Research designs can be broadly classified into two categories, namely quasi experimental and experimental.
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The research designs are said to be quasi experimental only if the subjects are randomly assigned to the groups and the statistical controls are used by the researcher.
Non equivalent control group research designs are one of the quasi experimental designs. Cook and Campbell (1979) gave some non equivalent control group designs.
One group posttest only research designs are also sometimes called one shot case studies. This is one of the non equivalent control group research designs. This type lacks a pretest baseline, thus, it results in making invalid conclusions.
Posttest only research designs with non equivalent comparison group designs are a kind of social science research design. In this kind it is quite impossible for the researcher to draw valid conclusions about the treatment effects which are entirely based on posttest information.
Posttest only research designs that predict higher order interactions are used in the cases when the expectation of the treatment effect interrelates with the third variable. However, these types are confined to the possible challenges of validity due to certain factors.
One group pretest-posttest research designs are common but defective in social science. These research designs are also known as proxy pretest-posttest designs.
Two group pretest-posttest research designs using an untreated control group are a kind of classic experimental design.
Double pretest research designs help in strengthening the pretest and posttest designs. This kind can be established only if there exists a particular trend in the data that is independent of the treatment effect and is measured by pretest.
Interrupted time series research designs are one of the quasi experimental designs. Cook and Campbell (1979) list certain time series designs.
Simple interrupted time series research designs are the expansion of one group pretest-posttest designs into multiple pretests and posttests. These one group pretest-posttest research designs do not have the control group and therefore make it difficult for the researcher to assess other confounding factors.
Interrupted time series research designs with removed treatments are powerful. These are powerful because in these designs, the threat of certain unwanted factors are removed.
Interrupted time series research designs with multiple replications are simply an interrupted time series design with removed treatments, except that the treatment and the removal in these occurs multiple times.
Interrupted time series research designs with switching replications require a much higher level of control over the subjects. But is stronger in ruling out the threats of invalid conclusions.
Interrupted time series research designs with non equivalent dependent variables have the goal of obtaining the dependent variables that are related to the dependents being studied. In these designs, the related variables are not assumed to be correlated with the treatment variables.
A non experimental research design is not a kind of quasi experimental design because these types do not use statistical controls.
The research designs are said to be non experimental only if there exists a systematic collection of the data with respect to interest of study that are not considered experimental (as there are no control groups or randomization of the subjects).
Some qualitative approaches are applied typically to such research designs. These include approaches like case study designs, content analysis, participant observation, etc.
A hypothesis is a proposed explanation for a phenomenon, based on observation, reasoning, or scientific theory, awaiting verification or falsification through experimentation and data analysis. It serves as a starting point for investigation, guiding the research process by suggesting what outcomes to expect. In the realm of statistics and scientific research, hypotheses are crucial for designing experiments, analyzing results, and advancing knowledge.
You must properly fragment the null hypothesis and alternative hypothesis before the data collection and interpretation phase in the research. Well fragmented hypotheses indicate that the researcher has adequate knowledge in that particular area and is thus able to take the investigation further because they can use a much more systematic system. It gives direction to the researcher on his/her collection and interpretation of data.
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The null hypothesis and alternative hypothesis are useful only if they state the expected relationship between the variables or if they are consistent with the existing body of knowledge. You should express them as simply and concisely as possible. They are useful if they have explanatory power.
The purpose and importance of the null hypothesis and alternative hypothesis are that they provide an approximate description of the phenomena. It provides the researcher with a relational statement that they can directly test in a study. The purpose is to provide the framework for reporting the inferences of the study. It also behaves as a working instrument of the theory. The purpose is to test whether the hypothesis supports the research, independent of the investigator’s values and decisions. They also provide direction to the research.
Researchers generally denote the null hypothesis as H0. It states the exact opposite of what an investigator or an experimenter predicts or expects. It basically defines the statement which states that there is no exact or actual relationship between the variables.
Researchers generally denote the alternative hypothesis as H1. It makes a statement that suggests or advises a potential result or an outcome that an investigator or the researcher may expect. It has been categorized into two categories: directional alternative hypothesis and non directional alternative hypothesis.
The directional hypothesis is a kind that explains the direction of the expected findings. Sometimes this type of alternative hypothesis is developed to examine the relationship among the variables rather than a comparison between the groups.
The non directional hypothesis is a kind that has no definite direction of the expected findings being specified.
