Scientific Theory or Model

A scientific theory is a synthesis of well-tested and verified hypotheses about some aspect of he world around us. When a scientific hypothesis has been confirmed repeated by experiment, it may become known as a scientific law or scientific principle. A scientific fact may be defined as an agreement by competent observers of a series of observations of the same phenomena. From time to time scientific facts are revised by additional data about the world around us. Scientists often employ a model in order to understand a particular set of phenomena. A model is a mental image of the phenomena using terms (or images) with which we are familar. For example, in the planetary model of the atom scientists visualize the atom as a nucleus with electrons orbiting around it in a manner similar to the way that planets revolve around the Sun. While this model is useul in understanding the atom, it is an over-simplified description of a real atom and does not describe/predict all of its attributes.

Here are five criteria that are generally used when comparing theories and a new theory statisfying these will then replace a previously accepted theory.

I. The previously accepted theory gave an acceptable explanation of something, the new theory must give the same results.
II. New theory explains something that the PAT either got wrong or, more commonly, did not apply.
III. Makes a prediction that is later verified.
IV. Elegance - Aesthetic quality - simple, powerful includes universal symmetries. That is simple, easy-to-remember or apply formulation, experssed as some symmetry of nature, be powerful enough to used in many applications
V. Provide a deeper insight or link to another branch of knowledge

Steps for Sample design

(i) Universe: While preparing a sample design, it is foremost required to define the set of objects to be studied.

Technically, it is also known as the Universe, which can be finite or infinite. In case of a finite universe, the number of items is limited. Whereas, in an infinite universe the number of items is limitless.

(ii) Sampling unit: It is necessary to decide a sampling unit before selecting a sample. It can be a geographical one (state, district, village, etc.), a construction unit (house, flat, etc.), a social unit (family, club, school, etc.), or an individual.

(iii) Source list: In other words, it is called the ‘sampling frame’ from which the sample is drawn. It comprises the names of all items of a universe (finite universe only). If source list/sampling frame is unavailable, the researcher has to prepare it by himself.

(iv) Sample size: This is the number of items, selected from the universe, constituting a sample. The sample size should not be too large or too small, but optimum. In other words, an optimum sample accomplishes the requirements of efficiency, representativeness, reliability and flexibility.

(v) Parameters of interest: While determining a sample design, it is required to consider the question of the specific population parameters of interest. For example, we may like to estimate the proportion of persons with some specific attributes in the population, or we may also like to know some average or other measure concerning the population.

(vi) Budgetary constraint: Practically, cost considerations have a major impact upon the decisions concerning not only the sample size but also the sample type. In fact, this can even lead to the use of a non-probability sample.

(vii) Sampling procedure: The researcher, at last, decides the techniques to be used in selecting the items for the sample. In fact, this technique/procedure stands for the sample design itself. Apparently, such a design should be selected, which for a provided sample size and cost, has a smaller sampling error.

theorotocal framework

Definition
Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge, within the limits of the critical bounding assumptions. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework introduces and describes the theory which explains why the research problem under study exists.

Importance of Theory
A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your  research paper and that will relate it to the broader fields of knowledge in the class you are taking.
The theoretical framework is not something that is found readily available in the literature. You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.
The theoretical framework strengthens the study in the following ways.
An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.
Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.
By virtue of its application nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges of a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

What is good research_

What is good research?

-All research is different but the following factors are common to all good pieces of research involving social care service users, their families and carers and staff working in this area:
There is a clear statement of research aims, which defines the research question.

-There is an information sheet for participants, which sets out clearly what the research is about, what it will involve and consent is obtained in writing on a consent form prior to research beginning.

-The methodology is appropriate to the research question. So, if the research is into people’s perceptions, a more qualitative, unstructured interview may be appropriate.

-If the research aims to identify the scale of a problem or need, a more quantitative, randomised, statistical sample survey may be more appropriate. Good research can often use a combination of methodologies, which complement one another.

-The research should be carried out in an unbiased fashion. As far as possible the researcher should not influence the results of the research in any way. If this is likely, it needs to be addressed explicitly and systematically.
From the beginning, the research should have appropriate and sufficient resources in terms of people, time, transport, money etc. allocated to it.

