Q1. Explain the steps involved in a research process.

Scientific research involves a systematic process that focuses on being objective and gathering a multitude of information for analysis so that the researcher can come to a conclusion. This process is used in all research and evaluation projects, regardless of the research method (scientific method of inquiry, evaluation research, or action research). The process focuses on testing hunches or ideas in a park and Recreation setting through a systematic process. In this process, the study is documented in such a way that another individual can conduct the same study again. This is referred to as replicating the study. Any research done without documenting the study so that others can review the process and results is not an investigation using the scientific research process. The scientific research process is a multiple-step process where the steps are interlinked with the other steps in the process. If changes are made in one step of the process, the researcher must review all the other steps to ensure that the changes are reflected throughout the process. Parks and Recreation professionals are often involved in conducting research or evaluation projects within the agency. These professionals need to understand the eight Steps of the research process as they apply to conducting a study. Table 2.4 lists the Steps of the research process and provides an example of each step for a sample research study.

Step 1: Identify the Problem The first step in the process is to identify a problem or develop a research question. The research problem may be something the agency identifies as a problem, some knowledge or information that is needed by the agency, or the desire to identify a Recreation trend nationally. In the example in table 2.4, the problem that the agency has identified is childhood obesity, which is a local problem and concern within the community. This serves as the focus of the study.

Step 2: Review the Literature Now that the problem has been identified, the researcher must learn more about the topic under investigation. To do this, the researcher must review the literature related to the research problem. This step provides foundational knowledge about the problem area. The review of literature also educates the researcher about what studies have been conducted in the past, how these studies were conducted, and the conclusions in the problem area. In the obesity study, the review of literature enables the programmer to discover horrifying statistics related to the long-term effects of childhood obesity in terms of health issues, death rates, and projected medical costs. In addition, the programmer finds several articles and information from the Centers for Disease Control and Prevention that describe the benefits of walking 10,000 steps a day. The information discovered during this step helps the programmer fully understand the magnitude of the problem, recognize the future consequences of obesity, and identify a strategy to combat obesity (i.e., walking).

Step 3: Clarify the Problem Many times the initial problem identified in the first step of the process is too large or broad in scope. In step 3 of the process, the researcher clarifies the problem and narrows the scope of the study. This can only be done after the literature has been reviewed. The knowledge gained through the review of literature guides the researcher in clarifying and narrowing the research project. In the example, the programmer has identified childhood obesity as the problem and the purpose of the study. This topic is very broad and could be studied based on genetics, family environment, diet, exercise, self-confidence, leisure activities, or health issues. All of these areas cannot be investigated in a single study; therefore, the problem and purpose of the study must be more clearly defined. The programmer has decided that the purpose of the study is to determine if walking 10,000 steps a day for three days a week will improve the individual’s health. This purpose is more narrowly focused and researchable than the original problem.

Step 4: Clearly Define Terms and Concepts Terms and concepts are words or phrases used in the purpose statement of the study or the description of the study. These items need to be specifically defined as they apply to the study. Terms or concepts often have different definitions depending on who is reading the study. To minimize confusion about what the terms and phrases mean, the researcher must specifically define them for the study. In the obesity study, the concept of “individual’s health” can be defined in hundreds of ways, such as physical, mental, emotional, or spiritual health. For this study, the individual’s health is defined as physical health. The concept of physical health may also be defined and measured in many ways. In this case, the programmer decides to more narrowly define “individual health” to refer to the areas of weight, percentage of body fat, and cholesterol. By defining the terms or concepts more narrowly, the scope of the study is more manageable for the programmer, making it easier to collect the necessary data for the study. This also makes the concepts more understandable to the reader.

Step 5: Define the Population Research projects can focus on a specific group of people, facilities, park development, employee evaluations, programs, financial status, marketing efforts, or the integration of technology into the operations. For example, if a researcher wants to examine a specific group of people in the community, the study could examine a specific age group, males or females, people living in a specific geographic area, or a specific ethnic group. Literally thousands of options are available to the researcher to specifically identify the group to study. The research problem and the purpose of the study assist the researcher in identifying the group to involve in the study. In research terms, the group to involve in the study is always called the population. Defining the population assists the researcher in several ways. First, it narrows the scope of the study from a very large population to one that is manageable. Second, the population identifies the group that the researcher’s efforts will be focused on within the study. This helps ensure that the researcher stays on the right path during the study. Finally, by defining the population, the researcher identifies the group that the results will apply to at the conclusion of the study. In the example in table 2.4, the programmer has identified the population of the study as children ages 10 to 12 years. This narrower population makes the study more manageable in terms of time and resources.