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LISREL stands for linear structural relation. The methodology of LISREL was first developed by Karl Joreskog in 1970. LISREL is statistical software that is used for structural regression modeling. Structural equation models are the system of linear equations. LISREL is the simultaneous estimation of the structural model and measurement model. Structural model assumes that all variables are measured without error. Factor analysis is the technique that deals with the measurement model. Factor analysis is of two types: one is the exploratory factor analysis, (where the computer determines the underlining factor) and the second type of factor analysis is confirmatory factor analysis (where the researcher determines the factor structure). LISREL makes it possible to combine the structural equation and factor analysis, and it can also generate path diagrams for structural equations. LISREL 8.8 is the latest version available. It is not only used for structural equation modeling, but it also has several other program applications, such as the PRELIS (Lisrel pre-processor) option is used for data manipulation and basic statistics.
In LISREL, the SURVEYGLIM option is used for generalized linear modeling. For categorical response variables, formative interface modeling is used in LIRSEL. For continuous response variables, the COMFIRM option is used. For multivariate data, the MAPGLIM option is used for generalized linear modeling. In business, psychology and medical research, most researchers use LISREL for structural equation modeling. It was the first software that was used for structural equation modeling. Competing software include AMOS, SAS, and EQS, etc. However, LISREL has its own importance due to unique features.
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The following are some basic features of LISREL:
Starting of LISREL: Select “LISREL” from the start menu or create a shortcut and start from the short cut.
Importing data in to LISREL: To enter data into LISREL, select “import options” from the file menu.
Opening a new window: In LISREL file, the “new “option is used to open a new window. From the new option we can open syntax, output, path diagram or data window as required.
Data manipulation: In the “data” option of LISREL, there are options like the variable properties, select variable, sort case, insert variable, delete variable, assign weight, etc.
Transform option: Like SPSS, LISREL also has an option to record or compute a new variable by using the “transform” option.
Statistics option: In LISREL, by using the statistics option, we can perform all the statistical models. LISREL can handle a number of models that include measurement models, no recursive models, hierarchical linear models, confirmatory factor analysis models, ordinal regression models, multiple group comparisons model, etc.
Graph option: Like many other statistical software, LISREL also has the option for graphs. By using the “graph” option in LISREL, we can produce high quality univariate, bivariate and multivariate charts.
Advance modeling: In LISREL, the multilevel option provides the flexibility to perform advance level modeling. By using the multilevel option, we can perform advance level linear and non-linear statistical methods.
View and Window option: Like any other statistical software, LISREL also has the view and window option. View option has the basic features like the tool bar, status bar, etc. By using the window option, we can arrange the window in a horizontal or vertical manner.
Advantages:
1. This software provides the full information about the model coefficient which increases the power of the model.
2. It provides good treatment to the missing value.
3. It provides significance testing for all the coefficients.
4. It imposes restrictions on models if that is what is wanted.
Drawbacks:
1. It is complicated to handle when someone is a novice.
2. The interaction effects are hard to handle.
3. Correlation matrix is used in SEM and it is assumed that these correlations are derived from the multivariate normality distribution. This assumption does not look valid.
The Kolmogorov Smrinov’s one sample test is a test for goodness of fit. The Kolmogorov Smrinov’s one sample test is concerned with the degree of agreement between the distribution of the observed sample values and some specified theoretical distribution. It determines whether or not the values in a sample can reasonably be thought to have come from a population having a theoretical distribution.
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In Kolmogorov Smrinov’s one sample test, it is assumed that the distribution of the underlying variables being tested is continuous in nature. It is appropriate for those types of variables that are tested at least on an ordinal scale. One usually conducts the test in order to test the normality assumption in analysis of variances.
Suppose, for example, F0(x) has a completely specified cumulative relative frequency distribution function in Kolmogorov Smrinov’s one sample test. In this case the theoretical distribution under the null hypothesis for any value of F0(x) is the proportion of the cases that are expected to have values which are equal to or are less than the value of x.
Suppose Sn(x) is the observed cumulative relative frequency distribution function of a random sample of ‘n’ observations in Kolmogorov Smrinov’s one sample test. If xi is any possible value then Sn(xi) = Fi/n , where Fi is nothing but the number of expected proportions of observations which are less than or equal to xi.
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Now, according to the null hypothesis in Kolmogorov Smrinov’s one sample test, it is expected that for every value of xi, Sn(xi) should be fairly close to F0(xi). In other words, if the null hypothesis is true, then the difference between Sn(xi) and F0(xi) is small and should be within the limits of the random error.
The Kolmogorov Smrinov’s one sample test focuses on the largest of the deviations. The largest deviation is called the maximum deviation. The maximum deviation is the largest absolute difference between the cumulative observed proportion and the cumulative proportion expected on the basis of the hypothesized distribution. The sampling distribution of the maximum deviation under the null hypothesis is generally known.
There are certain assumptions that are made in Kolmogorov Smrinov’s one sample test.
It is assumed that the sample is drawn from the population by the process of random sampling.