What is the difference between internal and external validity in research_

AnswerInternal validity has to do with the accuracy of the results. Results could be inaccurate if samples are not selected randomly. External validity has to do with the generalizability of the findings to the population. If the sample selected is only Hispanics under the age of 25, then it would be hard to generalize the results to the entire US population.

business research

In general, business research refers to any type of researching done when starting or running any kind of business. For example, starting any type of business requires research into the target customer and the competition to create a business plan. Conducting business market research in existing businesses is helpful in keeping in touch with consumer demand. Small business research begins with researching an idea and a name and continues with research based on customer demand and other businesses offering similar products or services. All business research is done to learn information that could make the company more successful.

Business research methods vary depending on the size of the company and the type of information needed. For instance, customer research may involve finding out both a customer’s feelings about and experiences using a product or service. The methods used to gauge customer satisfaction may be questionnaires, interviews or seminars. Researching public data can provide businesses with statistics on financial and educational information in regards to customer demographics and product usage, such as the hours of television viewed per week by people in a certain geographic area. Business research used for advertising purposes is common because marketing dollars must be carefully spent to increase sales and brand recognition from ads.

Other than business market research and advertising research, researching is done to provide information for investors. Business people aren't likely to invest in a company or organization without adequate research and statistics to show them that their investment is likely to pay off. Large or small business research can also help a company analyze its strengths and weaknesses by learning what customers are looking for in terms of products or services the business is offering. Then a company can use the business research information to adjust itself to better serve customers, gain over the competition and have a better chance of staying in business.

Most industries have trade journals that include research reports and statistics that relate to a certain type of business. International information is especially important to businesses that have ties with other countries and need to understand more about the cultures and demographics of other nations. For example, International Business Research is a publication of the Canadian Center of Science and Education and includes business essays and academic editorials from businesspeople from different parts of the world such as Australia, India and Malaysia.

characteristic of good measurement

Procedures for measuring attributes can be judged on a variety of merits. These include practical well as technical issues. All measurement procedures, whether qualitative or quantitative, have strengths and weaknesses—no one procedure is perfect for every task. In order to improve a study it is frequently prudent for an investigator to use multiple measurement tools and triangulate the results.

Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects. In short, it is the repeatability of your measurement. A measure is considered reliable if a person's score on the same test given twice is similar. It is important to remember that reliability is not measured, it is estimated. A good instrument will produce consistent scores. An instrument’s reliability is estimated using a correlation coefficient of one type or another.

Validity:  Validity is the extent to which a test measures what it claims to measure. It is vital for a test to be valid in order for the results to be accurately applied and interpreted. Validity isn’t determined by a single statistic, but by a body of research that demonstrates the relationship between the test and the behavior it is intended to measure. There are three types of validity: It is the strength of our conclusions, inferences or propositions. More formally, Cook and Campbell (1979) define it as the "best available approximation to the truth or falsity of a given inference, proposition or conclusion." In short, were we right? Let's look at a simple example. Say we are studying the effect of strict attendance policies on class participation. In our case, we saw that class participation did increase after the policy was established. Each type of validity would highlight a different aspect of the relationship between our treatment (strict attendance policy) and our observed outcome (increased class participation).

Practicability: It should be feasible & usable. Quality of being usable in context to the objective to be achieved.
USABILITY(practicality) ease in administration, scoring, interpretation and application, low cost, proper mechanical make – up

Measurability: It should measure the objective to be achieved.

Define Sampling Plan

A sampling plan is a detailed outline of which measurements will be taken at what times, on which material, in what manner, and by whom. Sampling plans should be designed in such a way that the resulting data will contain a representative sample of the parameters of interest and allow for all questions, as stated in the goals, to be answered.

Steps in the sampling plan   

The steps involved in developing a sampling plan are:
-identify the parameters to be measured, the range of possible values, and the required resolution
-design a sampling scheme that details how and when samples will be taken
-select sample sizes
-design data storage formats
-assign roles and responsibilities

Verify and execute   

Once the sampling plan has been developed, it can be verified and then passed on to the responsible parties for execution.

descriptive research

One of the goals of science is description (other goals include prediction and explanation).  Descriptive research methods are pretty much as they sound — they describe situations. They do not make accurate predictions, and they do not determine cause and effect.

