AGENDA DRIVEN OUTCOMES

Hi Merit \ Dave Howell \ 8.28.2020

A Precursor to Understanding CDC Covid-19 Deaths

“As leaders go, any leader, owner or group of leaders can justify the outcome they desire by any means.”

Fine, what is an outcome? Outcome [ out-kuhm ] is a noun meaning:

a final product or end result; consequence; issue.

a conclusion reached through a process of logical thinking.

Let me give you a sample of an outcome using a doctor instead of a business leader:

A doctor has assigned the following chances to a medical procedure like a spine disc replacement.

 * Full recovery 55%

  * Condition improves 24%

  * No change 17%

 * Condition worsens 4%

Suppose the procedure is performed on 5 patients. Assume that the procedure is independent for each patient. What is the probability that all five patients will recover completely? Well about 4 of the 5 patients will improve or experience a full recovery.

Let’s not get lost in the math yet, but let’s consider the COVID-19 dilemma:

EXHIBIT A Source: CDC.gov  

NOTES: No increase in mortality rates, given the incidence of influenza and pneumonia. Supporting data from CDC indicates the “Outcomes” among patients ager groups:

Summary

What is already known about this topic?

Early data from China suggest that a majority of coronavirus disease 2019 (COVID-19) deaths have occurred among adults aged ≥60 years and among persons with serious underlying health conditions.

What is added by this report?

This first preliminary description of outcomes among patients with COVID-19 in the United States indicates that fatality was highest in persons aged ≥85, ranging from 10% to 27%, followed by 3% to 11% among persons aged 65–84 years, 1% to 3% among persons aged 55-64 years, <1% among persons aged 20–54 years, and no fatalities among persons aged ≤19 years.

Let’s add some common sense perspective:

US NEWS MONDAY, March 30, 2020 (HealthDay News) -- Once infected with the new coronavirus:

-      20-something has about a 1% chance of illness so severe it requires hospitalization

-      50-plus year olds has about a 8% chance of illness so sever it requires hospitalization

-      80-plus year olds have a 19% chance of illness so severe it requires hospitalization.

On the other hand, the death rate from COVID-19 is significantly lower than that seen in prior estimates, the new report found. Among diagnosed cases, just under 1.4% of patients will die, or said differently 98.6% of all age groups will survive a bout with COVOId-19.

 Medical science has assigned the following chances to a catching COVID-19 and recovering.

·        Full recovery

·        Condition improves

·        No change

·        Condition worsens

What other words describe outcome?

SYNONYMS FOR outcome

·        conclusion

·        event

·        fallout

·        issue

·        reaction

·        result

·        aftereffect

·        aftermath

·        end

·        payback

·        payoff

·        score

·        sequel

·        upshot

·        blowoff

·        causatum

·        chain reaction

·        end result

Research Guide

The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, or the research and development of methods and systems for the advancement of human knowledge. Approaches to research depend on epistemologies, which vary considerably both within and between humanities and sciences. There are several forms of research: scientific, humanities, artistic, economic, social, business, marketing, practitioner research, etc.

Scientific research relies on the application of the scientific method, a harnessing of curiosity. This research provides scientific information and theories for the explanation of the nature and the properties of the world. It makes practical applications possible. Scientific research is funded by public authorities, by charitable organizations and by private groups, including many companies. Scientific research can be subdivided into different classifications according to their academic and application disciplines. Scientific research is a widely used criterion for judging the standing of an academic institution, such as business schools, but some argue that such is an inaccurate assessment of the institution, because the quality of research does not tell about the quality of teaching (these do not necessarily correlate totally).[2]

Research in the humanities involves different methods such as for example hermeneutics and semiotics, and a different, more relativist epistemology. Humanities scholars usually do not search for the ultimate correct answer to a question, but instead explore the issues and details that surround it. Context is always important, and context can be social, historical, political, cultural or ethnic. An example of research in the humanities is historical research, which is embodied in historical method. Historians use primary sources and other evidence to systematically investigate a topic, and then to write histories in the form of accounts of the past.

Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself. It is the debatable body of thought which offers an alternative to purely scientific methods in research in its search for knowledge and truth.

Etymology

Aristotle, 384 BC – 322 BC, - one of the early figures in the development of the

Definitions

Research has been defined in a number of different ways.

