If you are a student working to receive your doctoral degree, then you know just how difficult it is to finish your dissertation. Chances are that you’ve struggled with the statistics portion of the dissertation, you’ve had difficulty getting help when you need it, and you’ve pushed back deadlines over and over. If this is true, you are certainly not alone, and for this reason, doctoral dissertation consultants are available to help.
For help with your doctoral dissertation, click here.
Doctoral dissertation consultants make the task of writing, working on, and finishing the dissertation much more manageable. This is because doctoral dissertation consultants do exactly what their name implies— they help doctoral candidates attain their doctoral degrees by consulting them on their dissertation. This consultation comes in many forms and can help in every aspect of the dissertation.
Doctoral dissertation consultants are there to assist you throughout the dissertation process. In fact, doctoral dissertation consultants can even help you choose your topic. You no-doubt have an idea of what you want to study, but doctoral dissertation consultants can help you narrow down that topic and doctoral dissertation consultants can help you do the initial research that you must do before you choose your topic. And though it might seem obvious to choose a topic, this is in fact not always the case as many topics sound like a good idea, but do not make sense statistically. Thus, doctoral dissertation consultants can point you in the right direction as you choose a topic that is interesting to you.
Once you have chosen a topic, doctoral dissertation consultants can help you word or phrase that topic in a statistically-appropriate manner. If the topic is not phrased correctly, it will not get accepted and doctoral dissertation consultants are available to help steer you in the right direction in terms of phrasing the topic accurately and appropriately.
Once the topic has been chosen and is written in a statistically-appropriate manner, doctoral dissertation consultants will help you carry out the research portion of your dissertation. This is by far the most time-consuming area of the dissertation. This can be made even more time consuming if you gather data incorrectly or if you gather biased data. Doctoral dissertation consultants will not let that happen, however, as doctoral dissertation consultants are trained in statistics and can help you gather data. Doctoral dissertation consultants know all of the rules, guidelines, procedures and protocols for gathering data and thus, doctoral dissertation consultants will help you every step of the way in terms of gathering data. And while other doctoral degree-seeking students might struggle with gathering data and might have to start over because their data is invalid, with the help of doctoral dissertation consultants, you will be able to move on to the next step quickly and efficiently.
Because doctoral dissertation consultants are trained statisticians, they can also help you interpret the data that you have obtained. This too can be very time consuming if you are not trained in statistics. Granted, some students have the statistical know-how to get the job done efficiently, but most students are not in that same boat! In other words, most students are not trained in statistics (an anthropology major looking to get his or her dissertation, for example, might not have all of the necessary training in statistics). Thus, doctoral dissertation consultants will help you with your statistical needs and a doctoral dissertation consultant will guide you step by step through the process of statistics. In other words, not only will you come up with valid inferences and results, but you will also understand these results. This last part, the understanding of the statistics, is crucial, as it will be you who has to defend your dissertation—not the doctoral dissertation consultants. Doctoral dissertation consultants know this and they therefore prepare you for the defense of your dissertation.
There is no question, then, that doctoral dissertation consultants can be extremely beneficial throughout the entire process of the dissertation.
Friday, May 29, 2009
Wednesday, May 27, 2009
Dissertation Statistics Services
One of the most helpful things that a student can do while working on his or her dissertation is to acquire the help of dissertation statistics services. Dissertation statistics services can ensure that a student finishes his or her dissertation on-time and successfully.
While the ideal time to seek the help of dissertation statistics services is at the very beginning of a project, a student can get the assistance of dissertation statistics services at any point while working on his or her dissertation. Of course, if a student seeks dissertation statistics services at the beginning of the project, dissertation statistics services can ensure that the student starts off on the right foot. This is true because many students do not seek the help of dissertation statistics services until they are months into their project and they begin to struggle as they realize that they have done parts of their research incorrectly. Dissertation statistics services can ensure that students avoid the frustrating experience of having to start parts of the dissertation all over.
Dissertation statistics services provide help on all things related to statistics. This is true because dissertation statistics services are staffed with experts in both statistics and dissertations. In fact, most people who offer dissertation statistics services have themselves acquired their dissertation degree. This is helpful because these people offering dissertation statistics services know exactly what students should expect as they have gone through it themselves.
One of the most important parts of the dissertation is the statistics involved in the dissertation. Without statistics, a student cannot prove his or her thesis, and therefore cannot prove his or her dissertation properly. Statistics, then, play a huge role in the dissertation. Oftentimes, however, students are not trained adequately in statistics. Granted, they have taken some classes in statistics and they know the basics. This basic training, however, does not prepare a student for what they need to complete a successful dissertation. Dissertation statistics services can fill in the gaps of the student’s knowledge of statistics, however, as dissertation statistics assistance can provide all of the feedback, guidance and assistance that a doctoral-seeking student needs.
Dissertation statistics services help students with every statistical aspect of their dissertation. Thus, dissertation statistics services will help students perform the following important steps;
The services that dissertation statistics services provide are not limited to the dissertation, however. Dissertation statistics services will ensure that the student actually understands every single aspect of his or her dissertation. In other words, dissertation statistics services will go through every single point of the dissertation and every single methodology used in the dissertation. Dissertation statistics services do this so that the student can understand everything contained in the dissertation. This ensures that the student is able to properly defend his or her dissertation. In fact, dissertation statistics services are even willing and able to have video conferences about the data and information so that the student can understand it. In other words, dissertation statistics services will go to every possible end to ensure that the student both turns in a successful dissertation and also understands every single point made in that dissertation.
While the ideal time to seek the help of dissertation statistics services is at the very beginning of a project, a student can get the assistance of dissertation statistics services at any point while working on his or her dissertation. Of course, if a student seeks dissertation statistics services at the beginning of the project, dissertation statistics services can ensure that the student starts off on the right foot. This is true because many students do not seek the help of dissertation statistics services until they are months into their project and they begin to struggle as they realize that they have done parts of their research incorrectly. Dissertation statistics services can ensure that students avoid the frustrating experience of having to start parts of the dissertation all over.
Dissertation statistics services provide help on all things related to statistics. This is true because dissertation statistics services are staffed with experts in both statistics and dissertations. In fact, most people who offer dissertation statistics services have themselves acquired their dissertation degree. This is helpful because these people offering dissertation statistics services know exactly what students should expect as they have gone through it themselves.