Step 6: Develop the Instrumentation Plan The plan for the study is referred to as the instrumentation plan. The instrumentation plan serves as the road map for the entire study, specifying who will participate in the study; how, when, and where data will be collected; and the content of the program. This plan is composed of numerous decisions and considerations that are addressed in chapter 8 of this text. In the obesity study, the researcher has decided to have the children participate in a walking program for six months. The group of participants is called the sample, which is a smaller group selected from the population specified for the study. The study cannot possibly include every 10- to 12-year-old child in the community, so a smaller group is used to represent the population. The researcher develops the plan for the walking program, indicating what data will be collected, when and how the data will be collected, who will collect the data, and how the data will be analyzed. The instrumentation plan specifies all the steps that must be completed for the study. This ensures that the programmer has carefully thought through all these decisions and that she provides a step-by-step plan to be followed in the study.

Step 7: Collect Data Once the instrumentation plan is completed, the actual study begins with the collection of data. The collection of data is a critical step in providing the information needed to answer the research question. Every study includes the collection of some type of data—whether it is from the literature or from subjects—to answer the research question. Data can be collected in the form of words on a survey, with a questionnaire, through observations, or from the literature. In the obesity study, the programmers will be collecting data on the defined variables: weight, percentage of body fat, cholesterol levels, and the number of days the person walked a total of 10,000 steps during the class. The researcher collects these data at the first session and at the last session of the program. These two sets of data are necessary to determine the effect of the walking program on weight, body fat, and cholesterol level. Once the data are collected on the variables, the researcher is ready to move to the final step of the process, which is the data analysis.

Step 8: Analyze the Data All the time, effort, and resources dedicated to steps 1 through 7 of the research process culminate in this final step. The researcher finally has data to analyze so that the research question can be answered. In the instrumentation plan, the researcher specified how the data will be analyzed. The researcher now analyzes the data according to the plan. The results of this analysis are then reviewed and summarized in a manner directly related to the research questions. In the obesity study, the researcher compares the measurements of weight, percentage of body fat, and cholesterol that were taken at the first meeting of the subjects to the measurements of the same variables at the final program session. These two sets of data will be analyzed to determine if there was a difference between the first measurement and the second measurement for each individual in the program. Then, the data will be analyzed to determine if the differences are statistically significant. If the differences are statistically significant, the study validates the theory that was the focus of the study. The results of the study also provide valuable information about one strategy to combat childhood obesity in the community.

As you have probably concluded, conducting studies using the eight steps of the scientific research process requires you to dedicate time and effort to the planning process. You cannot conduct a study using the scientific research process when time is limited or the study is done at the last minute. Researchers who do this conduct studies that result in either false conclusions or conclusions that are not of any value to the organization.

Q2.What are descriptive research designs? Explain the different kinds of descriptive research designs.

A descriptive study is one in which information is collected without changing the environment (i.e., nothing is manipulated). Sometimes these are referred to as “ correlational ” or “ observational ” studies. The Office of Human Research Protections (OHRP) defines a descriptive study as “Any study that is not truly experimental.” In human research, a descriptive study can provide information about the naturally occurring health status, behaviour, attitudes or other characteristics of a particular group. Descriptive studies are also conducted to demonstrate associations or relationships between things in the world around you.

Descriptive studies can involve a one-time interaction with groups of people ( cross-sectional study ) or a study might follow individuals over time ( longitudinal study ). Descriptive studies, in which the researcher interacts with the participant, may involve surveys or interviews to collect the necessary information. Descriptive studies in which the researcher does not interact with the participant include observational studies of people in an environment and studies involving data collection using existing records (e.g., medical record review).