A broad definition of research is given by Martyn Shuttleworth - "In the broadest sense of the word, the definition of research includes any gathering of data, information and facts for the advancement of knowledge."[5]

Another definition of research is given by Creswell who states - "Research is a process of steps used to collect and analyze information to increase our understanding of a topic or issue". It consists of three steps: Pose a question, collect data to answer the question, and present an answer to the question.[6]

The Merriam-Webster Online Dictionary defines research in more detail as "a studious inquiry or examination; especially : investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws".[4]

Steps in conducting research

Research is often conducted using the hourglass model structure of research.[7] The hourglass model starts with a broad spectrum for research, focusing in on the required information through the method of the project (like the neck of the hourglass), then expands the research in the form of discussion and results. The major steps in conducting research are:

• Identification of research problem
• Literature review
• Specifying the purpose of research
• Determine specific research questions or hypotheses
• Data collection
• Analyzing and interpreting the data
• Reporting and evaluating research

The steps generally represent the overall process, however they should be viewed as an ever-changing process rather than a fixed set of steps. Most researches begin with a general statement of the problem, or rather, the purpose for engaging in the study. The literature review identifies flaws or holes in previous research which provides justification for the study. Often, a literature review is conducted in a given subject area before a research question is identified. A gap in the current literature, as identified by a researcher, then engenders a research question. The research question may be parallel to the hypothesis. The hypothesis is the supposition to be tested. The researcher(s) collects data to test the hypothesis. The researcher(s) then analyzes and interprets the data via a variety of statistical methods, engaging in what is known as Empirical research. The results of the data analysis in confirming or failing to reject the Null hypothesis are then reported and evaluated. At the end the researcher may discuss avenues for further research.

Rudolph Rummel says, "... no researcher should accept any one or two tests as definitive. It is only when a range of tests are consistent over many kinds of data, researchers, and methods can one have confidence in the results."[11]

Scientific research

Main article: Scientific method

Primary scientific research being carried out at the Microscopy Laboratory of the Idaho National Laboratory.

Scientific research equipment at MIT.

Generally, research is understood to follow a certain structural process. Though step order may vary depending on the subject matter and researcher, the following steps are usually part of most formal research, both basic and applied:

1. Observations and Formation of the topic: Consists of the subject area of ones interest and following that subject area to conduct subject related research. The subject area should not be randomly chosen since it requires reading a vast amount of literature on the topic to determine the gap in the literature the researcher intends to narrow. A keen interest in the chosen subject area is advisable. The research will have to be justified by linking its importance to already existing knowledge about the topic.
2. Hypothesis: A testable prediction which designates the relationship between two or more variables.
3. Conceptual definition: Description of a concept by relating it to other concepts.
4. Operational definition: Details in regards to defining the variables and how they will be measured/assessed in the study.
5. Gathering of data: Consists of identifying a population and selecting samples, gathering information from and/or about these samples by using specific research instruments. The instruments used for data collection must be valid and reliable.
6. Analysis of data: Involves breaking down the individual pieces of data in order to draw conclusions about it.
7. Data Interpretation: This can be represented through tables, figures and pictures, and then described in words.
8. Test, revising of hypothesis
9. Conclusion, reiteration if necessary

A common misconception is that a hypothesis will be proven (see, rather, Null hypothesis). Generally a hypothesis is used to make predictions that can be tested by observing the outcome of an experiment. If the outcome is inconsistent with the hypothesis, then the hypothesis is rejected (see falsifiability). However, if the outcome is consistent with the hypothesis, the experiment is said to support the hypothesis. This careful language is used because researchers recognize that alternative hypotheses may also be consistent with the observations. In this sense, a hypothesis can never be proven, but rather only supported by surviving rounds of scientific testing and, eventually, becoming widely thought of as true.

A useful hypothesis allows prediction and within the accuracy of observation of the time, the prediction will be verified. As the accuracy of observation improves with time, the hypothesis may no longer provide an accurate prediction. In this case a new hypothesis will arise to challenge the old, and to the extent that the new hypothesis makes more accurate predictions than the old, the new will supplant it. Researchers can also use a null hypothesis, which state no relationship or difference between the independent or dependent variables. A null hypothesis uses a sample of all possible people to make a conclusion about the population.[12]

Historical method

Main article: Historical method

German historian Leopold von Ranke (1795-1886), considered to be one of the founders of modern source-based history.

The historical method comprises the techniques and guidelines by which historians use historical sources and other evidence to research and then to write history. There are various history guidelines commonly used by historians in their work, under the headings of external criticism, internal criticism, and synthesis. This includes lower criticism and sensual criticism. Though items may vary depending on the subject matter and researcher, the following concepts are part of most formal historical research:[13]

• Identification of origin date
• Evidence of localization
• Recognition of authorship
• Analysis of data
• Identification of integrity

Research Methods

To understand the use of statistics, one needs to know a little bit about experimental design or how a researcher conducts investigations. A little knowledge about methodology will provide us with a place to hang our statistics. In other words, statistics are not numbers that just appear out of nowhere. Rather, the numbers (data) are generated out of research. Statistics are merely a tool to help us answer research questions. As such, an understanding of methodology will facilitate our understanding of basic statistics.