One of the most important parts of the dissertation is the statistics involved in the dissertation. Without statistics, a student cannot prove his or her thesis, and therefore cannot prove his or her dissertation properly. Statistics, then, play a huge role in the dissertation. Oftentimes, however, students are not trained adequately in statistics. Granted, they have taken some classes in statistics and they know the basics. This basic training, however, does not prepare a student for what they need to complete a successful dissertation. Dissertation statistics services can fill in the gaps of the student’s knowledge of statistics, however, as dissertation statistics assistance can provide all of the feedback, guidance and assistance that a doctoral-seeking student needs.
Dissertation statistics services help students with every statistical aspect of their dissertation. Thus, dissertation statistics services will help students perform the following important steps;
- Dissertation statistics services will ensure that the proper data is collected and acquired. This is oftentimes very difficult and time consuming as there is precise methodology for the collection of data.
- Dissertation statistics services will ensure that the student is using the proper sample size. This too can be very time consuming as the wrong sample size will nullify the data.
- Dissertation statistics services will interpret the results of the data and dissertation statistics services will ensure that those results fit into the dissertation.
The services that dissertation statistics services provide are not limited to the dissertation, however. Dissertation statistics services will ensure that the student actually understands every single aspect of his or her dissertation. In other words, dissertation statistics services will go through every single point of the dissertation and every single methodology used in the dissertation. Dissertation statistics services do this so that the student can understand everything contained in the dissertation. This ensures that the student is able to properly defend his or her dissertation. In fact, dissertation statistics services are even willing and able to have video conferences about the data and information so that the student can understand it. In other words, dissertation statistics services will go to every possible end to ensure that the student both turns in a successful dissertation and also understands every single point made in that dissertation.
Dissertation Statistics Consultant
There is nothing more helpful to a doctoral student than a dissertation statistics consultant. Because the dissertation is the most important part of a doctoral candidate’s academic career, seeking a dissertation statistics consultant can be the most important and wise decision a student can make.
Click here for a dissertation statistics consultant.
A dissertation statistics consultant will ensure that the doctoral student successfully finishes his or her dissertation. The dissertation statistics consultant does this by helping the student from the very beginning of the dissertation. A dissertation statistics consultant, then, should be hired at the very beginning of the project as this will save the student much time and energy.
Once the dissertation statistics consultant is hired, the student and the dissertation statistics consultant can get to work on the very important and very lengthy process of working on, researching and writing the dissertation. The first thing that happens once a student hires a dissertation statistics consultant is that the student and the dissertation statistics consultant have a conversation about what the student actually wants to study. This conversation is extremely important because in this conversation, the student can verbalize everything that they want to study and everything that they need help with from the dissertation statistics consultant. This is also a good time to see whether or not the dissertation statistics consultant is qualified to help the student. In this conversation, the student can tell if the dissertation statistics consultant is 1) articulate and a good communicator, 2) willing to help in all things related to the dissertation, and 3) proficient in writing dissertations. The student should not be afraid to ask the dissertation statistics consultant about their qualifications, etc.
Once this conversation takes place and the student is satisfied with the dissertation statistics consultant, they can put together a plan that will work. In other words, they can collectively come up with a timeline to get things done. This timeline is essential as the dissertation can take months to finish. Without a timeline, students usually end up finishing well after they had anticipated.
Once the timeline is complete and the topic is chosen and approved, the dissertation statistics consultant and the student can get to work on the most important part of the dissertation—the statistics part of the dissertation. The statistics part of the dissertation is the most important part because the statistics will provide the evidence for the student’s theory or thesis. Without the statistics, the dissertation does not actually prove anything. Thus, the statistics part of the dissertation is the most important part of the dissertation.
This, of course, is where the dissertation statistics consultant can be most useful, as oftentimes, students do not know everything they need to know in order to get valid statistics on which to base their dissertations. Every aspect of getting accurate statistics can take an extremely long time—but this is not true with the steady help of dissertation statistics consultants! Dissertation statistics consultants will help the student step by step as he or she does everything that he/she needs to do in order to get accurate statistics and results. This includes the gathering of the data (which is lengthy and difficult in and of itself—as the proper gathering of data involves following very precise rules, guidelines, methodologies and regulations), the interpretation of the data (once again this is lengthy and difficult as this too requires that a student follow proper protocol and methodology), and the application of this data to the dissertation.
Once all of this is complete, the dissertation statistics consultant will go over every single aspect of the completed dissertation. The dissertation statistics consultant will even proofread the entire document—something that can save the student from turning in a dissertation full of minor errors (something that no student wants to do).
Finally and perhaps most importantly, the dissertation statistics consultant will ensure that the student actually understands everything that he or she has written about. Clearly, dissertation statistics consultants go a long way in ensuring success for the student.
Click here for a dissertation statistics consultant.
A dissertation statistics consultant will ensure that the doctoral student successfully finishes his or her dissertation. The dissertation statistics consultant does this by helping the student from the very beginning of the dissertation. A dissertation statistics consultant, then, should be hired at the very beginning of the project as this will save the student much time and energy.
Once the dissertation statistics consultant is hired, the student and the dissertation statistics consultant can get to work on the very important and very lengthy process of working on, researching and writing the dissertation. The first thing that happens once a student hires a dissertation statistics consultant is that the student and the dissertation statistics consultant have a conversation about what the student actually wants to study. This conversation is extremely important because in this conversation, the student can verbalize everything that they want to study and everything that they need help with from the dissertation statistics consultant. This is also a good time to see whether or not the dissertation statistics consultant is qualified to help the student. In this conversation, the student can tell if the dissertation statistics consultant is 1) articulate and a good communicator, 2) willing to help in all things related to the dissertation, and 3) proficient in writing dissertations. The student should not be afraid to ask the dissertation statistics consultant about their qualifications, etc.
Once this conversation takes place and the student is satisfied with the dissertation statistics consultant, they can put together a plan that will work. In other words, they can collectively come up with a timeline to get things done. This timeline is essential as the dissertation can take months to finish. Without a timeline, students usually end up finishing well after they had anticipated.
Once the timeline is complete and the topic is chosen and approved, the dissertation statistics consultant and the student can get to work on the most important part of the dissertation—the statistics part of the dissertation. The statistics part of the dissertation is the most important part because the statistics will provide the evidence for the student’s theory or thesis. Without the statistics, the dissertation does not actually prove anything. Thus, the statistics part of the dissertation is the most important part of the dissertation.