There are three main types of descriptive methods: observational methods, case-study methods and survey methods. This article will briefly describe each of these methods, their advantages, and their drawbacks. This may help you better understand research findings, whether reported in the mainstream media, or when reading a research study on your own.

Observational Method With the observational method (sometimes referred to as field observation) animal and human behavior is closely observed.  There are two main categories of the observational method — naturalistic observation and laboratory observation. 

The biggest advantage of the naturalistic method of research is that researchers view participants in their natural environments.  This leads to greater ecological validity than laboratory observation, proponents say.  Ecological validity refers to the extent to which research can be used in real-life situations. Proponents of laboratory observation often suggest that due to more control in the laboratory, the results found when using laboratory observation are more meaningful than those obtained with naturalistic observation. Laboratory observations are usually less time-consuming and cheaper than naturalistic observations.   Of course, both naturalistic and laboratory observation are important in regard to the advancement of scientific knowledge.

Case Study Method Case study research involves an in-depth study of an individual or group of indviduals.  Case studies often lead to testable hypotheses and allow us to study rare phenomena.  Case studies should not be used to determine cause and effect, and they have limited use for making accurate predictions.   

There are two serious problems with case studies — expectancy effects and atypical individuals. Expectancy effects include the experimenter’s underlying biases that might affect the actions taken while conducting research.  These biases can lead to misrepresenting participants’ descriptions.  Describing atypical individuals may lead to poor generalizations and detract from external validity. 

Survey Method In survey method research, participants answer questions administered through interviews or questionnaires.  After participants answer the questions, researchers describe the responses given. In order for the survey to be both reliable and valid it is important that the questions are constructed properly.  Questions should be written so they are clear and easy to comprehend.

Another consideration when designing questions is whether to include open-ended, closed-ended, partially open-ended, or rating-scale questions

Q3. Explain the concepts of reliability, validity and sensitivity.

Reliability The reliability of an assessment tool is the extent to which it consistently and accurately measures learning. When the results of an assessment are reliable, we can be confident that repeated or equivalent assessments will provide consistent results. This puts us in a better position to make generalised statements about a student’s level of achievement, which is especially important when we are using the results of an assessment to make decisions about teaching and learning, or when we are reporting back to students and their parents or caregivers. No results, however, can be completely reliable. There is always some random variation that may affect the assessment, so educators should always be prepared to question results.

Factors which can affect reliability:

The length of the assessment – a longer assessment generally produces more reliable results.

The suitability of the questions or tasks for the students being assessed.

The phrasing and terminology of the questions.

The consistency in test administration – for example, the length of time given for the assessment, instructions given to students before the test.

The design of the marking schedule and moderation of marking procedures.

The readiness of students for the assessment – for example, a hot afternoon or straight after physical activity might not be the best time for students to be assessed.

How to be sure that a formal assessment tool is reliable

Check in the user manual for evidence of the reliability coefficient. These are measured between zero and 1. A coefficient of 0.9 or more indicates a high degree of reliability.

Assessment tool manuals contain comprehensive administration guidelines. It is essential to read the manual thoroughly before conducting the assessment.

Validity Educational assessment should always have a clear purpose. Nothing will be gained from assessment unless the assessment has some validity for the purpose. For that reason, validity is the most important single attribute of a good test.

The validity of an assessment tool is the extent to which it measures what it was designed to measure, without contamination from other characteristics. For example, a test of reading comprehension should not require mathematical ability.

There are several different types of validity:

Face validity: do the assessment items appear to be appropriate?

Content validity: does the assessment content cover what you want to assess?

Criterion-related validity: how well does the test measure what you want it to?

Construct validity: are you measuring what you think you're measuring?

It is fairly obvious that a valid assessment should have a good coverage of the criteria (concepts, skills and knowledge) relevant to the purpose of the examination. The important notion here is the purpose. For example:

The PROBE test is a form of reading running record which measures reading behaviours and includes some comprehension questions. It allows teachers to see the reading strategies that students are using, and potential problems with decoding. The test would not, however, provide in-depth information about a student’s comprehension strategies across a range of texts.

