Public health Model   c) Social Model d) Asset Model

   Which model would you use and why? Support reasoning

* you may want to complete a Google search to gain more info as it relates to these models as needed

Answer 1

1. Provide 1 detailed assessment question that you and your planning team would want to investigate based on the nature of the scenario.

Ans:-  

This special issue focuses primarily on the behavioural factors associated with obesity (dietary intake; physical activity) with consideration of the contextual factors including the home, school, and neighbourhood environments, which can affect health. Successful models of addressing obesity have examined behaviours and the environment, with the goal of altering the obesogenic environment through public health strategies aimed at promoting healthful eating and physical activity The ecological model of obesity has been described as “multilevel (e.g., regions, nations, states, cities, and neighbourhoods),” taking into consideration the “multi structural components (e.g., physical environment, socio-economic status, and social capital)” and “multifactorial lifestyle behaviours (e.g., diet, physical activity, and stress)” at “multi-institutional (e.g., school, local government, family, and local agency)” levels. Moreover, this ecological approach accounts for the interrelationships among these influences to better understand and measure the behavioural factors that negatively influence weight

A simple indicator of corpulence associated with excess fat mass is more difficult in children than in adults since, in children of a given age, BMI is positively associated with height. The correlations observed between BMI and direct estimations of fat mass are generally smaller and depend on the age, gender and pubertal status of the children. However, for high values, i.e. for the detection of obesity, the sensitivity and specificity of the BMI are considered satisfactory. Despite those reservations, it was considered useful to retain the same body mass index for children, in particular at the epidemiological level. A supplementary step in the standardization of the expression of overweight and obesity in children was made in 2000 by the International Obesity Task Force (IOTF) which proposed: first, replacing the BMI cutoffs defined on the basis of distributions specific to each population by those for a population common to a set of countries; secondly, defining the percentiles corresponding to the cutoffs for each age as those yielding an overweight and obesity frequency at age 18 years defined by the cutoffs of 25 and 30 kg/m2, respectively. Those are the universal cutoffs for adults.

The demonstration of a recent increase in the prevalence of childhood obesity in France and in other Western countries (earlier in the United States) has been well documented and reported in the Inserm collective expert review 'Obesity, screening and prevention in children'

I complement those data without repeating them. The table is restricted to those studies having used the IOTF definitions, enabling better comparisons. In the interest of simplification, the frequencies have been reported for both genders together.

2. Information needed that you and your planning team will need to help with addressing the assessment question.

Ans:-

The first challenge in addressing overweight and obesity lies in adopting a common public health measure of these conditions. An expert panel, convened by the National Institutes of Health (NIH) in 1998, has utilized Body Mass Index (BMI) for defining overweight and obesity.  

BMI is a practical measure that requires only two things:  

Accurate measures of an individual's weight and height. BMI is a measure of weight in relation to height. BMI is calculated as weight in pounds divided by the square of the height in inches, multiplied by 703. Alternatively, BMI can be calculated as weight in kilograms divided by the square of the height in meters. In children and adolescents, overweight has been defined as a sex- and age-specific BMI at or above the 95th percentile, based on revised Centers for Disease Control and Prevention (CDC) growth charts Neither a separate definition for obesity nor a definition for overweight based on health outcomes or risk factors are defined for children and adolescents Overweight and obesity and their associated health problems have substantial economic consequences for the U.S. health care system. The increasing prevalence of overweight and obesity is associated with both direct and indirect costs. Direct health care costs refer to preventive, diagnostic, and treatment services related to overweight and obesity (for example, physician visits and hospital and nursing home care). Indirect costs refer to the value of wages lost by people unable to work because of illness or disability, as well as the value of future earnings lost by premature death.27 In 1995, the total (direct and indirect) costs attributable to obesity amounted to an estimated $99 billion.27 In 2000, the total cost of obesity was estimated to be $117 billion ($61 billion direct and $56 billion indirect). Most of the cost associated with obesity is due to type 2 diabetes, coronary heart disease, and hypertension. The most recent data (1999) estimate that 13 per cent of children aged 6 to 11 years and 14 per cent of adolescents aged 12 to 19 years are overweight. During the past two decades, the percentage of children who are overweight has nearly doubled (from 7 to 13 per cent), and the percentage of adolescents who are overweight has almost tripled (from 5 to 14 per cent)