This, of course, is where the dissertation statistics consultant can be most useful, as oftentimes, students do not know everything they need to know in order to get valid statistics on which to base their dissertations. Every aspect of getting accurate statistics can take an extremely long time—but this is not true with the steady help of dissertation statistics consultants! Dissertation statistics consultants will help the student step by step as he or she does everything that he/she needs to do in order to get accurate statistics and results. This includes the gathering of the data (which is lengthy and difficult in and of itself—as the proper gathering of data involves following very precise rules, guidelines, methodologies and regulations), the interpretation of the data (once again this is lengthy and difficult as this too requires that a student follow proper protocol and methodology), and the application of this data to the dissertation.
Once all of this is complete, the dissertation statistics consultant will go over every single aspect of the completed dissertation. The dissertation statistics consultant will even proofread the entire document—something that can save the student from turning in a dissertation full of minor errors (something that no student wants to do).
Finally and perhaps most importantly, the dissertation statistics consultant will ensure that the student actually understands everything that he or she has written about. Clearly, dissertation statistics consultants go a long way in ensuring success for the student.
Tuesday, May 26, 2009
Latent Class Analysis (LCA)
Latent class analysis (LCA) is a multivariate technique that can be applied for cluster, factor, or regression purposes.
Latent class analysis (LCA) is commonly used by the researcher in cases where it is required to perform classification of cases into a set of latent classes. The Latent class analysis (LCA) carried out on latent classes are based on categorical types of indicator variables. In Latent class analysis (LCA), indicator variables are those variables that are assigned as ‘1’ if their condition is true, and are otherwise assigned as ‘0.’
Latent class analysis (LCA) uses a variant called Latent profile analysis for continuous variables. Mixture modeling with the structural equation models is a major type of Latent class analysis (LCA).
Latent class analysis (LCA) divides the cases into latent classes that are conditionally independent. In other words, Latent class analysis (LCA) divides those cases in which the variables of interest are not correlated within any other variables in the class.
The model parameters in Latent class analysis (LCA) are the maximum likelihood estimates (MLE) of conditional response probabilities.
There are two ways by which the number of the latent classes in the Latent class analysis (LCA) is determined. The first and more popular method is to perform an iterative test of goodness of fit models with the latent classes in Latent class analysis (LCA) using the likelihood ratio chi square test.
The other method is the method of bootstrapping of the latent classes in Latent class analysis (LCA). The rho estimates refer to the item response probabilities in Latent class analysis (LCA).
The odds ratio in Latent class analysis (LCA) measures the effective sizes of the covariates in the model. The odds ratio in Latent class analysis (LCA) is calculated by carrying out multinomial regression. The dependent variable in this regression in Latent class analysis (LCA) is the latent class variable, and the independent variable is the covariate.
If the value of the odds ratio in Latent class analysis (LCA) is 1.5 for class 1, then it means that a unit increase in the covariate corresponds to a 50 % greater likelihood.
The posterior probabilities in Latent class analysis (LCA) refer to the probability of that observation that is classified in a given class.
Latent class analysis (LCA) is done using software called Latent Gold. This software in Latent class analysis (LCA) implements Latent class models for cluster analysis, factor analysis, etc. The latent models in Latent class analysis (LCA) support nominal, ordinal as well as continuous data.
There are certain measures of model fit in Latent class analysis (LCA).
The latent model in Latent class analysis (LCA) can be fitted to the data with the help of likelihood ratio chi square. The larger the value of the statistic in Latent class analysis (LCA), the more inefficient the model is to fit the data.
The difference chi square in Latent class analysis (LCA) is used to calculate the difference of the two model chi squares for the two nested models.
In order to assess the validity or the reliability of Latent class analysis (LCA) a statistic called Cressie-Read statistic is used. The validity of Latent class analysis (LCA) can be assessed with the help of the probability value being compared with the probability value of the model chi square.
It is assumed that Latent class analysis (LCA) does not follow linearity within the data.
Latent class analysis (LCA) does not follow the normal distribution of the data.
Latent class analysis (LCA) does not follow the homogeneity of variances.
Latent class analysis (LCA) is commonly used by the researcher in cases where it is required to perform classification of cases into a set of latent classes. The Latent class analysis (LCA) carried out on latent classes are based on categorical types of indicator variables. In Latent class analysis (LCA), indicator variables are those variables that are assigned as ‘1’ if their condition is true, and are otherwise assigned as ‘0.’
Latent class analysis (LCA) uses a variant called Latent profile analysis for continuous variables. Mixture modeling with the structural equation models is a major type of Latent class analysis (LCA).
Latent class analysis (LCA) divides the cases into latent classes that are conditionally independent. In other words, Latent class analysis (LCA) divides those cases in which the variables of interest are not correlated within any other variables in the class.
The model parameters in Latent class analysis (LCA) are the maximum likelihood estimates (MLE) of conditional response probabilities.
There are two ways by which the number of the latent classes in the Latent class analysis (LCA) is determined. The first and more popular method is to perform an iterative test of goodness of fit models with the latent classes in Latent class analysis (LCA) using the likelihood ratio chi square test.
The other method is the method of bootstrapping of the latent classes in Latent class analysis (LCA). The rho estimates refer to the item response probabilities in Latent class analysis (LCA).
The odds ratio in Latent class analysis (LCA) measures the effective sizes of the covariates in the model. The odds ratio in Latent class analysis (LCA) is calculated by carrying out multinomial regression. The dependent variable in this regression in Latent class analysis (LCA) is the latent class variable, and the independent variable is the covariate.
If the value of the odds ratio in Latent class analysis (LCA) is 1.5 for class 1, then it means that a unit increase in the covariate corresponds to a 50 % greater likelihood.
The posterior probabilities in Latent class analysis (LCA) refer to the probability of that observation that is classified in a given class.
Latent class analysis (LCA) is done using software called Latent Gold. This software in Latent class analysis (LCA) implements Latent class models for cluster analysis, factor analysis, etc. The latent models in Latent class analysis (LCA) support nominal, ordinal as well as continuous data.
There are certain measures of model fit in Latent class analysis (LCA).
The latent model in Latent class analysis (LCA) can be fitted to the data with the help of likelihood ratio chi square. The larger the value of the statistic in Latent class analysis (LCA), the more inefficient the model is to fit the data.