STAR (Supplementary Test of Achievement in Reading) is not designed as a comprehensive test of reading ability. It focuses on assessing students’ vocabulary understanding, basic sentence comprehension and paragraph comprehension. It is most appropriately used for students who don’t score well on more general testing (such as PAT or e-asTTle) as it provides a more fine grained analysis of basic comprehension strategies.

There is an important relationship between reliability and validity. An assessment that has very low reliability will also have low validity; clearly a measurement with very poor accuracy or consistency is unlikely to be fit for its purpose. But, by the same token, the things required to achieve a very high degree of reliability can impact negatively on validity. For example, consistency in assessment conditions leads to greater reliability because it reduces 'noise' (variability) in the results. On the other hand, one of the things that can improve validity is flexibility in assessment tasks and conditions. Such flexibility allows assessment to be set appropriate to the learning context and to be made relevant to particular groups of students. Insisting on highly consistent assessment conditions to attain high reliability will result in little flexibility, and might therefore limit validity.

The Overall Teacher Judgment balances these notions with a balance between the reliability of a formal assessment tool, and the flexibility to use other evidence to make a judgment.

Q4.Explain the questionnaire design process.

The biggest challenge in developing a questionnaire is to translate the objectives of the data collection process into a well-conceptualized and methodologically sound study.

The following questions should be addressed:

Why is this survey being conducted?

What do I need to know?

How will the information be used?

How accurate and timely does the information have to be?

Before designing the questionnaire, many decisions have to be made. These decisions affect the questionnaire, and should be part of the draft plan for a survey. The draft plan should address the following issues:

Survey objectives and data requirements

In order to address the survey's objectives, you should prepare a document that provides a clear and comprehensive statement of the survey's goals, data requirements, and the analysis plan. This document will determine the variables to be measured, and ultimately, the survey questions and response alternatives.

When formulating the questions, consult with subject-matter experts and if possible, members of the target audience. Also, examine questions from other surveys on the same or similar topics. This research will provide you with a useful starting point and will help you create appropriate and informative questions. Make certain that the questions are relevant to the survey objectives and information requirements and ensure that there is an established rationale behind each question. Also, you should explain how the information gathered from these questions will be used and whether they will be good measures of the required data.

Analysis plan The next step in designing a questionnaire is to create an analysis plan. First, outline the questionnaire's objectives and data requirements. Describe the target audience as clearly as possible. Then, identify the reference period (the time period under construction—in the last year, in the last month etc.). Develop a list of the units to be sampled (e.g., students, houses, teachers, etc.). Decide on the method of data collection to be used (e.g., face-to-face interview, telephone interview, mailed questionnaire, etc.). Explain how the questionnaire content, wording, format and pre-testing process will be developed; as well as the procedures put in place to deal with the interviewer training and non-response results. Also, choose the methods to be used during the data processing (e.g., coding, editing etc.). Some of the other issues that can be analysed during this step include estimation methods, result output tabulations, result reports and the analysis. Finally, the last two important issues to be considered are the time required to complete the entire process and the budget that has been allotted to it.

Survey target population Often the target population (the population for which information is required) and the survey population (the population actually covered) differ for practical reasons, even though they should, in actuality, be the same. Sometimes, it is necessary to impose geographical limitations excluding certain parts of the target population because they are inaccessible due to difficulty or cost.

It is also possible that some of the survey concepts and methods that are used can be considered inappropriate for certain parts of the population. For example, consider a survey of post-secondary graduates where the objective is to determine if the graduates found jobs and, if so, what types of jobs. In this case, you might exclude graduates who specialized in religious seminaries or military schools, as these types of graduates would be reasonably assured of securing employment in their respective fields. Thus, the target population might contain only those graduates who graduated from universities, colleges and trade schools.

Method of data collection This next step in questionnaire design involves developing the methods of data collection. This is important step because you need to consider the costs, physical resources, and time required to conduct the survey.

First, select the best method for gathering the required data. Keep in mind that cost and data quality will be directly impacted by the method you choose.