In general, the prevalence of overweight and obesity is higher in women who are members of racial and ethnic minority populations than in non-Hispanic white women. Among men, Mexican Americans have a higher prevalence of overweight and obesity than non-Hispanic whites or non-Hispanic blacks. For non-Hispanic men, the prevalence of overweight and obesity among whites is slightly greater than among blacks. Within racial groups, gender disparities exist, although not always in the same direction. Based on NHANES III (1988-1994), the proportion of non-Hispanic black women who were overweight or obese (BMI > 25; 69 per cent) was higher than the proportion of non-Hispanic black men (58 per cent) (figure 6). For non-Hispanic whites, on the other hand, the proportion of men who were overweight or obese (BMI > 25; 62 per cent) exceeded the proportion of women (47 per cent). However, when looking at obesity alone (BMI > 30), the prevalence was slightly higher in non-Hispanic white women compared to non-Hispanic white men (23 per cent and 21 per cent, respectively). The prevalence of overweight or obesity (BMI > 25) was about the same in Mexican American men and women (69 per cent and 70 per cent, respectively). Although smaller surveys indicate a higher prevalence of overweight and obesity in American Indians, Alaska Natives, and Pacific Islander Americans and a lower prevalence in Asian Americans compared to the general population, the number surveyed in NHANES III was too small to reliably report prevalence comparisons of overweight and obesity for these populations.

3. From whom?

Ans:- This Surgeon General's Call To Action To Prevent and Decrease Overweight and Obesity seeks to engage leaders from diverse groups in addressing a public health issue that is among the most burdensome faced by the Nation: the health consequences of overweight and obesity. This burden manifests itself in premature death and disability, in health care costs, in lost productivity, and in social stigmatization. The burden is not trivial. Studies show that the risk of death rises with increasing weight. Even moderate weight excess (10 to 20 pounds for a person of average height) increases the risk of death, particularly among adults aged 30 to 64 years.

Overweight and obesity are caused by many factors. For each individual, body weight is determined by a combination of genetic, metabolic, behavioural, environmental, cultural, and socioeconomic influences. Behavioural and environmental factors are large contributors to overweight and obesity and provide the greatest opportunity for actions and interventions designed for prevention and treatment.

For the vast majority of individuals, overweight and obesity result from excess calorie consumption and/or inadequate physical activity. Unhealthy dietary habits and sedentary behaviour together account for approximately 300,000 deaths every year. Thus, a healthy diet and regular physical activity, consistent with the Dietary Guidelines for Americans, should be promoted as the cornerstone of any prevention or treatment effort. According to the U.S. Department of Agriculture's 1994-1996 Continuing Survey of Food Intakes by Individuals, very few Americans meet the majority of the Food Guide Pyramid recommendations. Only 3 per cent of all individuals meet four of the five recommendations for the intake of grains, fruits, vegetables, dairy products, and meats. Much work needs to be done to ensure the nutrient adequacy of our diets while at the same time avoiding excess calories. Dietary adequacy and moderation in energy consumption are both important for maintaining or achieving a healthy weight and for overall health.

4. How will it be collected (Quantitative or Qualitative; Primary or Secondary)

Ans:-

Quantitative and Primary both used for data collection and describe in details below.

Quantitative Research Definition:

Quantitative research is defined as a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires, etc., the results of which can be depicted in the form of numerical. After careful understanding of these numbers to predict the future of a product or service and make changes accordingly.

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research can be broken down into three further distinctive tracks, as well as the process flow.

There are four different types of quantitative research methods:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

1.Survey Research:

Cross-sectional surveys

Longitudinal surveys

2.Correlational Research

3.Causal-Comparative Research

4.Experimental Research

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection with the use of surveys and polls.

Step 1: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling.