The difference chi square in Latent class analysis (LCA) is used to calculate the difference of the two model chi squares for the two nested models.
In order to assess the validity or the reliability of Latent class analysis (LCA) a statistic called Cressie-Read statistic is used. The validity of Latent class analysis (LCA) can be assessed with the help of the probability value being compared with the probability value of the model chi square.
It is assumed that Latent class analysis (LCA) does not follow linearity within the data.
Latent class analysis (LCA) does not follow the normal distribution of the data.
Latent class analysis (LCA) does not follow the homogeneity of variances.
Hypothesis Testing
Hypothesis testing is a scientific process of testing whether or not the hypothesis is plausible.
The following steps are involved in hypothesis testing:
The first step in hypothesis testing is to state the null and alternative hypothesis clearly. The null and alternative hypothesis in hypothesis testing can be a one tailed or two tailed test.
The second step in hypothesis testing is to determine the test size. This means that the researcher decides whether a test should be one tailed or two tailed to get the right critical value and the rejection region.
The third step in hypothesis testing is to compute the test statistic and the probability value. This step of the hypothesis testing also involves the construction of the confidence interval depending upon the testing approach.
The fourth step in hypothesis testing involves the decision making step. This step of hypothesis testing helps the researcher reject or accept the null hypothesis by making comparisons between the subjective criterion from the second step and the objective test statistic or the probability value from the third step.
The fifth step in hypothesis testing is to draw a conclusion about the data and interpret the results obtained from the data.
There are basically three approaches to hypothesis testing. The researcher should note that all three approaches require different subject criteria and objective statistics, but all three approaches of hypothesis testing give the same conclusion.
The first approach of hypothesis testing is to test the statistic approach.
The common steps in all three approaches of hypothesis testing is the first step, which is to state the null and alternative hypothesis.
The second step of the test statistic approach of hypothesis testing is to determine the test size and to obtain the critical value. The third step of the test statistic approach of hypothesis testing is to compute the test statistic. The fourth step of the test statistic approach of hypothesis testing is to reject or accept the null hypothesis depending upon the comparison between the tabulated value and the calculated value. If the tabulated value in hypothesis testing is more than the calculated value, than the null hypothesis is accepted. Otherwise it is rejected. The last step of this approach of hypothesis testing is to make a substantive interpretation.
The second approach of hypothesis testing is the probability value approach. The second step of this approach in hypothesis testing is to determine the test size. The third step of this approach of hypothesis testing is to compute the test statistic and the probability value. The fourth step of this approach of hypothesis testing is to reject the null hypothesis if the probability value is less than the tabulated value. The last step of this approach of hypothesis testing is to make a substantive interpretation.
The third approach of hypothesis testing is the confidence interval approach. The second step of hypothesis testing is to determine the test size or the (1-test size) and the hypothesized value. The third step of hypothesis testing is to construct the confidence interval. The fourth step of hypothesis testing is to reject the null hypothesis if the hypothesized value does not exist in the range of the confidence interval. The last step of this approach of hypothesis testing is to make the substantive interpretation.
The first approach of hypothesis testing is a classical test statistic approach, which computes a test statistic from the empirical data and then makes a comparison with the critical value. If the test statistic in this classical approach of the hypothesis testing is larger than the critical value, then the null hypothesis is rejected. Otherwise, it is accepted.
The following steps are involved in hypothesis testing:
The first step in hypothesis testing is to state the null and alternative hypothesis clearly. The null and alternative hypothesis in hypothesis testing can be a one tailed or two tailed test.
The second step in hypothesis testing is to determine the test size. This means that the researcher decides whether a test should be one tailed or two tailed to get the right critical value and the rejection region.
The third step in hypothesis testing is to compute the test statistic and the probability value. This step of the hypothesis testing also involves the construction of the confidence interval depending upon the testing approach.
The fourth step in hypothesis testing involves the decision making step. This step of hypothesis testing helps the researcher reject or accept the null hypothesis by making comparisons between the subjective criterion from the second step and the objective test statistic or the probability value from the third step.
The fifth step in hypothesis testing is to draw a conclusion about the data and interpret the results obtained from the data.
There are basically three approaches to hypothesis testing. The researcher should note that all three approaches require different subject criteria and objective statistics, but all three approaches of hypothesis testing give the same conclusion.
The first approach of hypothesis testing is to test the statistic approach.
The common steps in all three approaches of hypothesis testing is the first step, which is to state the null and alternative hypothesis.
The second step of the test statistic approach of hypothesis testing is to determine the test size and to obtain the critical value. The third step of the test statistic approach of hypothesis testing is to compute the test statistic. The fourth step of the test statistic approach of hypothesis testing is to reject or accept the null hypothesis depending upon the comparison between the tabulated value and the calculated value. If the tabulated value in hypothesis testing is more than the calculated value, than the null hypothesis is accepted. Otherwise it is rejected. The last step of this approach of hypothesis testing is to make a substantive interpretation.
The second approach of hypothesis testing is the probability value approach. The second step of this approach in hypothesis testing is to determine the test size. The third step of this approach of hypothesis testing is to compute the test statistic and the probability value. The fourth step of this approach of hypothesis testing is to reject the null hypothesis if the probability value is less than the tabulated value. The last step of this approach of hypothesis testing is to make a substantive interpretation.
The third approach of hypothesis testing is the confidence interval approach. The second step of hypothesis testing is to determine the test size or the (1-test size) and the hypothesized value. The third step of hypothesis testing is to construct the confidence interval. The fourth step of hypothesis testing is to reject the null hypothesis if the hypothesized value does not exist in the range of the confidence interval. The last step of this approach of hypothesis testing is to make the substantive interpretation.
The first approach of hypothesis testing is a classical test statistic approach, which computes a test statistic from the empirical data and then makes a comparison with the critical value. If the test statistic in this classical approach of the hypothesis testing is larger than the critical value, then the null hypothesis is rejected. Otherwise, it is accepted.
Content Analysis
Content analysis provides information related to newspaper promotions, stories, photographs, displays and classified advertisements, etc. All of these fall in the scope of the study of Content analysis. Content analysis consists of counting and classifying content.
For a free consultation on content analysis, click here.
In Content analysis, the researcher gathers information about the entire paper. This information includes the number of pages, the number of sections, etc. Then, an in-depth Content analysis is performed by the researcher on each content item.
