Binary Logistic Regression

• Logistic regression is an extension of simple linear regression.
• Where the dependent variable is dichotomous or binary in nature, we cannot use simple linear regression. Logistic regression is the statistical technique used to predict the relationship between predictors (our independent variables) and a predicted variable (the dependent variable) where the dependent variable is binary (e.g., sex [male vs. female], response [yes vs. no], score [high vs. low], etc…).
• There must be two or more independent variables, or predictors, for a logistic regression.  The IVs, or predictors, can be continuous (interval/ratio) or categorical (ordinal/nominal).
• All predictor variables are tested in one block to assess their predictive ability while controlling for the effects of other predictors in the model.

·         Assumptions for a Logistic regression:

1.      adequate sample size (too few participants for too many predictors is bad!);

2.      absence of multicollinearity (multicollinearity = high intercorrelations among the predictors);

3.      no outliers

• The statistic -2LogL (minus 2 times the log of the likelihood) is a badness-of-fit indicator, that is, large numbers mean poor fit of the model to the data.
• When taken from large samples, the difference between two values of -2LogL is distributed as chi-square:

Where likelihood_R is for a restricted, or smaller, model and likelihood_F is for a full, or larger, model.

• Likelihood_F has all the parameters of interest.
• Likelihood_R is nested in the larger model. (nested = all terms occur in the larger model; necessary condition for model comparison tests).
• A nested model cannot have as a single IV, some other categorical or continuous variable not contained in the full model. If it does, then it is no longer nested, and we cannot compare the two values of -2LogL to get a chi-square value.
• The chi-square is used to statistically test whether including a variable reduces badness-of-fit measure.
• If chi-square is significant, the variable is considered to be a significant predictor in the equation.

Creating and Validating an Instrument

To determine if an appropriate instrument is available, a researcher can search literature and commercially available databases to find something suitable to the study.  If it is determined that there are no instruments available that measure the variables in a study, there are four rigorous phases for developing an instrument that accurately measures the variables of interest (Creswell, 2005).  Those four phases are: planning, construction, quantitative evaluation, and validation.  Each phase consists of several steps that must be taken to fully satisfy the requirements for fulfilling a phase. 

            The first phase is planning and the first step of planning includes identifying the purpose of the test and the target group.  In this step, the researcher should identify the purpose of the test, specify the content area to be studied, and identify the target group.  The second step of phase one is to, again, review the literature to be certain no instruments already exist for the evaluation of the variables of interest.  Several areas to look for existing instruments include the ERIC website (www.eric.ed.gov), Mental Measurements Yearbook (Impara & Plake, 1999), and Tests in Print (Murphy, Impara, & Plake, 1999).  Once the researcher is certain no other instruments exist, the researcher should review the literature to determine the operational definitions of the constructs that are to be measured.  This can be an arduous task because operationalizing a variable does not automatically indicate good measurement and therefore the researcher must review multiple literatures to determine an accurate and meaningful construct.  From this information, the researcher should develop open ended questions to present to a sample that is representative of the target group.  The open ended questions aid the researcher in determining areas of concern around the constructs to be measured.  The responses to the open ended questions and the review of the literature should be used in unison to create and modify accurate measures of the constructs.

            The second phase is construction and it begins with identifying the objectives of the instrument and developing a table of specifications.  Those specifications should narrow the purpose and identify the content areas.  In the specification process, each variable should be associated with a concept and an overarching theme (Ford, http://www.blaiseusers.org/2007/papers/Z1%20-%20Survey%20Specifications%20Mgmt%20at%20Stats%20Canada.pdf).  Once the table of specification is completed, the researcher can write the items in the instrument.  The researcher must determine the format to be used, ie. Likert scale, multiple choice, etc.  The format of the questions should be determined by the type of data that needs to be collected.  Depending on the financial resources of the research project, experts within the field may be hired to write the items.  Once the items are written, they need to be reviewed for clarity, formatting, acceptable response options, and wording.  After several reviews of the questions, they should be presented to peers and colleagues in the format the instrument is to be administered.  The peers and colleagues should match the items with the specification table and if there are not exact matches, revisions must be made.  An instrument is content valid when the items adequately reflect the process and content dimensions of the objectives of the instrument (Benson & Clark, 1982).  Again, the instrument should be distributed to a sample that is representative of the target group.  This time the group should take the survey and critique the quality of the individual items and overall instrument. 