Probability sampling:-

Simple random sampling

Stratified random sampling

Cluster sampling

Systematic sampling

Non-probability sampling:-

Convenience Sampling

Consecutive Sampling

Quota Sampling

Snowball Sampling

Judgmental Sampling

Step 2: Using Surveys & Polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using Surveys for Primary Quantitative Research

Fundamental Levels of Measurement – Nominal, Ordinal, Interval and Ratio Scales

Use of Different Question Types

To conduct quantitative research, close-ended questions have to be used in a survey. They can be a mix of multiple question types including multiple-choice questions like semantic differential scale questions, rating scale questions, etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the survey design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email
• Buy respondents
• Embed survey in a website
• Social distribution
• QR code
• SMS survey
• QuestionPro app
• API integration
• Using Polls for Primary Quantitative Research

C. Data Analysis Techniques

The third aspect of primary quantitative research is data analysis. After the collection of raw data, there has to be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the objective of research and establish the statistical relevance of results.

SWOT Analysis

Conjoint Analysis

Cross-tabulation

TURF Analysis

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of research.

This research method involves the collection of quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data that is collected from primary quantitative research as well as aid in strengthening or proving or disproving previously collected data.

Following are five popularly used secondary quantitative research methods:

1. Data available on the internet
2. Government and non-government sources
3. Public libraries
4. Educational institutions
5. Commercial information sources

Quantitative Research Characteristics

Some distinctive characteristics of quantitative research are:

• Structured tools
• Sample size
• Close-ended questions
• Prior studies
• Quantitative data
• Generalization of results

Quantitative Research Examples

Some examples of Quantitative Research are:

1. If any organization would like to conduct a customer satisfaction (CSAT) survey, a customer satisfaction survey template can be used. Through this survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the mind of the customer based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question, matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
2. Another example of quantitative research is an organization that conducts an event, collecting feedback from the event attendees about the value that they see from the event. By using an event survey template, the organization can collect actionable feedback about satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

Advantages of Quantitative Research

There are many advantages of quantitative research. Some of the major advantages of why researchers use this method in market research are:

• Collect reliable and accurate data: As data is collected, analyzed, and presented in numbers, the results obtained will be extremely reliable. Numbers do not lie. They offer an honest picture of the conducted research without discrepancies and is also extremely accurate. In situations where a researcher predicts conflict, quantitative research is conducted.
• Quick data collection: A quantitative research is carried out with a group of respondents who represent a population. A surveyor any other quantitative research method applied to these respondents and the involvement of statistics, conducting, and analyzing results is quite straightforward and less time-consuming.
• Wider scope of data analysis: Due to the statistics, this research method provides a wide scope of data collection.
• Eliminate bias: This research method offers no scope for personal comments or biasing of results. The results achieved are numerical and are thus, fair in most cases.

Primary research is defined as a methodology used by researchers to collect data directly, rather than depending on data collected from previously done research. Technically, they “own” the data. Primary research is solely carried out to address a certain problem, which requires in-depth analysis.

Primary Research Methods:-

1. Interviews (telephonic or face-to-face)

2. Online surveys

3. Focus groups

4. Observations

Advantages of Primary Research

1. One of the most important advantages is, data collected is first hand and is accurate. In other words, there is no dilution of data. Also, this research method can be customized to suit personal requirements and needs of organizations or businesses.
2. Primary research focuses mainly on a problem in hand, which means entire attention is directed to find a probable solution to a pinpointed subject matter. Primary research allows researchers to go in-depth of a matter and study all foreseeable options.
3. Data collected can be controlled. Primary research gives the means to control how data is collected and used. It’s up to the discretion of businesses or organizations who are collecting data on how to best make use of data to get meaningful research insights.
4. Primary research is a time-tested method, therefore, one can rely on the results that are obtained from conducting this type of research.

Disadvantages of Primary Research

1. One of the major disadvantages of primary research is, it can be quite expensive to conduct. One may be required to spend a huge sum of money depending on the setup or primary research method used. Not all businesses or organizations may be able to spend a considerable amount of money.
2. This type of research can be time-consuming. Conducting interviews, sending and receiving online surveys can be quite an exhaustive process and need investing time and patience for the process to work. Moreover, evaluating results and applying the findings to improve product or service will need additional time.
3. Sometimes just using one primary research method may not be enough. In such cases, the use of more than one method is required and this might increase both times required to conduct research and the cost associated with it.