The researcher in Content analysis analyzes each story in terms of its attributes, including topics, sources treatments, writing styles, etc. The researcher in Content analysis analyzes newspaper promotions for things like type, color, topic and size.
The researchers conduct content analysis for certain reasons. These reasons are as follows:
There are certain terms that are used in content analysis that are helpful in understanding Content analysis. For example, unitizing in content analysis is a process in which the investigator establishes uniformity in the analysis. Thus, the researcher in content analysis unitizes the words, sentences, paragraphs, etc.
Sampling is one of the crucial weapons in content analysis. The sampling plan in Content analysis is designed to minimize the distortion caused in some particular content due to certain major events, etc. In Content analysis, the content is generally enormous. Thus, the researcher utilizes the technique of sampling in order to make his content in content analysis less complicated. The theory behind sampling in content analysis consists of counting. This involves development of different kinds of similar-meaning terms.
Inference is a major part of content analysis. A contextual phenomenon in content analysis must be analyzed in order to obtain a valid inference of the context for findings.
Content analysis involves conclusions that are usually communicated by the researcher in a narrative manner.
There are basically two assumptions in content analysis. First, content analysis is generally assumed to be subjected to the problems of sampling. Second, content analysis is assumed to be based upon the context for words and meanings.
There are certain software resources for conducting content analysis. These include the following:
For a free consultation on content analysis, click here.
In Content analysis, the researcher gathers information about the entire paper. This information includes the number of pages, the number of sections, etc. Then, an in-depth Content analysis is performed by the researcher on each content item.
The researcher in Content analysis analyzes each story in terms of its attributes, including topics, sources treatments, writing styles, etc. The researcher in Content analysis analyzes newspaper promotions for things like type, color, topic and size.
The researchers conduct content analysis for certain reasons. These reasons are as follows:
- Content analysis is usually carried out to study the discrepancy in the trends of the content with respect to time.
- Content analysis is carried out to describe the reasons why the readers focus on certain topics of the content.
- Content analysis can be used to make comparisons on international differences.
- Content analysis helps in comparing group differences in the content.
- Content analysis can expose the usage of biased terms in the research. Such biased terms can influence the opinions or behaviors of people.
- Content analysis is also useful in the testing of hypotheses about the cultural and symbolic usages of terms in the content.
- Content analysis helps the researcher for purposes of coding as well. Coding on open ended questions is done with the help of Content analysis.
There are certain terms that are used in content analysis that are helpful in understanding Content analysis. For example, unitizing in content analysis is a process in which the investigator establishes uniformity in the analysis. Thus, the researcher in content analysis unitizes the words, sentences, paragraphs, etc.
Sampling is one of the crucial weapons in content analysis. The sampling plan in Content analysis is designed to minimize the distortion caused in some particular content due to certain major events, etc. In Content analysis, the content is generally enormous. Thus, the researcher utilizes the technique of sampling in order to make his content in content analysis less complicated. The theory behind sampling in content analysis consists of counting. This involves development of different kinds of similar-meaning terms.
Inference is a major part of content analysis. A contextual phenomenon in content analysis must be analyzed in order to obtain a valid inference of the context for findings.
Content analysis involves conclusions that are usually communicated by the researcher in a narrative manner.
There are basically two assumptions in content analysis. First, content analysis is generally assumed to be subjected to the problems of sampling. Second, content analysis is assumed to be based upon the context for words and meanings.
There are certain software resources for conducting content analysis. These include the following:
- ATLAS.ti is used in content analysis as software for text analysis and model building.
- The General Inquirer is the classic package for content analysis.
- Intext and TextQuest is software developed by Harald Klein for content analysis.
Heteroscedasticity
The crucial assumption of a classical linear regression model is that the volatility that has occurred in the model should be uniform in nature. If this assumption is not satisfied by the model, then one would have to consider that the model has been exposed to heteroscedasticity.
There are examples that can be discussed to gain a better understanding of heteroscedasticity. In the case of an income expenditure model, if the income is decreased, then the expenditure will also simultaneously decrease, and vice versa. If, however, heteroscedasticity is present in the model, then as the income is increased, then the graph for the expenditure variable would remain constant.
For a free consultation on heteroscedasticity, click here.
Heteroscedasticity generally occurs due to the presence of an outlier. An outlier in relation to heteroscedasticity is nothing but an observation that is numerically apart from the rest of the observations given in the data.
Heteroscedasticity can occur if a major variable is eliminated from the model. In the case of the income expenditure model, for example, if the variable called ‘income’ is eliminated, then there would be no inference from that model, and one would have to consider that the model has undergone heteroscedasticity.
Heteroscedasticity can also occur due to the presence of symmetrical or assymeterical curves of the regressor included in the model.
Heteroscedasticity can also occur due to false data transformation and incorrect functional form (like comparisons between a linear model and a log linear model, etc.).
Heteroscedasticity is a common or popular type of disturbance, especially in cases involving cross sectional data or time series data. If investigators who conduct ordinary least squares (OLS) do not consider the disturbance caused by heteroscedasticity, then they would not be able to examine the confidence intervals and the tests of hypotheses. This is because in the presence of heteroscedasticity, the variance calculated would be significantly less than the variance of the best linear unbiased estimator. As a result, the outcomes of the significant tests will not be accurate due to heteroscedasticity.
For a researcher to detect the presence of heteroscedasticity in the data, certain informal tests have been proposed by several econometricians.
There is a high probability of heteroscedasticity in a cross sectional data if small, medium and large organizations are sampled together.
An informal method, called the graphical method, helps the researcher to detect the presence of heteroscedasticity. If the investigator assumes that there is no heteroscedasticity and then performs regression analysis, the estimated residuals (with the help of the graphical method) would then exhibit certain patterns that would indicate the presence of heteroscedasticity.
A formal test, called Spearman’s rank correlation test, is used by the researcher to detect the presence of heteroscedasticity.
Suppose the researcher assumes a simple linear model, for example- Yi = β0 + β1Xi + ui - to detect the presence of heteroscedasticity. The researcher then fits the model to the data by calculating the absolute values of the residual and further sorting them in ascending or descending manner to detect heteroscedasticity. Then, the researcher computes the value of Spearman’s rank correlation for heteroscedasticity.