            Phase three is quantitative evaluation and includes administration of a pilot study to a representative sample.  It may be helpful to ask the participants for feedback to allow for further refinement of the instrument.  The pilot study provides quantitative data that the researcher can test for internal consistency by conducting Cronbach’s alphas.  The reliability coefficient can range from 0.00 to 1.00, with values of 0.70 or higher indicating acceptable reliability (George and Mallery, 2003).  If the instrument is going to be used to predict future behavior, the instrument needs to be administered to the same sample at two different time periods and the responses will need to be correlated to determine if there is concurrent validity.  These measurements can be examined to aid the researcher in making informed decisions about revisions to the instrument.    

            Phase four is validation.  In this phase the researcher should conduct a quantitative pilot study and analyze the data.  It may be helpful to ask the participants for feedback to allow for further refinement of the instrument.  The pilot study provides quantitative data that the researcher can test for internal consistency by conducting Cronbach’s alphas.  To establish validity, the researcher must determine which concept of validity is important.  The three types of validity include content, criterion-related, and construct.  Content validity is the extent to which the questions on a survey are representative of the questions that could be asked to assess a particular construct.  To examine content validity, the researcher should consult two to three experts.  Criterion-referenced validity is used when the researcher wants to determine if the scores from an instrument are a good predictor of an expected outcome.  In order to assess this type of validity, the researcher must be able to define the expected outcome.  A correlation coefficient of a .60 or above will indicate a significant, positive relationship (Creswell, 2005).  Construct validity is established by determining if the scores recorded by an instrument are meaningful, significant, useful, and have a purpose.  In order to determine if construct validity has been achieved, the scores need to be assessed statistically and practically.  This can be done by comparing the relationship of a question from the scale to the overall scale, testing a theory to determine if the outcome supports the theory, and by correlating the scores with other similar or dissimilar variables.  The use of similar instruments is referred to as convergent validity and the use of dissimilar instruments is divergent validity. 

References

Creswell, J. W. (2005). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (2^nd ed.). Upper Saddle River, NJ: .Pearson Education, Inc.  

George, D. & Mallery, P. (2003). SPSS for Windows step by step: a simple guide and reference, 11.0 update (4^th ed.). Boston, MA: Allyn and Bacon. 

Murphy, L. L., Impara, J. C., & Plake, B. S. (Eds.). (1999)

Dissertation Statistics Help

Dissertation statistics help is a click away! Free online resources, video tutorials, free dissertation templates, SPSS tutoring, research design help, statistics analyses, dissertation newsletters, and much more. Another semester is coming to an end, and you’re not quite there yet. Most students can really help with the proposal (especially chapter 3) and the results chapter 4. Let’s talk about these both.

Dissertation Proposal
For the proposal, the main sticking points are the research questions, data analysis plan, sample size justification, and research design. For the research design, I’ve found http://www.socialresearchmethods.net/kb/ to be a great free resource, and if you don’t have Creswell’s book, Research design: qualitative, quantitative, and mixed methods approaches, get it. When it comes to dissertation statistics help, students don’t realize the following sequence:

Clear research questions - data analysis plan - sample size justification (or power analysis)

Research questions need to be written in statistical language. For some current news examples, (1) is there a relationship between party affiliation (republican vs. democrat) and the government shutdown (yes vs. no)? (2) Does the use of Twitter predict anger in Libya, or (3) are there differences on gold prices by debt fears? These words relationship, predict, and differences infer that you want a data analysis plan with correlations/chi-square, regression analysis, and ANOVA. The data plan needs also to talk about the assumptions of these analyses, and justification why these are the appropriate analysis. Based on the statistical analysis, the sample size can be determined. Each analysis has its own sample size justification. A great free sample size calculator is G-power or if you want a quick write-up you can go to http://www.statisticssolutions.com/products-services/login/standard-membership/sample-sizepower-analysis-calculator-with-write-up where you pick the analysis and the justification is written for you (it’s cheap, quick, and you’re not spending a month figuring it out or paying someone $1000 for it).