Validity

A key concept relevant to a discussion of research methodology is that of validity. When an individual asks, "Is this study valid?", they are questioning the validity of at least one aspect of the study. There are four types of validity that can be discussed in relation to research and statistics. Thus, when discussing the validity of a study, one must be specific as to which type of validity is under discussion. Therefore, the answer to the question asked above might be that the study is valid in relation to one type of validity but invalid in relation to another type of validity.

Each of the four types of validity will be briefly defined and described below. Be aware that this represents a cursory discussion of the concept of validity. Each type of validity has many threats which can pose a problem in a research study. Examples, but not an exhaustive discussion, of threats to each validity will be provided. For a comprehensive discussion of the four types of validity, the threats associated with each type of validity, and additional validity issues see Cook and Campbell (1979).

Statistical Conclusion Validity: Unfortunately, without a background in basic statistics, this type of validity is difficult to understand. According to Cook and Campbell (1979), "statistical conclusion validity refers to inferences about whether it is reasonable to presume covariation given a specified alpha level and the obtained variances (p. 41)." Essentially, the question that is being asked is - "Are the variables under study related?" or "Is variable A correlated (does it covary) with Variable B?". If a study has good statistical conclusion validity, we should be relatively certain that the answer to these questions is "yes". Examples of issues or problems that would threaten statistical conclusion validity would be random heterogeneity of the research subjects (the subjects represent a diverse group - this increases statistical error) and small sample size (more difficult to find meaningful relationships with a small number of subjects).

Internal Validity: Once it has been determined that the two variables (A & B) are related, the next issue to be determined is one of causality. Does A cause B? If a study is lacking internal validity, one can not make cause and effect statements based on the research; the study would be descriptive but not causal. There are many potential threats to internal validity. For example, if a study has a pretest, an experimental treatment, and a follow-up posttest, history is a threat to internal validity. If a difference is found between the pretest and posttest, it might be due to the experimental treatment but it might also be due to any other event that subjects experienced between the two times of testing (for example, a historical event, a change in weather, etc.).

Construct Validity: One is examining the issue of construct validity when one is asking the questions "Am I really measuring the construct that I want to study?" or "Is my study confounded (Am I confusing constructs)?". For example, if I want to know a particular drug (Variable A) will be effective for treating depression (Variable B) , I will need at least one measure of depression. If that measure does not truly reflect depression levels but rather anxiety levels (Confounding Variable X), than my study will be lacking construct validity. Thus, good construct validity means the we will be relatively sure that Construct A is related to Construct B and that this is possibly a causal relationship. Examples of other threats to construct validity include subjects apprehension about being evaluated, hypothesis guessing on the part of subjects, and bias introduced in a study by expectencies on the part of the experimenter.

External Validity: External validity addresses the issue of being able to generalize the results of your study to other times, places, and persons. For example, if you conduct a study looking at heart disease in men, can these results be generalized to women? Therefore, one needs to ask the following questions to determine if a threat to the external validity exists: "Would I find these same results with a difference sample?", "Would I get these same results if I conducted my study in a different setting?", and "Would I get these same results if I had conducted this study in the past or if I redo this study in the future?" If I can not answer "yes" to each of these questions, then the external validity of my study is threatened.

Types of Research Studies

There are four major classifications of research designs. These include observational research, correlational research, true experiments, and quasi-experiments. Each of these will be discussed further below.

Observational research: There are many types of studies which could be defined as observational research including case studies, ethnographic studies, ethological studies, etc. The primary characteristic of each of these types of studies is that phenomena are being observed and recorded. Often times, the studies are qualitative in nature. For example, a psychological case study would entail extensive notes based on observations of and interviews with the client. A detailed report with analysis would be written and reported constituting the study of this individual case. These studies may also be qualitative in nature or include qualitative components in the research. For example, an ethological study of primate behavior in the wild may include measures of behavior durations ie. the amount of time an animal engaged in a specified behavior. This measure of time would be qualitative.

Surveys are often classified as a type of observational research.

Correlational research: In general, correlational research examines the covariation of two or more variables. For example, the early research on cigarette smoking examine the covariation of cigarette smoking and a variety of lung diseases. These two variable, smoking and lung disease were found to covary together.

Correlational research can be accomplished by a variety of techniques which include the collection of empirical data. Often times, correlational research is considered type of observational research as nothing is manipulated by the experimenter or individual conducting the research. For example, the early studies on cigarette smoking did not manipulate how many cigarettes were smoked. The researcher only collected the data on the two variables. Nothing was controlled by the researchers.

It is important to not that correlational research is not causal research. In other words, we can not make statements concerning cause and effect on the basis of this type of research. There are two major reasons why we can not make cause and effect statements. First, we don?1t know the direction of the cause. Second, a third variable may be involved of which we are not aware. An example may help clarify these points.