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.

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.  

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.

Internal and external validity

When we conduct experiments, our goal is to demonstrate cause and effect relationships between the independent and dependent variables. We often try to do it in a way that enables us to make statements about people at large. How well we can do this is referred to as study�s generalisability. A study that readily allows its findings to generalise to the population at large has high external validity. To the degree that we are successful in eliminating confounding variables within the study itself is referred to as internal validity. External and internal validity are not all-or-none, black-and-white, present-or-absent dimensions of an experimental design. Validity varies along a continuum from low to high.

One major source of confounding arises from non-random patterns in the membership of participants in the study, or within groups in the study. This can affect internal and external validity in a variety of ways, none of which are necessarily predictable. It is often only after doing a great deal of work that we discover that some glitch in our procedures or some oversight has rendered our results uninterpretable.

is a scatter plot a quantitative or qualitative way to display data_

Scatter plots are a quantitative way to display data because they involve observations that include numbers in them. Qualitative data involves observations that do not include numbers in them. Scatter plots are similar to line graphs in that they both map quantitative data but the points on scatter plots are not connected with a line but instead express a general trend.

linear regression

Linear Regression

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

Sampling & types

What is sampling?

A shortcut method for investigating a whole population
Data is gathered on a small part of the whole parent population or sampling frame, and used to inform what the whole picture is like

Why sample?

In reality there is simply not enough; time, energy, money, labour/man power, equipment, access to suitable sites to measure every single item or site within the parent population or whole sampling frame.

Therefore an appropriate sampling strategy is adopted to obtain a representative, and statistically valid sample of the whole.

Different Types of Sample:

1.Simple Random Sample

Obtaining a genuine random sample is difficult. We usually use Random Number Tables, and use the following procedure;

Number the population from 0 to n
Pick a random place I the number table
Work in a random direction
Organise numbers into the required number of digits (e.g. if the size of the population is 80, use 2 digits)
Reject any numbers not applicable (in our example, numbers between 80 and 99)
Continue until the required number of samples has been collected
[ If the sample is "without replacement", discard any repetitions of any number]

Advantages:

The sample will be free from Bias (i.e. it's random!)

Disadvantages:

Difficult to obtain

Due to its very randomness, "freak" results can sometimes be obtained that are not representative of the population. In addition, these freak results may be difficult to spot. Increasing the sample size is the best way to eradicate this problem.

2.Systematic Sample

With this method, items are chosen from the population according to a fixed rule, e.g. every 10th house along a street. This method should yield a more representative sample than the random sample (especially if the sample size is small). It seeks to eliminate sources of bias, e.g. an inspector checking sweets on a conveyor belt might unconsciously favour red sweets. However, a systematic method can also introduce bias, e.g. the period chosen might coincide with the period of faulty machine, thus yielding an unrepresentative number of faulty sweets.


Advantages:

Can eliminate other sources of bias

Disadvantages:

Can introduce bias where the pattern used for the samples coincides with a pattern in the population.

3.Stratified Sampling

The population is broken down into categories, and a random sample is taken of each category. The proportions of the sample sizes are the same as the proportion of each category to the whole.

Advantages:

Yields more accurate results than simple random sampling

Can show different tendencies within each category (e.g. men and women)

Disadvantages:

Nothing major, hence it's used a lot

4.Quota Sampling

As with stratified samples, the population is broken down into different categories. However, the size of the sample of each category does not reflect the population as a whole. This can be used where an unrepresentative sample is desirable (e.g. you might want to interview more children than adults for a survey on computer games), or where it would be too difficult to undertake a stratified sample.



Advantages:

Simpler to undertake than a stratified sample

Sometimes a deliberately biased sample is desirable

Disadvantages:

Not a genuine random sample

Likely to yield a biased result

5.Cluster Sampling

Used when populations can be broken down into many different categories, or clusters (e.g. church parishes). Rather than taking a sample from each cluster, a random selection of clusters is chosen to represent the whole. Within each cluster, a random sample is taken.