Following all describe for better understand [ Quantitative or Qualitative // Primary or Secondary ]

{Quantitative research is defined as a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires, etc., the results of which can be depicted in the form of numerical. After careful understanding of these numbers to predict the future of a product or service and make changes accordingly.

An example of quantitative research is the survey conducted to understand the amount of time a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey template can be administered to ask questions like how much time did a doctor take to see a patient, how often does a patient walk into a hospital, and other such questions.

Quantitative research is mostly conducted in the social sciences using the statistical methods used above to collect quantitative data from the research study. In this research method, researchers and statisticians deploy mathematical frameworks and theories that pertain to the quantity under question.

Quantitative research templates are objective, elaborate, and many times, even investigational. The results achieved from this research method are logical, statistical, and unbiased. Data collection happened using a structured method and conducted on larger samples that represent the entire population}

{Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication.

This method is not only about “what” people think but also “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that the number of men visiting this store is more. One good method to determine why women were not visiting the store is to conduct an in-depth interview of potential customers in the category.

On successfully interviewing female customers, visiting the nearby stores and malls, and selecting them through random sampling, it was known that the store doesn’t have enough items for women and so there were fewer women visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store because there were more male products than female ones.

Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses, where the interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience takes decision can help derive a conclusion in market research}

{Primary research is defined as a methodology used by researchers to collect data directly, rather than depending on data collected from previously done research. Technically, they “own” the data. Primary research is solely carried out to address a certain problem, which requires in-depth analysis}

{Secondary research or desk research is a research method that involves using already existing data. Existing data is summarized and collated to increase the overall effectiveness of research.

Secondary research includes research material published in research reports and similar documents. These documents can be made available by public libraries, websites, data obtained from already filled in surveys etc. Some government and non-government agencies also store data that can be used for research purposes and can be retrieved from them.

Secondary research is much more cost-effective than primary research, as it makes use of already existing data, unlike primary research where data is collected first hand by organizations or businesses or they can employ a third party to collect data on their behalf}

5. When collected (General Timeline)

Ans:-

A timeline is a very important part of a project proposal. It basically shows the chronological order of events that you plan to do in your project. It is supposed to give the reader a broad overview of the project at a glance. It does not have to be very detailed.

Normally, events are arranged along a horizontal line. The line also has time specifications. Depending on the length of the project, these might be days, weeks, months, or even years. Milestones and events are specified above or underneath the line and connected with the moment on the timeline where they will take place. For this, you can use lines or arrows. If appropriate, you can also include pictures. Be careful though that your timeline is easy to read and does not look too cluttered.

What should you include in your timeline?

A timeline is not a detailed work plan, but a quick way to give your donor an overview of your planned activities. In a timeline, you should include the starting and the ending date of your project and important milestones. You should also make sure that there is a reference to the events that you include in your timeline in your project description.

Do not include too many events as it will make your timeline difficult to read. Also, you need to adjust the scale of your timeline to your project. If you have long periods of time where no events take place, you can decide to depict these periods in a shortened form. Just make sure it is obvious to see, i.e. by using a different colour or pattern.

If the donor requests you to report your progress at specific points in time, you can also include these. In this way, it is easy to see for your donor at a glance what they can expect in your progress reports.

6. Analysis (How results for questions will be produced)

Ans:-

Survey analysis refers to the process of analysing your results from customer (and other) surveys. This can, for example, be Net Promoter Score surveys that you send a few times a year to your customers.

Why do you need to analyse survey data?

Data on its own means nothing without proper analysis. Thus, you need to make sure your survey analysis produces meaningful results that help make decisions that ultimately improve your business.

There are multiple ways of doing this, both manual and through software, which we’ll get to later.

Types of survey data

Data exists as numerical and text data, but for the purpose of this post, we will focus on text responses here.

Close-ended questions

Closed-ended questions can be answered by a simple one-word answer, such as “yes” or “no”. They often consist of pre-populated answers for the respondent to choose from; while an open-ended question asks the respondent to provide feedback in their own words.

Closed-ended questions come in many forms such as multiple-choice, drop down and ranking questions.

In this case, they don’t allow the respondent to provide original or spontaneous answers but only choose from a list of pre-selected options. Closed-ended questions are the equivalent of being offered milk or orange juice to drink instead of being asked: “What would you like to drink?”