In major clinical depressions, the neurotransmitters serotonin and/or norepinephrine have been found to be depleted (Coppen, 1967; Schildkraut & Kety, 1967). In other words, low levels of these two neurotransmitters have been found to be associated with increased levels of clinical depression. However, while we know that the two variables covary - a relationship exists - we do not know if a causal relationship exists. Thus, it is unclear whether a depletion in serotonin/norepinephrine cause depression or whether depression causes a depletion is neurotransmitter levels. This demonstrates the first problem with correlational research; we don't know the direction of the cause. Second, a third variable has been uncovered which may be affecting both of the variables under study. The number of receptors on the postsynaptic neuron has been found to be increased in depression (Segal, Kuczenski, & Mandell, 1974; Ventulani, Staqarz, Dingell, & Sulser, 1976). Thus, it is possible that the increased number of receptors on the postsynaptic neuron is actually responsible for the relationship between neurotransmitter levels and depression. As you can see from the discussion above, one can not make a simple cause and effect statement concerning neurotransmitter levels and depression based on correlational research. To reiterate, it is inappropriate in correlational research to make statements concerning cause and effect.

Correlational research is often conducted as exploratory or beginning research. Once variables have been identified and defined, experiments are conductable.

True Experiments: The true experiment is often thought of as a laboratory study. However, this is not always the case. A true experiment is defined as an experiment conducted where an effort is made to impose control over all other variables except the one under study. It is often easier to impose this sort of control in a laboratory setting. Thus, true experiments have often been erroneously identified as laboratory studies.

To understand the nature of the experiment, we must first define a few terms:

1. Experimental or treatment group - this is the group that receives the experimental treatment, manipulation, or is different from the control group on the variable under study.
2. Control group - this group is used to produce comparisons. The treatment of interest is deliberately withheld or manipulated to provide a baseline performance with which to compare the experimental or treatment group's performance.
3. Independent variable - this is the variable that the experimenter manipulates in a study. It can be any aspect of the environment that is empirically investigated for the purpose of examining its influence on the dependent variable.
4. Dependent variable - the variable that is measured in a study. The experimenter does not control this variable.
5. Random assignment - in a study, each subject has an equal probability of being selected for either the treatment or control group.
6. Double blind - neither the subject nor the experimenter knows whether the subject is in the treatment of the control condition.

Now that we have these terms defined, we can examine further the structure of the true experiment. First, every experiment must have at least two groups: an experimental and a control group. Each group will receive a level of the independent variable. The dependent variable will be measured to determine if the independent variable has an effect. As stated previously, the control group will provide us with a baseline for comparison. All subjects should be randomly assigned to groups, be tested a simultaneously as possible, and the experiment should be conducted double blind. Perhaps an example will help clarify these points.

Wolfer and Visintainer (1975) examined the effects of systematic preparation and support on children who were scheduled for inpatient minor surgery. The hypothesis was that such preparation would reduce the amount of psychological upset and increase the amount of cooperation among thee young patients. Eighty children were selected to participate in the study. Children were randomly assigned to either the treatment or the control condition. During their hospitalization the treatment group received the special program and the control group did not. Care was take such that kids in the treatment and the control groups were not roomed together. Measures that were taken included heart rates before and after blood tests, ease of fluid intake, and self-report anxiety measures. The study demonstrated that the systematic preparation and support reduced the difficulties of being in the hospital for these kids.

Let us examine now the features of the experiment described above. First, there was a treatment and control group. If we had had only the treatment group, we would have no way of knowing whether the reduced anxiety was due to the treatment or the weather, new hospital food, etc. The control group provides us with the basis to make comparisons The independent variable in this study was the presence or absence of the systematic preparation program. The dependent variable consisted of the heart rates, fluid intake, and anxiety measures. The scores on these measures were influenced by and depended on whether the child was in the treatment or control group. The children were randomly assigned to either group. If the "friendly" children had been placed in the treatment group we would have no way of knowing whether they were less anxious and more cooperative because of the treatment or because they were "friendly". In theory, the random assignment should balance the number of "friendly" children between the two groups. The two groups were also tested at about the same time. In other words, one group was not measured during the summer and the other during the winter. By testing the two groups as simultaneously as possible, we can rule out any bias due to time. Finally, the children were unaware that they were participants in an experiment (the parents had agreed to their children's participation in research and the program), thus making the study single blind. If the individuals who were responsible for the dependent measures were also unaware of whether the child was in the treatment or control group, then the experiment would have been double blind.