Advantages:

Less expensive and time consuming than a fully random sample

Can show "regional" variations

Disadvantages:

Not a genuine random sample

Likely to yield a biased result (especially if only a few clusters are sampled)

sapmle design size

The sample size, in this case, refers to the number of children to be included in the survey.
Step 1: Base Sample-size calculation
The appropriate sample size for a population-based survey is determined largely by three factors: (i) the estimated prevalence of the variable of interest – chronic malnutrition in this instance, (ii) the desired level of confidence and (iii) the acceptable margin of error.
For a survey design based on a simple random sample, the sample size required can be calculated according to the following formula.
Formula:
n=    t² x p(1-p)
    
m²
Description:
n = required sample size
t = confidence level at 95% (standard value of 1.96)
p = estimated prevalence of malnutrition in the project area
m = margin of error at 5% (standard value of 0.05)
Example
In the Al Haouz project in Morocco, it has been estimated that roughly 30% (0.3) of the children in the project area suffer from chronic malnutrition. This figure has been taken from national statistics on malnutrition in rural areas. Use of the standard values listed above provides the following calculation.
Calculation:
n=    1.96² x .3(1-.3)
    
.05²
n =    3.8416 x .21
    
.0025
n =    .8068
     .0025
n =    322.72 ˜ 323
Step 2: Design Effect
The anthropometric survey is designed as a cluster sample (a representative selection of villages), not a simple random sample. To correct for the difference in design, the sample size is multiplied by the design effect (D).
The design effect is generally assumed to be 2 for nutrition surveys using cluster-sampling methodology.
Example
n x D = 323 x 2 = 646
Step 3: Contingency
The sample is further increased by 5% to account for contingencies such as non-response or recording error.
Example
n + 5% = 646 x 1.05 = 678.3 ˜ 678
Step 4: Distribution of Observations
Finally, the calculation result is rounded up to the closest number that matches well with the number of clusters (30 villages) to be surveyed.
Thirty is the standard number of clusters established by the WHO Expanded Programme of Immunization (EPI Cluster Surveys). There is no statistically necessary reason to maintain exactly 30 clusters, and the number can be adjusted if there is a compelling motive for doing so.
Example
Final Sample Size: N = 690 children
The final sample size (N) is then divided by the number of clusters (30) to determine the number of observations per cluster.
Example
N ÷ no. clusters = 690 ÷ 30 = 23 children per village
General Rule: Standardized Sample Sizes for Nutrition Surveys
The following table provides the recommended sample size for various estimated levels of malnutrition, incorporating standard values for confidence level and margin of error. The final sample size includes the contingency percentage and is rounded to match well with a 30-cluster survey.

P
(est. % malnutrition)
n
(base sample size)
n x D
(n x design effect)
N
(final sample size)
0.2 (20%)
246
492
540
0.25 (25%)
288
576
600
0.3 (30%)
323
646
690
0.35 (35%)
350
700
720
0.4 (40%)
369
738
750
0.45 (45%)
380
760
780
0.5 (50%)
384
768
810
Note:
If it is not possible to find an estimated prevalence of malnutrition for the project area, the recommended action is to set the sample size at 810.

scale of measuremnt

Measurement scales are used to categorize and/or quantify variables. This lesson describes the four scales of measurement that are commonly used in statistical analysis: nominal, ordinal, interval, and ratio scales.

Properties of Measurement Scales:

Each scale of measurement satisfies one or more of the following properties of measurement.

1.Identity. Each value on the measurement scale has a unique meaning.

2.Magnitude. Values on the measurement scale have an ordered relationship to one another. That is, some values are larger and some are smaller.

3.Equal intervals. Scale units along the scale are equal to one another. This means, for example, that the difference between 1 and 2 would be equal to the difference between 19 and 20.

4.A minimum value of zero. The scale has a true zero point, below which no values exist.


Nominal Scale of Measurement
The nominal scale of measurement only satisfies the identity property of measurement. Values assigned to variables represent a descriptive category, but have no inherent numerical value with respect to magnitude.

Gender is an example of a variable that is measured on a nominal scale. Individuals may be classified as "male" or "female", but neither value represents more or less "gender" than the other. Religion and political affiliation are other examples of variables that are normally measured on a nominal scale.

Ordinal Scale of Measurement
The ordinal scale has the property of both identity and magnitude. Each value on the ordinal scale has a unique meaning, and it has an ordered relationship to every other value on the scale.