These types of questions are designed to create data that are easily quantifiable, and easy to code, so they’re finally in their nature. They also allow researchers to categorize respondents into groups based on the options they have selected.

Open-ended questions

An open-ended question is the opposite of a closed-ended question. It’s designed to produce a meaningful answer and create rich, qualitative data using the subject’s own knowledge and feelings.

Open-ended questions often begin with words such as “Why” and “How”, or sentences such as “Tell me about” Open-ended questions also tend to be more objective and less leading than closed-ended questions.

How to analyse survey data

How do you find meaningful answers and insights into survey responses?

To improve your survey analysis, use the following 5 steps:

1. Start with the end in mind – what are your top research questions?

2. Filter results by cross-tabulating subgroups

3. Interrogate the data

4. Analyse your results

5. Draw conclusions

1. Check off your top research questions

Go back to your main research questions which you outlined before you started your survey. Don’t have any? You should have set some out when you set a goal for your survey. (More on survey planning below).

A top research question for a business conference could be: “How did the attendees rate the conference overall?”

The percentages in this example show how many respondents answered a particular way, or rather, how many people gave each answer as a proportion of the number of people who answered the question.

Thus, 60% or your respondents (1098 of those surveyed) are planning to return. This is the majority of people, even though almost a third are not planning to come back. Maybe there’s something you can do to convince the 11% who are not sure yet!

2. Filter results by cross-tabulating subgroups

At the start of your survey, you will have set up goals for what you wanted to achieve and exactly which subgroups you wanted to analyse and compare against each other.

This is the time to go back to those and check how they (for example the subgroups; enterprises, small businesses, self-employed) answered, with regards to attending again next year.

For this, you can cross-tabulate, and show the answers per question for each subgroup.

Here, you can see that most of the enterprises and the self-employed must have liked the conference as they’re wanting to come back, but you might have missed the mark with the small businesses.

You can also filter your results based on specific types of respondents, or subgroups. So just look at how one subgroup (women, men) answered the question without comparing.

Then you apply the cross tab to look at different attendees to look at female enterprise attendees, female self-employed attendees etc. Just remember that your sample size will be smaller every time you slice the data this way, so check that you still have a valid enough sample size.

3. Interrogate the data

Look at your survey questions and really interrogate them. The following are some questions we use for this:

1. What are the most common responses to questions X?

2. Which responses are affecting/impacting us the most?

3. What’s different about this month/this year?

4. What did respondents in group Y say?

5. Which group of respondents are most affected by issue Z?

6. Have customers noticed our efforts in solving issue Z?

7. What do people say about Z?

For example, look at question 1 and 2. The difference between the two is that the first one returns the volume, whereas in the second one we can look at the volume relating to a particular satisfaction score. If something is very common, it may not affect the score. But if, for example, your Detractors in an NPS survey mention something a lot, that particular theme will be affecting the score in a negative way. These two questions are important to take hand in hand.

You can also compare different slices of the data, such as two different time periods, or two groups of respondents. Or, look at a particular issue or a theme, and ask questions such as “have customers noticed our efforts in solving a particular issue?”, if you’re conducting a continuous survey over multiple months or years.

Best practices for analysing survey data

Make sure you incorporate these tips in your analysis, to ensure your survey results are successful.

1. Ensure the sample size is sufficient

To always make sure you have a sufficient sample size, consider how many people you need to survey in order to get an accurate result.

You most often will not be able to, and shouldn’t for practicality reasons, collect data from all of the people you want to speak to. So you’d take a sample (or subset) of the people of interest and learn what we can from that sample.

Clearly, if you are working with larger sample size, your results will be more reliable as they will often be more precise. Larger sample size does often equate to needing a bigger budget though.

The way to get around this issue is to perform a sample size calculation before starting a survey. Then, you can have a large enough sample size to draw meaningful conclusions, without wasting time and money on sampling more than you really need.

Consider how much margin of error you’re comfortable working with first, as your sample size is always an estimate of how the overall population think and behave.