A special case of the true experiment is the clinical trial. A clinical trial is defined as a carefully designed experiment that seeks to determine the clinical efficacy of a new treatment or drug. The design of a clinical trial is very similar to that of a true experiment. Once again, there are two groups: a treatment group (the group that receives the therapeutic agent) and a control group (the group that receives the placebo). The control group is often called the placebo group. The independent variable in the clinical trial is the level of the therapeutic agent. Once again, subjects are randomly assigned to groups, they are tested simultaneously, and the experiment should be conducted double blind. In other words, neither the patient or the person administering the drug should know whether the patient is receiving the drug or the placebo.

Quasi-Experiments: Quasi-experiments are very similar to true experiments but use naturally formed or pre-existing groups. For example, if we wanted to compare young and old subjects on lung capacity, it is impossible to randomly assign subjects to either the young or old group (naturally formed groups). Therefore, this can not be a true experiment. When one has naturally formed groups, the variable under study is a subject variable (in this case - age) as opposed to an independent variable. As such, it also limits the conclusions we can draw from such an research study. If we were to conduct the quasi-experiment, we would find that the older group had less lung capacity as compared to the younger group. We might conclude that old age thus results in less lung capacity. But other variables might also account for this result. It might be that repeated exposure to pollutants as opposed to age has caused the difference in lung capacity. It could also be a generational factor. Perhaps more of the older group smoked in their early years as compared to the younger group due to increased awareness of the hazards of cigarettes. The point is that there are many differences between the groups that we can not control that could account for differences in our dependent measures. Thus, we must be careful concerning making statement of causality with quasi-experimental designs.

Quasi-experiments may result from studying the differences between naturally formed groups (ie. young & old; men & women). However, there are also instances when a researcher designs a study as a traditional experiment only to discover that random assignment to groups is restricted by outside factors. The researcher is forced to divide groups according to some pre-existing criteria. For example, if a corporation wanted to test the effectiveness of a new wellness program, they might decide to implement their program at one site and use a comporable site (no wellness program) as a control. As the employees are not shuffled and randomly assigned to work at each site, the study has pre-existing groups. After a few months of study, the researchers could then see if the wellness site had less absenteeism and lower health costs than the non-wellness site. The results are again restricted due to the quasi-correlational nature of the study. As the study has pre-existing groups, there may be other differences between those groups than just the presence or absence of a wellness program. For example, the wellness program may be in a significantly newer, more attractive building, or the manager from hell may work at the nonwellness program site. Either way, it a difference is found between the two sites it may or may not be due to the presence/absence of the wellness program.

To summarize, quasi-experiments may result from either studying naturally formed groups or use of pre-existing groups. When the study includes naturally formed groups, the variable under study is a subject variable. When a study uses pre-existing groups that are not naturally formed, the variable that is manipulated between the two groups is an independent variable (With the exception of no random assignment, the study looks similar in form to a true experiment). As no random assignment exists in a quasi-experiment, no causal statements can be made based on the results of the study.

Populations and Samples

When conducting research, one must often use a sample of the population as opposed to using the entire population. Before we go further into the reasons why, let us first discuss what differentiates between a population and a sample.

A population can be defined as any set of persons/subjects having a common observable characteristic. For example, all individuals who reside in the United States make up a population. Also, all pregnant women make up a population. The characteristics of a population are called a parameter. A statistic can be defined as any subset of the population. The characteristics of a sample are called a statistic.

Why Sample?

This brings us to the question of why sample. Why should we not use the population as the focus of study. There are at least four major reasons to sample.

First, it is usually too costly to test the entire population. The United States government spends millions of dollars to conduct the U.S. Census every ten years. While the U.S. government may have that kind of money, most researchers do not.

The second reason to sample is that it may be impossible to test the entire population. For example, let us say that we wanted to test the 5-HIAA (a serotonergic metabolite) levels in the cerebrospinal fluid (CSF) of depressed individuals. There are far too many individuals who do not make it into the mental health system to even be identified as depressed, let alone to test their CSF.

The third reason to sample is that testing the entire population often produces error. Thus, sampling may be more accurate. Perhaps an example will help clarify this point. Say researchers wanted to examine the effectiveness of a new drug on Alzheimer's disease. One dependent variable that could be used is an Activities of Daily Living Checklist. In other words, it is a measure of functioning o a day to day basis. In this experiment, it would make sense to have as few of people rating the patients as possible. If one individual rates the entire sample, there will be some measure of consistency from one patient to the next. If many raters are used, this introduces a source of error. These raters may all use a slightly different criteria for judging Activities of Daily Living. Thus, as in this example, it would be problematic to study an entire population.

The final reason to sample is that testing may be destructive. It makes no sense to lesion the lateral hypothalamus of all rats to determine if it has an effect on food intake. We can get that information from operating on a small sample of rats. Also, you probably would not want to buy a car that had the door slammed five hundred thousand time or had been crash tested. Rather, you probably would want to purchase the car that did not make it into either of those samples.