An example of an ordinal scale in action would be the results of a horse race, reported as "win", "place", and "show". We know the rank order in which horses finished the race. The horse that won finished ahead of the horse that placed, and the horse that placed finished ahead of the horse that showed. However, we cannot tell from this ordinal scale whether it was a close race or whether the winning horse won by a mile.

Interval Scale of Measurement
The interval scale of measurement has the properties of identity, magnitude, and equal intervals.

A perfect example of an interval scale is the Fahrenheit scale to measure temperature. The scale is made up of equal temperature units, so that the difference between 40 and 50 degrees Fahrenheit is equal to the difference between 50 and 60 degrees Fahrenheit.

With an interval scale, you know not only whether different values are bigger or smaller, you also know how much bigger or smaller they are. For example, suppose it is 60 degrees Fahrenheit on Monday and 70 degrees on Tuesday. You know not only that it was hotter on Tuesday, you also know that it was 10 degrees hotter.

Ratio Scale of Measurement
The ratio scale of measurement satisfies all four of the properties of measurement: identity, magnitude, equal intervals, and a minimum value of zero.

The weight of an object would be an example of a ratio scale. Each value on the weight scale has a unique meaning, weights can be rank ordered, units along the weight scale are equal to one another, and the scale has a minimum value of zero.

Weight scales have a minimum value of zero because objects at rest can be weightless, but they cannot have negative weight.

Nominal Scale Examples

diagnostic categories

sex of the participant

classification based on discrete characteristics (e.g., hair color)

group affiliation (e.g., Republican, Democrate, Boy Scout, etc.)

the town people live in

a person's name

an arbitrary identification, including identification numbers that are arbitrary

menu items selected

any yes/no distinctions

most forms of classification (species of animals or type of tree)

location of damage in the brain

Ordinal Scale Examples

any rank ordering

class ranks

social class categories

order of finish in a race

Interval Scale Examples

scores on scales that are standardized (i.e., with an arbitrary mean and standard deviation, usually designed to always give a positive score)

scores on scales that are known to not have a true zero (e.g., most temperature scales except for the Kelvin Scale)

scores on measures where it is not clear that zero means none of the trait (e.g., a math test)

scores on most personality scales based on counting the number of endorsed items

Ratio Scale Examples

time to complete a task

number of responses given in a specified time period

weight of an object

size of an object

number of objects detected

number of errors made in a specified time period

proportion of responses in a specified category

scatterplot

A scatterplot is a useful summary of a set of bivariate data (two variables), usually drawn before working out a linear correlation coefficient or fitting a regression line. It gives a good visual picture of the relationship between the two variables, and aids the interpretation of the correlation coefficient or regression model.
Each unit contributes one point to the scatterplot, on which points are plotted but not joined. The resulting pattern indicates the type and strength of the relationship between the two variables.

Scientific Theory or Model

A scientific theory is a synthesis of well-tested and verified hypotheses about some aspect of he world around us. When a scientific hypothesis has been confirmed repeated by experiment, it may become known as a scientific law or scientific principle. A scientific fact may be defined as an agreement by competent observers of a series of observations of the same phenomena. From time to time scientific facts are revised by additional data about the world around us. Scientists often employ a model in order to understand a particular set of phenomena. A model is a mental image of the phenomena using terms (or images) with which we are familar. For example, in the planetary model of the atom scientists visualize the atom as a nucleus with electrons orbiting around it in a manner similar to the way that planets revolve around the Sun. While this model is useul in understanding the atom, it is an over-simplified description of a real atom and does not describe/predict all of its attributes.

Here are five criteria that are generally used when comparing theories and a new theory statisfying these will then replace a previously accepted theory.

I. The previously accepted theory gave an acceptable explanation of something, the new theory must give the same results.
II. New theory explains something that the PAT either got wrong or, more commonly, did not apply.
III. Makes a prediction that is later verified.
IV. Elegance - Aesthetic quality - simple, powerful includes universal symmetries. That is simple, easy-to-remember or apply formulation, experssed as some symmetry of nature, be powerful enough to used in many applications
V. Provide a deeper insight or link to another branch of knowledge