There are several options available: face-to-face interviews or computer assisted personal interviewing (CAPI) are two examples. These methods are administered by a trained interviewer and can have either a structured or unstructured line of questioning. There are also two telephone methods available: telephone interviews or computer assisted telephone interviewing (CATI). Both of these methods are also administered by a trained interviewer, but the telephone versions are structured with a more formal interview schedule. Finally, there is also the option of a collecting data through a self-completed questionnaire. This method allows the respondent to complete the questionnaire without the aid of an interviewer. It is highly structured and can be returned by mail or through a drop-off system.

Size of the survey Since each survey is different, there are no hard and fast rules for determining its size. The deciding factors in the scale of the survey operations are time, cost, operational constraints and the desired precision of the results. Evaluate and assess each of these issues and you will be in a better position to decide the sample size. Also, consider what should be the acceptable level of error in the sample. If there is a lot of variability in the population, the sample size will need to be bigger to obtain the specified level of reliability.

Data processing plansThis processes the questionnaire responses into output. Coding; data capture; editing; dealing with invalid or missing data; and, if necessary creating derived variables are the tasks that will be completed during data processing. In short, the aim in this step is to produce a file of data that is as free of errors as possible.

BudgetSometimes, questionnaire design is decided upon by the amount of money available to do a specific survey. Costs are one of the main justifications for choosing to conduct sample surveys instead of a census. With surveys, it is possible to obtain reasonable results with a relatively small sample or target population. For example, if you need information on all Canadian

citizens over 15 years of age, a survey of a small percentage of these (1,000 or 2,000 depending on the requirements) might provide adequate results.

TimeOne of the advantages of survey sampling is that it permits investigators to produce the information quickly. It is often the case that survey results are required shortly after the need for information has been identified. For example, if an organization wants to conduct a survey to measure the public awareness of a media advertisement campaign, the survey should be conducted shortly after the campaign is undertaken. Since sampling requires a smaller scale of operation, it reduces the data collection and processing time, while allowing for greater design time and more complex processing programs.

Questionnaire testingThis is a fundamental step in developing a questionnaire. Testing helps discover poor wording or ordering of questions; identify errors in the questionnaire layout and instructions; determine problems caused by the respondent's inability or unwillingness to answer the questions; suggest additional response categories that can be pre-coded on the questionnaire; and provide a preliminary indication of the length of the interview and any refusal problems. Testing can include the entire questionnaire or only a particular portion of it. A questionnaire will at some point in time have to be fully tested.

Data quality

This step identifies errors and verifies results. No matter how much planning and testing goes into a survey, something unexpected will often happen. As a result, no survey is ever perfect. Quality assurance programs such as interview training, information editing, computer program testing, non-respondent follow-ups, and data collection and output spot-checks are required to minimize non-sampling errors introduced during various stages of the survey. Statistical quality-control programs ensure that the specified error levels are controlled to minimum.

 

Q5.The procedure of testing hypothesis requires a researcher to adopt several steps. Describe in brief all such steps.

Hypothesis testing is generally used when you are comparing two or more groups.  For example, you might implement protocols for performing intubation on pediatric patients in the pre-hospital setting.  To evaluate whether these protocols were successful in improving intubation rates, you could measure the intubation rate over time in one group randomly assigned to training in the new protocols, and compare this to the intubation rate over time in another control group that did not receive training in the new protocols.

When you are evaluating a hypothesis, you need to account for both the variability in your sample and how large your sample is.  Based on this information, you'd like to make an assessment of whether any differences you see are meaningful, or if they are likely just due to chance.  This is formally done through a process called hypothesis testing.

Five Steps in Hypothesis Testing:

1. Specify the Null Hypothesis 2. Specify the Alternative Hypothesis

3. Set the Significance Level (a)

4. Calculate the Test Statistic and Corresponding P-Value

5. Drawing a Conclusion

Step 1: Specify the Null Hypothesis

The null hypothesis (H0) is a statement of no effect, relationship, or difference between two or more groups or factors.  In research studies, a researcher is usually interested in disproving the null hypothesis.

Step 2: Specify the Alternative Hypothesis

The alternative hypothesis (H1) is the statement that there is an effect or difference.  This is usually the hypothesis the researcher is interested in proving.  The alternative hypothesis can be one-sided (only provides one direction, e.g., lower) or two-sided.  We often use two-sided tests even when our true hypothesis is one-sided because it requires more evidence against the null hypothesis to accept the alternative hypothesis.