Types of Sampling Procedures

As stated above, a sample consists of a subset of the population. Any member of the defined population can be included in a sample. A theoretical list (an actual list may not exist) of individuals or elements who make up a population is called a sampling frame. There are five major sampling procedures.

The first sampling procedure is convenience. Volunteers, members of a class, individuals in the hospital with the specific diagnosis being studied are examples of often used convenience samples. This is by far the most often used sample procedure. It is also by far the most biases sampling procedure as it is not random (not everyone in the population has an equal chance of being selected to participate in the study). Thus, individuals who volunteer to participate in an exersice study may be different that individuals who do not volunteer.

Another form of sampling is the simple random sample. In this method, all subject or elements have an equal probability of being selected. There are two major ways of conducting a random sample. The first is to consult a random number table, and the second is to have the computer select a random sample.

A systematic sample is conducted by randomly selecting a first case on a list of the population and then proceeding every Nth case until your sample is selected. This is particularly useful if your list of the population is long. For example, if your list was the phone book, it would be easiest to start at perhaps the 17th person, and then select every 50th person from that point on.

Stratified sampling makes up the fourth sampling strategy. In a stratified sample, we sample either proportionately or equally to represent various strata or subpopulations. For example if our strata were states we would make sure and sample from each of the fifty states. If our strata were religious affiliation, stratified sampling would ensure sampling from every religious block or grouping. If our strata were gender, we would sample both men and women.

Cluster sampling makes up the final sampling procedure. In cluster sampling we take a random sample of strata and then survey every member of the group. For example, if our strata were individuals schools in the St. Louis Public School System, we would randomly select perhaps 20 schools and then test all of the students within those schools.

Sampling Problems

There are several potential sampling problems. When designing a study, a sampling procedure is also developed including the potential sampling frame. Several problems may exist within the sampling frame. First, there may be missing elements - individuals who should be on your list but for some reason are not on the list. For example, if my population consists of all individuals living in a particular city and I use the phone directory as my sampling frame or list, I will miss individuals with unlisted numbers or who can not afford a phone.

Foreign elements make up my second sampling problem. Elements which should not be included in my population and sample appear on my sampling list. Thus, if I were to use property records to create my list of individuals living within a particular city, landlords who live elsewhere would be foreign elements. In this case, renters would be missing elements.

Duplicates represent the third sampling problem. These are elements who appear more than once on the sampling frame. For example, if I am a researcher studying patient satisfaction with emergency room care, I may potentially include the same patient more than once in my study. If the patients are completing a patient satisfaction questionnaire, I need to make sure that patients are aware that if they have completed the questionnaire previously, they should not complete it again. If they complete it more that once, their second set of data respresents a duplicate.

A wide range of research methods are used in psychology. These methods vary by the sources of information that are drawn on, how that information is sampled, and the types of instruments that are used in data collection. Methods also vary by whether they collect qualitative data, quantitative data or both.

Qualitative psychological research is where the research findings are not arrived at by statistical or other quantitative procedures. Quantitative psychological research is where the research findings result from mathematical modeling and statistical estimation or statistical inference. Since qualitative information can be handled as such statistically, the distinction relates to method, rather than the topic studied.

There are three main types of psychological research:

• Correlational research
• Descriptive research
• Experimental research

The following are common research designs and data collection methods:

• Archival research
• Case study
• Computer simulation (modeling)
• Content analysis
• Experiment, often with separate treatment and control groups (see scientific control and design of experiments)
• Field experiment
• Interview, can be structured or unstructured.
• Meta-analysis
• Neuroimaging and other psychophysiological methods
• Observational study, can be naturalistic (see natural experiment), participant or controlled.
• Quasi-experiment
• Self-report inventory
• Survey, often with a random sample (see survey sampling)
• Twin study

Human subject research is a systematic investigation that can be either research or clinically oriented and involves the use of human subjects in any capacity.[1] Systematic investigation incorporates both the collection and analysis of data in order to answer a specific question. Examples of research oriented investigation include surveys, questionnaires, interviews, and focus groups. Examples of clinically oriented investigation include analysis of biological specimens, epidemiological and behavioral studies and medical chart review studies.[1] Human subject research is used in various fields including research on basic biology, clinical medicine, nursing, psychology, sociology, political science, and anthropology. As research has become formalized the academic community has developed formal definitions of "human subject research", largely in response to abuses of human subjects.

Human subject rights

• Voluntary, informed consent
• Respect for persons: treated as autonomous agents
• The right to end participation in research at any time[4]
• Right to safeguard integrity[4]
• Benefits should outweigh cost
• Protection from physical, mental and emotional harm
• Access to information regarding research[4]
• Protection of privacy and well-being

SF424 (R&R) Application Guides for Restructured Application Forms:

To be used with applications that indicate “ADOBE-FORMS-B1,” “ADOBE-FORMS-B2,” or “ADOBE-FORMS-C” in the “Competition ID” field of the SF424 (R&R) forms package.