Chapter 4: Getting the Dissertation Statistics Help You Need
Statistical help for a dissertation means graduate students get the help to selecting the correct statistical tests and assumptions, conducting the right analyses, the right interpretation, and the presenting the results in the right (usually APA 6th edition) format. Statistics Solutions (the company I have operated for 18 years) have the right research design experience (our Ph.D.’s are in Clinical Psychology and Statistics) expertise in SPSS (we even sell SPSS for about $100), and formatting and teaching experience to assist you. The company has online video tutorials that show you how to conduct, interpret, and report the analyses. We have APA editors or you can visit sites like Purdue University’s great website for APA formatting. We also consult with your qualitative analysis as well as your quantitative analyses.

Final thoughts on Dissertation Statistics Help
Dissertation statistics can be tricky (especially time-series, cluster analysis, SEM and CFA’s). As with anything you read or purchase, check the company out. For example, we have Ph.D.’s (I have my Ph.D. from Miami U in Ohio) and been through the rigorous process, we do our work in-house, we have a references of previous students, and registered with the BBB (an A+ rating) .

I hope this post was helpful and that you pass it along to colleagues that might find it interesting. If you’d like more information about our services you can contact us at http://www.statisticssolutions.com/contact.

Happy Learning!

Dr. James Lani, Ph.D.
CEO, Statistics Solutions

Level of measurement

The level of measurement has been classified into basically four categories. It is important for the researcher to understand that the level of measurement is determined partly by arithmetic operations and statistical operations.

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Sorted in an ascending order of precision, the four different levels of measurement are the nominal, the ordinal, the interval and the ratio scale.

The first among the four levels of measurement is the nominal level. This level of measurement basically refers to those cases in which the numbers are used to organize the data. The use of words and letters is also done in this level of measurement. Suppose there is data that has two categories of students, namely weak students and strong students. Using this level of measurement, the researcher can easily classify the weak category of students with the letter ‘W,’ and the strong category of students can be denoted with an ‘S.’ This assigning of letters to distinguish the classification is the nominal level of measurement.

The second type of level of measurement is the ordinal level. This level of measurement generally involves those measurements that signify some kind of ordered associations between the number items. If four teams participate in a match, the team that has beaten all three teams would win the match and would be assigned the first rank. Then, the team performing right below the first team would be assigned the second rank, and so on. Thus, this level of measurement also assigns the reasons behind the rank assigned to any particular item. So, this level of measurement indicates the appropriate ordering of the measurements. The researcher should note that in this type of level of measurement, the change or the share between any two types of rankings does not remain the same along the scale.

The next type of level of measurement is that of the interval level of measurement. In this level of measurement, the researcher categorizes and assigns orders to the measurements and also reveals that the distances between each interval on the scale is equivalent along the scale from the low interval to the high interval. One such example is the measurement of anxiety of a student that is in between the score of 10 and 11 is same as if the student is in between the score of 40 and 41. Another appropriate example for this type of level of measurement is that while measuring the temperature in centigrade, the distance between 940C and 960C is similar to the distance between 1000C and 1020C.

The last level of measurement is the ratio level of measurement. In this type of level of measurement, the researcher can observe a value of actual zero as well. This kind of phenomena is quite unlike the other types of level of measurement. However, the researcher should note that this level of measurement has the same property as that of the interval level of measurement. The divisions between the points on the scale have the equivalent distance between them, and the rankings assigned to the items are according to their size in this level of measurement.

The researcher should note that among these levels of measurements, the nominal level is simply used to classify the data, whereas the levels of measurement described by the interval and the ratio are much more exact.