Step 3: Set the Significance Level (a)

The significance level (denoted by the Greek letter alpha— a) is generally set at 0.05.  This means that there is a 5% chance that you will accept your alternative hypothesis when your null hypothesis is actually true. The smaller the significance level, the greater the burden of proof needed to reject the null hypothesis, or in other words, to support the alternative hypothesis.

 

Step 4: Calculate the Test Statistic and Corresponding P-Value

In another section we present some basic test statistics to evaluate a hypothesis. Hypothesis testing generally uses a test statistic that compares groups or examines associations between variables.  When describing a single sample without establishing relationships between variables, a confidence interval is commonly used.

The p-value describes the probability of obtaining a sample statistic as or more extreme by chance alone if your null hypothesis is true.  This p-value is determined based on the result of your test statistic.  Your conclusions about the hypothesis are based on your p-value and your significance level. 

1. P-value <= significance level (a) => Reject your null hypothesis in favor of your alternative hypothesis.  Your result is statistically significant.

2. P-value > significance level (a) => Fail to reject your null hypothesis.  Your result is not statistically significant.

Hypothesis testing is not set up so that you can absolutely prove a null hypothesis.  Therefore, when you do not find evidence against the null hypothesis, you fail to reject the null hypothesis. When you do find strong enough evidence against the null hypothesis, you reject the null hypothesis.  Your conclusions also translate into a statement about your alternative hypothesis.  When presenting the results of a hypothesis test, include the descriptive statistics in your conclusions as well.  Report exact p-values rather than a certain range.  For example, "The intubation rate differed significantly by patient age with younger patients have a lower rate of successful intubation (p=0.02)."  Here are two more examples with the conclusion stated in several different ways.

Q6.a. What are the different kinds of research reports available to the researcher?

There are many ways to categorize the different types of research. For example, research in different fields can be called different types of research, such as scientific research, social research, medical research, environmental research and so forth. The research methods that are used and purposes of the research also can be used to categorize the different types of research. A few of these types of research include quantitative and qualitative research; observational and experimental research; and basic, applied and developmental research.

Quantitative and Qualitative

Quantitative research is the collecting of objective numerical data. Features are classified and counted, and statistical models are constructed to analyze and explain the information that has been gathered. Some of the tools used for this type of research include questionnaires that are given to test subjects, equipment that is used to measure something and databases of existing information. The goal of quantitative research is to compile statistical evidence, so the questionnaires used in this method typically include yes-or-no questions or multiple-choice questions rather than open-ended questions such as essay questions.

Observational and Experimental

Observational research is the collection of information without interference or input from the researcher. It is the examination of things as they naturally or inherently are. The researcher simply observes, measures or records what occurs. That information is then analyzed and used to draw conclusions.

Basic, Applied and Developmental

When the purpose of research is simply to reveal or discover what is true, it can be called basic research. This type of research involves exploring that which is not known or understood. Applied research is taking what is already known and looking for ways to use it, such as to solve problems. Developmental research is similar to applied research but focuses on using what is already known to improve products or existing technology or to create something new.

Q 6 b. What should be the ideal structure of a research report?

A. Preliminary Section 1. Title Page 2. Acknowledgments (if any) 3. Table of Contents 4. List of Tables (if any) 5. List of Figures (if any) 6. Abstract

B. Main Body 1. Introduction a. Statement of the Problem b. Significance of the Problem (and historical background) c. Purpose d. Statement of Hypothesis e. Assumptions f. Limitations g. Definition of Terms

2. Review of Related Literature (and analysis of previous research)

3. Design of the Study

a. Description of Research Design and Procedures Used b. Sources of Data c. Sampling Procedures d. Methods and Instruments of Data Gathering e. Statistical Treatment

4. Analysis of Data

contains:

a. text with appropriate b. tables and c. figures

5. Summary and Conclusions

a. Restatement of the Problem b. Description of Procedures c. Major Findings (reject or fail to reject Ho) d. Conclusions e. Recommendations for Further Investigation

C. Reference Section

1. End Notes (if in that format of citation)

2. Bibliography or Literature Cited