The researcher then assumes the population rank correlation coefficient as zero and the size of the sample is assumed to be greater than 8 for heteroscedasticity. A significance test is carried out to detect heteroscedasticity. If the computed value of t is more than the tabulated value, then the researcher assumes that heteroscedasticity is present in the data. Otherwise heteroscedasticity is not present in the data.
There are examples that can be discussed to gain a better understanding of heteroscedasticity. In the case of an income expenditure model, if the income is decreased, then the expenditure will also simultaneously decrease, and vice versa. If, however, heteroscedasticity is present in the model, then as the income is increased, then the graph for the expenditure variable would remain constant.
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Heteroscedasticity generally occurs due to the presence of an outlier. An outlier in relation to heteroscedasticity is nothing but an observation that is numerically apart from the rest of the observations given in the data.
Heteroscedasticity can occur if a major variable is eliminated from the model. In the case of the income expenditure model, for example, if the variable called ‘income’ is eliminated, then there would be no inference from that model, and one would have to consider that the model has undergone heteroscedasticity.
Heteroscedasticity can also occur due to the presence of symmetrical or assymeterical curves of the regressor included in the model.
Heteroscedasticity can also occur due to false data transformation and incorrect functional form (like comparisons between a linear model and a log linear model, etc.).
Heteroscedasticity is a common or popular type of disturbance, especially in cases involving cross sectional data or time series data. If investigators who conduct ordinary least squares (OLS) do not consider the disturbance caused by heteroscedasticity, then they would not be able to examine the confidence intervals and the tests of hypotheses. This is because in the presence of heteroscedasticity, the variance calculated would be significantly less than the variance of the best linear unbiased estimator. As a result, the outcomes of the significant tests will not be accurate due to heteroscedasticity.
For a researcher to detect the presence of heteroscedasticity in the data, certain informal tests have been proposed by several econometricians.
There is a high probability of heteroscedasticity in a cross sectional data if small, medium and large organizations are sampled together.
An informal method, called the graphical method, helps the researcher to detect the presence of heteroscedasticity. If the investigator assumes that there is no heteroscedasticity and then performs regression analysis, the estimated residuals (with the help of the graphical method) would then exhibit certain patterns that would indicate the presence of heteroscedasticity.
A formal test, called Spearman’s rank correlation test, is used by the researcher to detect the presence of heteroscedasticity.
Suppose the researcher assumes a simple linear model, for example- Yi = β0 + β1Xi + ui - to detect the presence of heteroscedasticity. The researcher then fits the model to the data by calculating the absolute values of the residual and further sorting them in ascending or descending manner to detect heteroscedasticity. Then, the researcher computes the value of Spearman’s rank correlation for heteroscedasticity.
The researcher then assumes the population rank correlation coefficient as zero and the size of the sample is assumed to be greater than 8 for heteroscedasticity. A significance test is carried out to detect heteroscedasticity. If the computed value of t is more than the tabulated value, then the researcher assumes that heteroscedasticity is present in the data. Otherwise heteroscedasticity is not present in the data.
Dissertation Statistics Consultation
A dissertation statistics consultation is a service that a dissertation statistics firm can provide to anyone who needs to write a dissertation. A dissertation statistics consultation is a way to help students who are struggling with the statistics part of their dissertation. Thus, dissertation statistics consultations can make the difficult task of writing a dissertation, and moreover, dealing with statistics, much more manageable.
To schedule a free dissertation statistics consultation, click here.
Dissertation statistics consultations involve closely working with a dissertation consultant. With the help of dissertation statistics consultations, the student can navigate the difficult aspects of his or her dissertation. And while a student’s advisor can provide similar help as a dissertation statistics consultation, oftentimes a student’s advisor is not easily accessible or available. Dissertation statistics consultations provided by dissertation consultants are always available however, as the main goal of the dissertation statistics consultation is to help students whenever they need help.
First, dissertation statistics consultations involve a lengthy discussion about the topic of study. In the dissertation statistics consultation, the student and the dissertation consultant discuss all aspects of the topic. The dissertation statistics consultation can provide valuable feedback to the student at this stage in the process, as the dissertation statistics consultation provided by the dissertation consultant will address several issues that are likely to come-up when the student attempts to get the topic approved. One such issue is whether or not the topic is appropriate and able to be studied. In other words, with the help of a dissertation statistics consultation, the student will have a better grasp of whether or not the topic can actually be studied, and whether or not that topic should be chosen by the student. Another issue that comes up during the topic- choosing phase is addressing the concern of whether or not that topic has been studied before. A proper dissertation statistics consultation will advise students on how to do research into whether or not their topic of study has already been studied.
The next service that a dissertation statistics consultation will provide is to explain to the student how to write that topic in a way that makes sense statistically. This part of the dissertation statistics consultation is essential because without the proper wording for the topic, the dissertation will not get approved. A dissertation statistics consultation will explain this wording so that it gets approved and makes sense to the student.
Once the topic is chosen and is phrased correctly, the dissertation statistics consultation provided by dissertation consultants can address the actual gathering of statistics. Because statistics is a science, the gathering of data and the interpretation of that data need to be done meticulously. A dissertation statistics consultation will ensure that the student knows how to gather proper statistics because a dissertation statistics consultation will discuss the methods, means and theories behind the gathering of data. Dissertation consultants are well versed in the gathering of data, and in the dissertation statistics consultation, the dissertation consultants will explain these precise methods of gathering data. Thus, with the help of dissertation consultants, students can gather data much quicker and much more efficiently.
Once the data is gathered, a dissertation statistics consultation provides all of the necessary information as to how to interpret the results and apply it to the dissertation. Here too the expertise of the dissertation consultant comes into play as the dissertation statistics consultation will go over every facet of the interpretation of results.
Finally, a dissertation statistics consultation service will ensure that the dissertation is finished accurately and on-time. With the on-going help of a dissertation consultant, the student will be able to finish his or her dissertation with much success.
To schedule a free dissertation statistics consultation, click here.
Dissertation statistics consultations involve closely working with a dissertation consultant. With the help of dissertation statistics consultations, the student can navigate the difficult aspects of his or her dissertation. And while a student’s advisor can provide similar help as a dissertation statistics consultation, oftentimes a student’s advisor is not easily accessible or available. Dissertation statistics consultations provided by dissertation consultants are always available however, as the main goal of the dissertation statistics consultation is to help students whenever they need help.