Dissertation Statistics Help

If you are a doctoral student who has started your dissertation, you know that the road ahead of you is lengthy and difficult. This is because the dissertation is lengthy and difficult. In fact, chances are that you have already been overwhelmed by the mere thought of working on and finishing your dissertation. You are not alone, however, as most students who must write a dissertation have had the exact same feelings of dread, anxiety and panic.

There is, of course, a way to deal with this dread, anxiety and panic and this is to acquire dissertation statistics help. Dissertation statistics help is a service provided by dissertation consultants and dissertation statistics help can make every single step of your dissertation easier and more manageable. Dissertation statistics help offers a student individual and personalized help as dissertation statistics help ensures that the student finishes with accuracy, timeliness and success.

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Dissertation statistics help is becoming more and more popular as more and more people attempt to receive their doctoral degrees. Dissertation statistics help has also become popular because it has become apparent that many students are not trained in the difficult field of statistics. The student is trained in his or her area of study, and yet, when it comes to statistics, the student has not received the proper training. Dissertation statistics help steps into this void and dissertation statistics help actually instructs the students in terms of statistics. Dissertation statistics help provides this one-on-one instruction to all students who seek dissertation statistics help. And this training is quite possibly the most valuable and most important service that dissertation statistics help provides. This is true because the student will eventually have to defend his or her dissertation and if the student does not understand the statistics part of his or her dissertation, they will not pass the oral defense part of the dissertation. Thus, dissertation statistics help will provide all of the training necessary to the student so that he or she can pass the oral defense of his or her dissertation.

Before the student gets to the oral defense of his or her dissertation, the student must complete the dissertation. This is made easy and understandable by dissertation statistics help. Dissertation statistics help will provide valuable insight as to how to do every single step of the statistics that are involved with the student’s dissertation. The first step, of course, is to make sure that the topic is valid and can be studied. Dissertation statistics help will make sure that the student’s topic can indeed be studied statistically, and dissertation statistics help will assist the student if his or her topic is not able to be studied and measured statistically. Dissertation statistics help will then go about the process of the statistics with the student. What that means is that dissertation statistics help will guide the student through every single step of the dissertation statistics. This includes first collecting the data (which can be very lengthy and difficult if it is not done correctly), interpreting the results of the data collected (which again can be lengthy and difficult if not done correctly) and applying those results to the dissertation and thesis. Once all of this is complete, dissertation statistics help will also edit and proofread the entire dissertation, just to make sure that the student will indeed succeed when they turn in their dissertation for approval.

Without question, dissertation statistics help is the absolute best way to expedite the process of writing a dissertation. With dissertation statistics help, the student is guaranteed to succeed as he or she has dissertation statistics help assisting him/her every single step of the way.

Continuous Probability distribution

Continuous probability distribution is that type of distribution that deals with continuous type of data or random variables. The continuous random variables deal with different kinds of continuous probability distribution.

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There are different continuous probability distributions.

A normal distribution is a continuous probability distribution with parameters µ ( called the mean) and s2 (called the variance) that have a range of -8 to +8. Its continuous probability distribution is given by the following:

f(x;µ, s)= (1/ s p) exp(-0.5 (x-µ)2/ s2).

This type of continuous probability distribution plays a crucial role in statistical theory for several reasons. Most of the distributions, like binomial, poisson and hyper geometric distributions are approximated with the help of this continuous probability distribution.

This continuous probability distribution finds a large number of applications in Statistical Quality Control.

This type of continuous probability distribution is used widely in the study of large sample theory where normality is involved. Sample statistics can be best studied with the help of the curves of this type of continuous probability distribution.

The overall theory of significance tests (like t test, F test, etc.) are entirely based upon the fundamental assumption that the parent population belongs to this type of continuous probability distribution.

Even if the variable is not following this type of continuous probability distribution, then it can be transformed into this type of continuous probability distribution.