2. Statistical significance – and why it matters

How do you know you can “trust” your survey analysis i.e. that you can use the answers with confidence as a basis for your decision making? In this regard, the “significant” in statistical significance refers to how accurate your data is. Or rather, that your results are not based on pure chance, but that they are in fact, representative of a sample. If your data has statistical significance, it means that to a large extent, the survey results are meaningful.

It also shows that your respondents “look like” the total population of people about whom you want to draw conclusions.

3. Focus on your insights, not the data

When presenting to your stakeholders, it’s imperative to highlight the insights derived from your data, rather than the data itself.

You’ll do yourself a disservice. Don’t even present the information from the data. Don’t wait for your team to create insights out of the data, you’ll get a better response and better feedback if you are the one that demonstrates the insights, to begin with, as it goes beyond just sharing percentages and data breakouts.

4. Complement with other types of data

Don’t stop at the survey data alone. When presenting your insights, to your stakeholders or board, it’s always helpful to use different data points and which might include even personal experiences. If you have personal experience with the topic, use it! If you have qualitative research that supports the data, use it!

So, if you can overlap qualitative research findings with your quantitative data, do so.

Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.

Three ways to code open-ended responses

When you analyse open-ended responses, you need to code them. Coding open-ended questions have 3 approaches, here’s a taster:

1. Manual coding by someone internally. If you receive 100-200 responses per month, this is absolutely doable. The big disadvantage here is that there is a high likelihood that whoever codes your text will apply their own biases and simply not notice particular themes, because they subconsciously don’t think it’s important to monitor.

2. Outsource to an agency. You can email the results and they would simply send back coded responses.

3. Automating the coding. You use an algorithm to simulate the work of a professional human coder.

Whichever way you code text, you want to determine which category a comment falls under. In the below example, any comment about friends and family both fall into the second category. Then, you can easily visualize it as a bar chart.

A few tips on survey design

Good surveys start with smart survey design. Firstly, you need to plan for survey design success. Here are a few tips:

Tips for survey design planning

1. Keep it short

Only include questions that you are actually going to use. You might think there are lots of questions that seem useful, but they can actually negatively affect your survey results. Another reason is that often we ask redundant questions that don’t contribute to the main problem we want to solve. The survey can be as short as three questions.

2. Use open-ended questions first

To avoid enforcing your own assumptions, use open-ended questions first. Often, we start with a few checkboxes or lists, which can be intimidating for survey respondents. An open-ended question feels more inviting and warmer – it makes people feel like you want to hear what they want to say and actually start a conversation. Open-ended questions give you more insightful answers, however, closed questions are easier to respond to, easier to analyse, but they do not create rich insights.

The best approach is to use a mix of both types of questions, as it’s more compelling to answer different types of questions for respondents.

3. Use surveys as a way to present solutions

Your surveys will reveal what areas in your business need extra support or what creates bottlenecks in your service. Use your surveys as a way of presenting solutions to your audience and getting direct feedback on those solutions in a more consultative way.

4. Consider your timing

It’s important to think about the timing of your survey. Take into account when your audience is most likely to respond to your survey and give them the opportunity to do it at their leisure, at the time that suits them.

5. Challenge your assumptions

It’s crucial to challenge your assumptions, as it’s very tempting to make assumptions about why things are the way they are. There is usually more than meets the eye about a person’s preferences and background which can affect the scenario.

6. Have multiple survey-writers

To have multiple survey writer can be helpful, as having people read each other’s work and test the questions helps address the fact that most questions can be interpreted in more than one way.

7. Choose your survey questions carefully

When you’re choosing your survey questions, make it really count. Only use those that can make a difference to your end outcomes.

8. Be prepared to report back results and take action

As a respondent you want to know your responses count, are reviewed and are making a difference. As an incentive, you can share the results with the participants, in the form of a benchmark, or a measurement that you then report to the participants.

9. What’s in it for them?

Always think about what customers (or survey respondents) want and what’s in it for them. Many businesses don’t actually think about this when they send out their surveys.

If you can nail the “what’s in it for me”, you automatically solve many of the possible issues for the survey, such as whether the respondents have enough incentive or not, or if the survey is consistent enough.