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MS Word  (2 MB)

PDF  (2.6 MB)

SBIR/STTR Application Guide SF424 (R&R) - Adobe Forms Version B Series

01/23/2013

MS Word  (2.9 MB)

PDF  (2.7 MB)

Individual Fellowship Application Guide SF424 (R&R) - Adobe Forms Version B Series

07/25/2011

MS Word  (2.3 MB)

PDF   (2.5 MB)

Supplemental Grant Application Instructions For All Competing Applications and Progress Reports

07/25/2013

MS Word  (476 KB)

PDF  (991 KB)

Additional Format Pages

NIH requires all text attachments in an SF424 (R&R) application to be PDF. However, to avoid system errors, applicants should create text attachments using word processing software and then convert to PDF using PDF-generating software. While Word samples are provided below, applicants will need to convert the finished product to PDF before attaching within an SF424 (R&R) application. Do not use the PDF samples from the PHS398 application page. Those are fillable-PDF forms which will cause an error in the electronic submission of an SF424 (R&R) applications.

Additional Format Pages

Date Posted

File Link/Format/Size

Biographical Sketch Format Page – Adobe Forms Version B

(use also for Fellowship Sponsor/Co-Sponsors)

11/13/2009

MS Word  (36 KB)

Biographical Sketch Format Page – Adobe Forms Version C

(use also for Fellowship Sponsor/Co-Sponsors)

07/25/2013

MS Word  (36 KB)

Biographical Sketch Sample – Adobe Forms Version B

03/25/2011

MS Word  (44 KB)

Biographical Sketch Sample – Adobe Forms Version C

07/25/2013

MS Word  (45 KB)

wship Applicant Biographical Sketch Format Page

02/05/2010

MS Word  (52 KB)

Fellowship Application Biographical Sketch Sample

04/19/2011

MS Word  (79 KB)

Referee Instructions for Mentored Career Development Awards

08/11/2011

MS Word  (38 KB)

References for Fellowship Awards

02/05/2010

MS Word  (49 KB)

Planned Enrollment Report

07/25/2013

PDF  (929 KB)

Cumulative Inclusion Enrollment Report

07/25/2013

PDF  (945 KB)

Modular Budget Sample: Same Modules

11/08/2005

PDF  (27 KB)

Modular Budget Sample: Variable Modules

07/23/2008

PDF  (26 KB)

Additional Senior/Key Person Profiles (Format provided for those applications requiring: Forms B - > 8 Senior/Key Person Profiles; Forms C - >100 Senior/Key Person Profiles)

07/05/2006

MS Word  (51 KB)

Additional Performance Sites (Forms B - > 30 Project Performance Sites; Forms C - >300 Project Performance Sites)

07/05/2006

MS Word  (47 KB)

Format for submitting modified Specific Aims, Project Summary/Abstract, and Public Health Relevance Statements to ICs when requested by NIH staff.

05/01/2009

MS Word  (27 KB)?PDF  (20 KB)

SBIR Funding Agreement Certification

07/25/2013

MS Word (38 KB)?PDF (674 KB)

STTR Funding Agreement Certification

07/25/2013

MS Word (38 KB)?PDF (674 KB)

Data Tables

Data tables for use with Institutional Research Training grant applications using the SF424 (R&R).

Blank Data Tables and Instructions and Sample Data Tables files are available for each of the situations listed below. The Instructions and Sample Data Tables file includes example data, and detailed instructions and rationale statements for each table. These are designed to print best in landscape mode. The Blank Data Tables file provides fillable format pages.

Introduction

Date Posted

File Link/Format/Size

Introduction to Data Tables – Read this First!

11/24/2010

MS Word (78 KB)

PDF (56 KB)

Data Tables

Date Posted

Blank Data Tables

File Link/Format/Size

Instructions and Sample Data Tables

File Link/Format/Size

All Tables

11/24/2010

MS Word (285 KB)

MS Word (414 KB)

PDF (349 KB)

New Application – Predoctoral Training, Only

Submit the tables indicated: 1New, 2, 3, 4, 5A, 6ANew, 7A, 8A, 9ANew, 10 (optional)

11/24/2010

MS Word (138 KB)

MS Word (177 KB)

PDF (165 KB)

Renewal/Revision Application – Predoctoral Training, Only

Submit the tables indicated: 1R/R, 2, 3, 4, 5A, 6AR/R, 7A, 8A, 9AR/R, 10, 11, 12A

11/24/2010

MS Word (175 KB)

MS Word (222 KB)

PDF (200 KB)

New Application – Postdoctoral Training, Only

Submit the tables indicated: 1New, 2, 3, 4, 5B, 6BNew, 7B, 8B, 9BNew, 10 (optional)