A gamma distribution is a continuous probability distribution with the parameter ‘d>0’that has a range of 0 to 8. Its continuous probability distribution is given by the following:

f(x)= exp(-x) xd-1/

This type of continuous probability distribution has a property called the additive property. This property states that the sum of the independent variates of this continuous probability distribution is equal to the variate of this continuous probability distribution.

A beta distribution of the first kind is a continuous probability distribution with the parameters µ>0 and v>0 that has the range of 0 to 1. Its continuous probability distribution is given by the following:

f(x)= (1/B(µ,v)) xµ-1 (1-x)v-1

A beta distribution of the second kind is a continuous probability distribution with the parameters µ>0 and v>0 that has the range of 0 to 8. Its continuous probability distribution is given by the following:

f(x)= (1/B(µ,v)) xµ-1 (1+x)v+µ

An exponential distribution is a continuous probability distribution with the parameter ‘c’ >0 that has the range of 0 to 8. Its continuous probability distribution is given by the following:

f(x,c)= c exp(-cx)

A standard laplace or double exponential distribution is a continuous probability distribution with no parameter. The reason that there is no parameter in this type of continuous probability distribution is because this continuous probability distribution is standardized in nature. Thus, this continuous probability distribution does not have any parameters. Its continuous probability distribution is given by the following:

f(x)= 0.5 exp (- )

A weibul distribution is a continuous probability distribution with three parameters c(>0), a(>0) and µ that has the range of µ to 8. Its continuous probability distribution is given by the following:

f(x;c,a,µ) = (c (x-µ/a)c-1)/ a exp (-(x-µ/a)c)

A logistic distribution is a continuous probability distribution with parameter a and ß. This type of continuous probability distribution is used widely as a growth function in population and other demographic studies. This type of continuous probability distribution is considered to be the mixture of the extreme values of the distributions.

A Cauchy distribution is a continuous probability distribution with parameter ‘l’ > 0 and ‘µ.’ This type of continuous probability distribution has the range of -8 to +8. The continuous probability distribution is given by the following:

f(x)= l/p(l2+(x-µ)2)

This type of continuous probability distribution follows the additive property as stated above. The type of continuous probability distribution plays a role in providing counter examples.

Chi-square

Chi square is defined as the square of the standard normal variable. There are certain chi square tests and they are discussed below in a detailed manner.

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A cross tabulation is also a kind of chi square test that is used by the researcher in order to test the statistical significance of the correlation that is observed in the study. The chi square test is used by the researcher to determine the strength of the association in the objects under study.

The researcher should note that the greater the difference between the observed value of the cell frequency and the expected value of the cell frequency, the larger the value of the statistic of the chi square. This means that the difference of the observed value and the expected value in the chi square test is directly proportional to the value of the chi square statistic in the chi square test.

To determine the association or the correlation between the two variables that exist in the chi square test, the probability that is computed for obtaining the value of the chi square must be larger or greater, or must have a higher value than the one obtained, which is computed from the chi square test of cross tabulation.

Another popular chi square test is the goodness of fit test. This goodness of fit in the chi square test helps the researcher to understand whether or not the sample that is collected from some population belongs to some specific distribution. This chi square test is basically applicable in cases where the discrete type of probability distributions is involved, like Poisson distribution, binomial distribution, etc. This chi square test is an alternative to the non parametric type of test, called the Kolmogorov Smirnov goodness of fit test.

The null hypothesis that the researcher assumes in this chi square test is that the drawn data from the population follows the distribution. The definition of the statistic used in the chi square test is the same, which is the sum of the square of the deviation between the observed and the expected frequency that is divided by the expected frequency. An important point related to the validity of this type of chi square test is that the expected number of cell frequencies should be less than five.

Researchers generally assume certain assumptions in the chi square test, and on the basis of those assumptions, only the chi square test is carried out.

The first assumption in the chi square test is that the sampling of the data is collected by the process of random sampling from the population.

A sample size that is sufficiently large is assumed in the chi square test. The chi square test that is conducted on the sample of a smaller size results in the drawing of an inaccurate inference about the data. If the researcher conducts the chi square test on a small sample size, then it may happen that the researcher might end up committing a Type II error.