First, dissertation statistics consultations involve a lengthy discussion about the topic of study. In the dissertation statistics consultation, the student and the dissertation consultant discuss all aspects of the topic. The dissertation statistics consultation can provide valuable feedback to the student at this stage in the process, as the dissertation statistics consultation provided by the dissertation consultant will address several issues that are likely to come-up when the student attempts to get the topic approved. One such issue is whether or not the topic is appropriate and able to be studied. In other words, with the help of a dissertation statistics consultation, the student will have a better grasp of whether or not the topic can actually be studied, and whether or not that topic should be chosen by the student. Another issue that comes up during the topic- choosing phase is addressing the concern of whether or not that topic has been studied before. A proper dissertation statistics consultation will advise students on how to do research into whether or not their topic of study has already been studied.
The next service that a dissertation statistics consultation will provide is to explain to the student how to write that topic in a way that makes sense statistically. This part of the dissertation statistics consultation is essential because without the proper wording for the topic, the dissertation will not get approved. A dissertation statistics consultation will explain this wording so that it gets approved and makes sense to the student.
Once the topic is chosen and is phrased correctly, the dissertation statistics consultation provided by dissertation consultants can address the actual gathering of statistics. Because statistics is a science, the gathering of data and the interpretation of that data need to be done meticulously. A dissertation statistics consultation will ensure that the student knows how to gather proper statistics because a dissertation statistics consultation will discuss the methods, means and theories behind the gathering of data. Dissertation consultants are well versed in the gathering of data, and in the dissertation statistics consultation, the dissertation consultants will explain these precise methods of gathering data. Thus, with the help of dissertation consultants, students can gather data much quicker and much more efficiently.
Once the data is gathered, a dissertation statistics consultation provides all of the necessary information as to how to interpret the results and apply it to the dissertation. Here too the expertise of the dissertation consultant comes into play as the dissertation statistics consultation will go over every facet of the interpretation of results.
Finally, a dissertation statistics consultation service will ensure that the dissertation is finished accurately and on-time. With the on-going help of a dissertation consultant, the student will be able to finish his or her dissertation with much success.
Correlation
Correlation, as the name suggests, depicts a relationship between two or more variables under study. Correlation is generally categorized into two types, namely Bivariate Correlation and Partial Correlation.
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Bivariate Correlation is the one that shows an association between two variables. Correlation is the one that shows the association between two variables while keeping control or adjusting the effect of one or more additional variables.
A Correlation is a degree of measure, which means that a Correlation can be negative, positive, or perfect. A positive Correlation is a type of Correlation in which an increase changes the other variable. In other words, if there is an increase (or decrease) in one variable, then there is a simultaneous increase (decrease) in the other variable. A negative Correlation is a type of Correlation where if there is a decrease (or increase) in one variable, then there is a simultaneous increase (or decrease) in the other variables.
A perfect Correlation is that type of Correlation where a change in one variable affects an equivalent change in the other variable.
A British biometrician named Karl Pearson developed a formula to measure the degree of the Correlation, called the Correlation Coefficient. This Correlation Coefficient is generally depicted as ‘r.’ In mathematical language, the Correlation Coefficient, which was developed by the biometrician Karl Pearson, is defined as the ratio between the covariance of the two variables and the product of the square root of their individual variances. The range of the Correlation Coefficient generally lies between -1 to +1. If the value of the Correlation Coefficient is ‘+1,’ then the variable is said to be positively correlated. If, on the other hand, the value of the Correlation Coefficient is ‘-1,’ then the variable is said to be negatively correlated.
The value of the Correlation Coefficient does not depend upon the change in origin and the change in the scale.
If the value of the Correlation Coefficient is zero, then the variables are said to be uncorrelated. Thus, the variables would be regarded as independent. If there is no Correlation in the variables, then the change in one variable will not affect the change in the other variable at all, and therefore the variables will be independent.
However, the researcher should note that the two independent variables are not in any Correlation if the covariance of the variables is zero. This, however, is not true in the opposite case. This means that if the covariance of the two variables is zero, then it does not necessarily mean that the two variables are independent.
There are certain assumptions that come along with the Correlation Coefficient. The following are the assumptions for the Correlation Coefficient:
The Correlation Coefficient assumes that the variables under study should be linearly correlated.
Correlation coefficient assumes that a cause and effect relationship exists between different forces operating on the items of the two variable series. Such forces assumed by the correlation coefficient must be common to both series.
For the cases where operating forces are entirely independent, then the value of the correlation coefficient must be zero. If the value of the correlation coefficient is not zero, then in such cases, correlation is often termed as chance correlation or spurious correlation. For example, the correlation between the income of a person and the height of a person is a case of spurious correlation. Another example of spurious correlation is the correlation between the size of the shoe and the intelligence of a certain group of people.
A Pearsonian coefficient of correlation between the ranks of two variables, say, x and y, is called rank correlation coefficient between that group of variables.
For a free consultation on correlation or dissertation statistics, click here.
Bivariate Correlation is the one that shows an association between two variables. Correlation is the one that shows the association between two variables while keeping control or adjusting the effect of one or more additional variables.
A Correlation is a degree of measure, which means that a Correlation can be negative, positive, or perfect. A positive Correlation is a type of Correlation in which an increase changes the other variable. In other words, if there is an increase (or decrease) in one variable, then there is a simultaneous increase (decrease) in the other variable. A negative Correlation is a type of Correlation where if there is a decrease (or increase) in one variable, then there is a simultaneous increase (or decrease) in the other variables.
A perfect Correlation is that type of Correlation where a change in one variable affects an equivalent change in the other variable.
A British biometrician named Karl Pearson developed a formula to measure the degree of the Correlation, called the Correlation Coefficient. This Correlation Coefficient is generally depicted as ‘r.’ In mathematical language, the Correlation Coefficient, which was developed by the biometrician Karl Pearson, is defined as the ratio between the covariance of the two variables and the product of the square root of their individual variances. The range of the Correlation Coefficient generally lies between -1 to +1. If the value of the Correlation Coefficient is ‘+1,’ then the variable is said to be positively correlated. If, on the other hand, the value of the Correlation Coefficient is ‘-1,’ then the variable is said to be negatively correlated.
The value of the Correlation Coefficient does not depend upon the change in origin and the change in the scale.