11/24/2010

MS Word (140 KB)

MS Word (187 KB)

PDF (171 KB)

Renewal/Revision Application – Postdoctoral Training, Only

Submit the tables indicated: 1R/R, 2, 3, 4, 5B, 6BR/R, 7B, 8B, 9BR/R, 10, 11, 12B

11/24/2010

MS Word (171 KB)

MS Word (225 KB)

PDF (199 KB)

New Application – Mixed Pre- and Postdoctoral Training

Submit the tables indicated: 1New, 2, 3, 4, 5AB, 6ABNew, 7AB, 8AB, 9ABNew, 10 (optional)

11/24/2010

MS Word (202 KB)

MS Word (277 KB)

PDF (248 KB)

Renewal/Revision Application – Mixed Pre- and Postdoctoral Training

Submit the tables indicated: 1R/R, 2, 3, 4, 5AB, 6ABR/R, 7AB, 8AB, 9ABR/R, 10, 11, 12AB

11/24/2010

MS Word (246 KB)

MS Word (334 KB)

PDF (295 KB)

Individual Blank Data Tables

Date Posted

File Link/Format/Size

Data Table 1 (New)

11/24/2010

MS Word (40 KB)

Data Table 1 (Renewal/Revision)

11/24/2010

MS Word (40 KB)

Data Table 2

11/24/2010

MS Word (40 KB)

Data Table 3

11/24/2010

MS Word (41 KB)

Data Table 4

11/24/2010

MS Word (43 KB)

Data Table 5A

11/24/2010

MS Word (40 KB)

Data Table 5B

11/24/2010

MS Word (44 KB)

Data Table 6A (Predoc – New)

11/24/2010

MS Word (41 KB)

Data Table 6B (Postdoc – New)

11/24/2010

MS Word (41 KB)

Data Table 6A (Predoc – R/R)

11/24/2010

MS Word (41 KB)

Data Table 6B (Postdoc – R/R)

11/24/2010

MS Word (41 KB)

Data Table 7A

11/24/2010

MS Word (56 KB)

Data Table 7B

11/24/2010

MS Word (55 KB)

Data Table 8A

11/24/2010

MS Word (48 KB)

Data Table 8B

11/24/2010

MS Word (45 KB)

Data Table 9A (Predoc – New)

11/24/2010

MS Word (40 KB)

Data Table 9B (Postdoc – New)

11/24/2010

MS Word (42 KB)

Data Table 9A (Predoc – R/R)

11/24/2010

MS Word (40 KB)

Data Table 9B (Postdoc – R/R)

11/24/2010

MS Word (42 KB)

Data Table 10

11/24/2010

MS Word (43 KB)

Data Table 11

11/24/2010

MS Word (55 KB)

Data Table 12A

11/24/2010

MS Word (45 KB)

Data Table 12B

11/24/2010

MS Word (41 KB)

Other Information:

Date Posted

File Link/Format/Size

eRA Assembly of the SF424 (R&R) Application (Adobe Forms Version B). Includes assembly of “K” Career Development Award applications.?(An information document describing the system-generated grant image of a SF424 (R&R) application once submitted and received by the agency).

11/13/2009

MS Word  (112 KB)

eRA Assembly of the SF424 (R&R) “T” Institutional Research Training Award Application (Adobe Forms Version B). (An information document describing the system-generated grant image of a SF424 (R&R) application once submitted and received by the agency).

11/13/2009

MS Word  (82 KB)

Person Months Information: FAQs, including a Conversion Calculator

04/21/2006

HTML  (18 KB)

Notable Changes Made to SF424 (R&R) Application Guides

07/25/2013: New Forms Version C General Application Guide: The General Application guide has been changed for the transition to Forms Version C. includes changes to SF424 Research & Related (R&R) form instructions necessitated by the OMB renewal and Grants.gov's subsequent release of updated forms in June 2013. Changes have also been made to various PHS 398 forms and instructions approved by OMB in August 2012 and released by Grants.gov in June 2013. Parts II (Supplemental Instructions for Preparing the Human Subjects Section of the Research Plan) and III (Policies, Assurance, Definitions, and Other Information) of the previous version of the application guide have been moved to a stand-alone document titled, "Supplemental Grant Application Instructions For All Competing Applications and Progress Reports." 

Also, Forms Version B has been updated to reflect new application processes and features such as a change from the requirement to register with the Central Contractor Registry Database (CCR) to the System for Award Management (SAM) and Grants.gov's "Find Grants" feature.

See NIH Guide Notices NOT-OD-13-091 and NOT-OD-13-092.

Application Guides for SBIR/STTR and Fellowship applications will be updated soon.

Archive:  See Notable Changes Archive