As in all other significant tests, it is assumed that in the chi square test, the observations are always independent of each other.

The last assumption that is made in the chi square test is that the observations in the sample must acquire the same fundamental distribution.

Descriptive measure

Descriptive measure is basically a type of measure that deals with quantitative data in a mass that shows certain kinds of general characteristics. Descriptive measure is generally of different types for the different types of characteristics of data. This document will detail the different types of descriptive measure.

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A tendency that depicts the absorption around specific values, especially around the center, is called the descriptive measure of central tendency. This descriptive measure depicts the central tendency in data that should satisfy several properties. These properties were discussed by the famous statistician, Professor Yule.

The descriptive measure that shows the central tendency must be strictly defined. The descriptive measure that shows this tendency is generally flexible and simple to calculate and understand. The descriptive measure that exhibits such kinds of tendencies must be based on all the observations. The descriptive measure that has this kind of tendency must be adaptable for any kind of mathematical treatment. The descriptive measure that has such tendencies must not get affected by the extreme values in the observations.

The arithmetic mean is the descriptive measure that depicts the centering of the observations. The descriptive measure is defined as the overall sum of the observations in the data that are divided by the number of the observations in the data. This descriptive measure follows the main conditions of the properties that are explained by Professor Yule. The major limitation of the descriptive measure that shows central tendency is that such a descriptive measure cannot be obtained by inspection. This descriptive measure that shows the central tendency cannot be located graphically. This type of descriptive measure that depicts the central tendency cannot be calculated by the researcher if any particular observation is missing from the data. This descriptive measure, which shows the central tendency, is not applicable for that kind of data that shows qualitative characteristics.

The descriptive measure that shows the central tendency also has a descriptive measure called the weighted mean. This descriptive measure also works in a similar manner to arithmetic mean, except for the fact that this descriptive measure attaches weights to the items under consideration according to their importance in the life of the user. For example, if one wants to obtain the cost of living of a certain group of people, then the arithmetic mean descriptive measure will give importance to all the commodities, while the weighted mean descriptive measure gives more weight to certain commodities.

Median is another type of descriptive measure that depicts the central tendency. This descriptive measure is the only measure that is applicable when the researcher is dealing with qualitative data. This descriptive measure is defined as the value that conducts the partition of the data into two equal parts. The limitation of this kind of descriptive measure is that this measure is not adaptable to the algebraic treatment. Also, this kind of descriptive measure is not at all based on all the observations. If the observations are of even numbers, then this descriptive measure cannot be determined appropriately. This descriptive measure is used by the researcher to address the various issues, like the problem concerning wages, the distribution of wealth, etc.

Dissertation Statistics Services

Though every student dreams of finishing his or her dissertation on-time and successfully, unfortunately, that is not usually the case. This is because many students struggle greatly when it comes to writing their dissertation. Oftentimes real life gets in the way, and deadlines get pushed aside for other tests, papers and life circumstances. And though the dissertation is the most important aspect of a student’s career, it oftentimes goes on the back burner as students are overwhelmed with the entire process of writing the dissertation.

There is help available, however, and this help comes in the form of dissertation statistics services. Dissertation statistics services are provided by dissertation consultants and dissertation consulting firms. These dissertation consultants offering dissertation statistics services are well versed in every single aspect of the dissertation. Dissertation statistics services, then, can provide relief to the struggling student as dissertation statistics services can step in and help the student finish the dissertation.

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One of the most difficult aspects of writing a dissertation is the statistics portion of the dissertation. Dissertation statistics services can help the student with their statistics needs as dissertation statistics services can offer one-on-one statistics help. The statistics in the dissertation need to be absolutely precise, and for this reason, dissertation statistics services can prove to be invaluable. In other words, dissertation statistics services can provide help on the most challenging aspect of the dissertation.