If the value of the Correlation Coefficient is zero, then the variables are said to be uncorrelated. Thus, the variables would be regarded as independent. If there is no Correlation in the variables, then the change in one variable will not affect the change in the other variable at all, and therefore the variables will be independent.
However, the researcher should note that the two independent variables are not in any Correlation if the covariance of the variables is zero. This, however, is not true in the opposite case. This means that if the covariance of the two variables is zero, then it does not necessarily mean that the two variables are independent.
There are certain assumptions that come along with the Correlation Coefficient. The following are the assumptions for the Correlation Coefficient:
The Correlation Coefficient assumes that the variables under study should be linearly correlated.
Correlation coefficient assumes that a cause and effect relationship exists between different forces operating on the items of the two variable series. Such forces assumed by the correlation coefficient must be common to both series.
For the cases where operating forces are entirely independent, then the value of the correlation coefficient must be zero. If the value of the correlation coefficient is not zero, then in such cases, correlation is often termed as chance correlation or spurious correlation. For example, the correlation between the income of a person and the height of a person is a case of spurious correlation. Another example of spurious correlation is the correlation between the size of the shoe and the intelligence of a certain group of people.
A Pearsonian coefficient of correlation between the ranks of two variables, say, x and y, is called rank correlation coefficient between that group of variables.
Conjoint Analysis
Conjoint Analysis is considered a type of analyses that is used in the field of market research. Conjoint Analysis is used by the researcher to obtain the statistically significant importance that a consumer in the market gives to the crucial characteristics of a particular product. Conjoint Analysis is also used to obtain the utilities that a consumer in the market attaches to the various levels of the attributes of a particular product.
For a free consultation on conjoint analysis, click here.
All of this is determined in Conjoint Analysis with the help of an assessment done on the consumer’s preference towards a particular set of characteristics of a brand or a brand profile.
The researcher working on Conjoint Analysis constructs stimuli that consist of a questionnaire. This questionnaire consists of certain attribute levels of a particular brand under study. This stimulus in Conjoint Analysis is filled-out by the respondents participating in the study.
In order to obtain a valid inference about the study with the help of Conjoint Analysis, it is crucial that the respondents participating in the study respond to the stimulus in an appropriate manner. The respondents in Conjoint Analysis should address the questions of the stimuli according to their desirability.
These evaluations carried out in Conjoint Analysis are reliable only if the subjective evaluations of the respondent are true.
Conjoint Analysis, therefore, addresses various issues. The utilization of Conjoint Analysis is done in order to determine the comparative importance of the crucial characteristics that affect the choice of the consumer. Conjoint Analysis is used in estimating the share of market brands that fluctuates by the level of attributes.
Thus, in a similar manner, Conjoint Analysis can be used by the researcher to assess the consumer’s preference over the attributes of consumer goods, industrial goods, etc. The process of Conjoint Analysis is useful in cases where one needs to address certain issues instead of carrying out the concept of testing. Conjoint Analysis is useful for a person who is not so well versed with statistical skills.
The model that is used by the researcher in Conjoint Analysis to fit the data obtained is the utility function model. This model in Conjoint Analysis is a mathematical model that is used by the researcher to establish a fundamental relationship between the attributes and the utility attached to the product under study.
In Conjoint Analysis, the dependent or the predicted variable is generally the variable that is labeled as the preferences that make the customers attached to a particular brand.
In order to assess the reliability or the validity of Conjoint Analysis, there are several procedures that have been developed.
In Conjoint Analysis, a reliability test called the test retest reliability test, is used by the researcher to obtain identical judgments that are sometimes present in the process of data collection. If the Conjoint Analysis is carried out in a collective manner, then the estimated sample is split into several samples. Then, on each of the split sub samples, Conjoint Analysis is carried out in order to assure whether or not the Conjoint Analysis is valid.
The steps involved while conducting conjoint analysis are the following:
For a free consultation on conjoint analysis, click here.
All of this is determined in Conjoint Analysis with the help of an assessment done on the consumer’s preference towards a particular set of characteristics of a brand or a brand profile.
The researcher working on Conjoint Analysis constructs stimuli that consist of a questionnaire. This questionnaire consists of certain attribute levels of a particular brand under study. This stimulus in Conjoint Analysis is filled-out by the respondents participating in the study.
In order to obtain a valid inference about the study with the help of Conjoint Analysis, it is crucial that the respondents participating in the study respond to the stimulus in an appropriate manner. The respondents in Conjoint Analysis should address the questions of the stimuli according to their desirability.
These evaluations carried out in Conjoint Analysis are reliable only if the subjective evaluations of the respondent are true.
Conjoint Analysis, therefore, addresses various issues. The utilization of Conjoint Analysis is done in order to determine the comparative importance of the crucial characteristics that affect the choice of the consumer. Conjoint Analysis is used in estimating the share of market brands that fluctuates by the level of attributes.
Thus, in a similar manner, Conjoint Analysis can be used by the researcher to assess the consumer’s preference over the attributes of consumer goods, industrial goods, etc. The process of Conjoint Analysis is useful in cases where one needs to address certain issues instead of carrying out the concept of testing. Conjoint Analysis is useful for a person who is not so well versed with statistical skills.
The model that is used by the researcher in Conjoint Analysis to fit the data obtained is the utility function model. This model in Conjoint Analysis is a mathematical model that is used by the researcher to establish a fundamental relationship between the attributes and the utility attached to the product under study.
In Conjoint Analysis, the dependent or the predicted variable is generally the variable that is labeled as the preferences that make the customers attached to a particular brand.
In order to assess the reliability or the validity of Conjoint Analysis, there are several procedures that have been developed.
In Conjoint Analysis, a reliability test called the test retest reliability test, is used by the researcher to obtain identical judgments that are sometimes present in the process of data collection. If the Conjoint Analysis is carried out in a collective manner, then the estimated sample is split into several samples. Then, on each of the split sub samples, Conjoint Analysis is carried out in order to assure whether or not the Conjoint Analysis is valid.
The steps involved while conducting conjoint analysis are the following:
- The first step in conjoint analysis is to form a problem.
- The next step in conjoint analysis is to construct stimuli.
- The third step in conjoint analysis is to choose the form of input data.
- The fourth step of conjoint analysis consists of the selection of the conjoint analysis procedure.
- The fifth step is to infer the results from conjoint analysis.
- The last step is to assess the reliability and validity of conjoint analysis.
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