7. Listed below are four need assessment models:

Ans:- I provide a general introduction for all models and at the end, I prefer the most appropriate model with supporting reasons.

a) Epidemiological Model:-  Epidemiology is the study of the distribution of diseases and other health-related conditions in populations, and the application of this study to control health problems. ... The four types of epidemiologic studies commonly used in radiation research are cluster, ecologic, case-control, and cohort studies.

b) Public health Model:- The public health model emphasizes the overall health of the public. In contrast, traditional healthcare focuses on the health of one individual. Public health uses a three-prong approach to prevention and intervention. ... Harm reduction is a specific type of public health strategy.

c) Social Model:-  The social model of disability says that disability is caused by the way society is organised, rather than by a person's impairment or difference. It looks at ways of removing barriers that restrict life choices for disabled people. Social model theory refers to the social barriers imposed on disabled people and posits that these are "caused by the way society is organised, rather than by a person's impairment or difference"

d) Asset Model:-  Asset/liability modelling is the process used to manage the business and financial objectives of a financial institution or an individual through an assessment of the portfolio assets and liabilities in an integrated manner.

•    Which model would you use and why? Support reasoning

Public health Model is the most appropriate way to apply in this case and we can say that in another way Community health assessment.

A public health researcher is a type of public health professional who is charged with researching all matters of public health. Developing new research procedures in order to improve various methods of public health research. Implementing new procedures. Researching health trends within a population.

A community health assessment gives organizations comprehensive information about the community’s current health status, needs, and issues. This information can help develop a community health improvement plan by justifying how and where resources should be allocated to best meet community needs.

Benefits include

• Improved organizational and community coordination and collaboration
• Increased knowledge about public health and the interconnectedness of activities
• Strengthened partnerships within state and local public health systems
• Identified strengths and weaknesses to address in quality improvement efforts
• Baselines on performance to use in preparing for accreditation
• Benchmarks for public health practice improvements

The public health model emphasizes the overall health of the public. In contrast, traditional healthcare focuses on the health of one individual. Public health uses a three-prong approach to prevention and intervention. This is known as "the agent, the host, and the environment." Threats to public health require

1) a susceptible host (e.g., a person)

2) an infectious agent, and

3) a supportive environment (meaning an environment that makes the spread of infection possible such as unsanitary or unsafe living conditions).

The public health model originally developed his 3-sided triangular model for infectious disease. The model now includes addictions.

According to the public health model, interventions may target any part of this triangle and we would expect public health to improve. For example, an addiction prevention strategy that targets "the host" (a person) teaches children "refusal skills." These skills reduce their susceptibility to addiction. An intervention aimed at the infectious agent (drugs) is to control access to drugs (laws that regulate alcohol and tobacco). Conversely, we could limit exposure to drugs by making them illegal and difficult to obtain. An intervention targeting the environment is a public health campaign that strives to change people's attitudes towards gambling.

Harm reduction is a specific type of public health strategy. Harm reduction is more applicable to drugs and alcohol than activity addictions. Harm reduction accepts that it is not possible to eliminate addiction. Instead, the public health goal becomes reducing the harmful effects of addiction. Because addiction affects both individuals with addictions and their communities, harm reduction seeks to reduce harm through any means necessary. The goal is an overall improvement in public health. For instance, IV drug users who become HIV+ can spread this disease to addicts and non-addicts alike. A harm reduction approach could be a needle exchange program (providing free, clean needles to IV drug users). People who abuse alcohol can kill someone with their car. A harm reduction approach might be a public health campaign that encourages the use of sober "designated drivers." This approach accepts that people will get drunk but reduces harm by providing an alternative to drunk driving.

Other, more controversial examples of the harm reduction approach are methadone maintenance programs and drug consumption rooms. Methadone maintenance programs provide heroin addicts with oral dose methadone to replace IV heroin use, at a supervised medical clinic. Some European nations are experimenting with drug consumption rooms. Here, IV drug users can go to use their own drugs. They receive clean needles and medical monitoring while they are using drugs. These harm reduction strategies have several public health goals:

1) To substitute high-risk drug use, with more hygienic, lower risk drug use,

2) To reduce morbidity, mortality, while promoting the long-term health of users and the general public, and

3) To reduce crime and public nuisance associated with drug use.

In the public health model, recovery consists of intervening at any level (host, agent, or environment) in varying degrees, as needed.