Dissertation statistics services can provide help every single step of the way as dissertation statistics services are offered by experts who are trained in statistics. Dissertation statistics services can therefore be helpful in the very beginning of a project as dissertation statistics services can offer valuable feedback as to whether or not the topic can actually be studied, and whether or not proper statistics can be obtained on the topic of study. Dissertation statistics services will ensure that the student chooses a suitable topic and this can save the student an incredible amount of time as some students get started on their topic only to realize that it cannot or should not be studied.

Dissertation statistics services also provide help, assistance and guidance on the statistics portion of the dissertation. Dissertation statistics services will guide the student every step of the way as they try to gather data, interpret that data and use that data to write a thesis and a dissertation. Many students are not well prepared for the statistics portion of the dissertation and this is not the students’ fault. In fact, most students spend their time studying their field of expertise, not studying statistics. For this reason, dissertation statistics services can prove to be invaluable as dissertation statistics services can step in and explain everything that needs to be explained about statistics. Dissertation statistics services provided by statistical consultants are trained to offer expert advice in statistics as most people offering this advice are statisticians. Additionally, most dissertation consulting firms are staffed with people who themselves have received their PhD and who therefore have written a dissertation. As such, the people who offer dissertation statics services know just how difficult it can be to write a dissertation. The people who offer dissertation statistics services know, for example, that a timeline is necessary if a dissertation is to be completed on time. Dissertation statistics services offer guidance and support to the student to make that timeline and what’s more, dissertation statistics services makes sure that that timeline is followed precisely.

Acquiring dissertation statistics services can be the best decision a student can make. And while all students dream of finishing their dissertation easily, on-time, and successfully, oftentimes this is not possible without the help of dissertation statistics services.

Sample Size

Sample size calculation is one of the crucial steps in statistical analyses. The extraction of the sample size is generally a tedious process in the preparation of a particular statistical study.

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The statistical studies that consist of various kinds of surveys, experiments, observational studies, etc. always give valid conclusions only if they are appropriately and significantly planned by keeping certain things in mind. These things include the reliability of the instruments to be used for measuring, the appropriate understanding of the problem, and the determination of an adequate sample size.

Because type II errors are generally committed by the researcher due to inadequate sample size, some clinical trial sample size calculation programs have been developed. This sample size calculation program has been specifically designed in order to avoid Type II errors which are a serious kind of error, especially in the field of medicine.

The sample size program can be downloaded from some clinical related websites. This sample size calculation program also has certain programs which can be used for computing the group sequential boundaries. The sample size calculation program is generally located in the bottom half of the window that is downloaded from the clinical website.

The sample size program is generally written in the form of java programming language. This means that the sample size determination program can run on any kind of computer provided if it has the java runtime environment (JRE). Generally, MS Windows has the java runtime environment (JRE) enabled in them and the sample size.jar file can be directly executed from window explorer.

JRE can be downloaded with the help of some java related websites. After that is complete, one can run sample size calculation programs on the computer. The sample size calculation program can also be run in a non MS windows using the command prompt “java-jar sample size.jar.”

The sample size calculation program consists of a file menu which has three tabs, namely dichotomous, continuous and survival.

The sample size calculation program for the dichotomous type of response has variables like ‘yes or no,’ ‘heads and tails,’ ‘presence or absence,’ etc. In the sample size calculation program, it is assumed that the user who will be working on the sample size calculation should be well versed with the different parameters.

There is also a calculate button in the screen of the sample size calculation program. This button can help the user to perform different sorts of sample size calculations, like calculate power, etc. In the case where the sample size calculation is not possible, then it warns the user with a popup message, and thus no calculation will be performed.

For the sample size calculation program for the continuous response variables, the screen is divided into two halves.

In the one half there is sample size calculation, and in the other half there is the saved results. The user using the sample size calculation program can provide the values in any open cells in the calculation section. Similar to the dichotomous case, if the sample size calculation is not possible, then the warning message of “no calculation” appears on the screen as a popup message.

In the case of survival, the sample size calculation program is done on those kinds of variables where the primary variable is the occurrence of some event.