It is assumed that the level of data variables should be continuous interval or ratio types in order to get the exact results. If approximate results are required by the researcher through Kolmogorov Smrinov’s one sample test, then the researcher can use ordinal data or grouped interval level of data.
Kolmogorov Smrinov’s one sample test is also used for ordinal scale of data when the large-sample assumptions of the chi-square goodness-of-fit test are not met.
The hypothetical distribution is specified in advance in Kolmogorov Smrinov’s one sample test.
In the case of the normal distribution the expected sample mean and sample standard deviation should always be specified in advance.
In the case of Poisson distribution and in the case of exponential distribution in Kolmogorov Smrinov’s one sample test, the expected sample mean should always be specified in advance.
In the case of uniform distribution in Kolmogorov Smrinov’s one sample test, the expected range which consists of the minimum and maximum values, should always be specified in advance.
Descriptive measure can be defined as the kind of measure dealing with the quantitative data in a mass that exhibits certain general characteristics. The descriptive measure has different types, all depending on the different characteristics of the data.
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First, the descriptive measure of deviation or dispersion is a measurement to the extent to which an individual item can vary. Professor Yule has laid out certain properties that the descriptive measure of deviation of the data should satisfy.
For one, the descriptive measure of deviation needs to be rigidly defined. Additionally, it should be easy to understand and it should also be flexible in calculation. This descriptive measure should also be based on every observation. Further, it should be open to any further mathematical treatment. And finally, it should not be affected by fluctuations in the sampling.
Whenever a researcher wants to make a comparison in the variability of the two series which differs widely in their averages, then the researcher calculates the coefficient of dispersion based on different types of descriptive measures of deviation or dispersion. There are four coefficients of dispersion based on different descriptive measures of dispersion or deviation: range, quartile deviation, mean deviation, and standard deviation.
The coefficient of variation is a hundred times the coefficient of dispersion that is based on the descriptive measure of dispersion which is standard deviation.
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The data in a frequency distribution may fall into symmetrical or asymmetrical patterns and this measure of the direction and degree of asymmetry is called the descriptive measure of skewness. This refers to lack of symmetry. The researcher studies the descriptive measure of skewness in order to have knowledge about the shape and size of the curve through which the researcher can draw an inference about the given distribution.
A distribution is said to follow the descriptive measure of skewness if mean, mode, and median fall at different points. This type will also follow in the case when quartiles are not equidistant from the median and also in the case when the curve drawn from the given data is not symmetrical.
There are three descriptive measure of skewness.
The first type of descriptive measure of skewness is M- Md, where Md is the median of the distribution.
The second type of descriptive measure of skewness is M-M0, where M0 is the mode of the distribution.
The third type of descriptive measure of skewness is (Q3- Md)-( Md – Q1).
These are also types of absolute descriptive measures of skewness.
The researcher calculates the relative measure for the descriptive measure called the coefficients of skewness which are the pure numbers of independent units of the measurements.
Karl Pearson’s coefficient of skewness for the descriptive measure of skewness is the first type of coefficient of skewness that is based on mean, median and mode. This coefficient for the descriptive measure of skewness is positive if the value of the mean is more than the value of mode. Or, the median and the coefficient for the descriptive measure of skewness is negative if the value of mode or median is more than the mean.
Bowley’s coefficient of skewness for the descriptive measure of skewness is the second type of coefficient of skewness that is based on the quartiles. This type of coefficient of skewness for the descriptive measure of skewness is used in those cases where the mode is ill defined and the extreme values are present in the observation. It is also used in cases where the distribution has open end classes or unequal intervals.
During a study, researchers often have questions that they must answer. These questions include questions like ‘are the groups different?’, ‘on what variables, are the groups most different?’, ‘can one predict which group a person belongs to using such variables?’ etc. In answering such questions, discriminant analysis is quite helpful.
Researchers use discriminant analysis to analyze research data when the dependent variable is categorical and the independent variable is interval in nature. The term categorical variable means that researchers divide the dependent variable into several categories. For example, three brands of computers, Computer A, Computer B and Computer C can be the categorical dependent variable.
Discriminant analysis aims to create functions that use independent variables to distinguish between categories of the dependent variable. It enables the researcher to examine whether significant differences exist among the groups, in terms of the predictor variables. It also evaluates the accuracy of the classification.
Discriminant analysis describes the number of categories that the dependent variable possesses.
In statistics, we assume everything extends to infinity. So, when the dependent variable has two categories, we use two-group discriminant analysis. If the dependent variable has three or more than three categories, then the type used is multiple discriminant analysis. The major distinction to the types of discriminant analysis is that for a two group, it is possible to derive only one discriminant function. In multiple discriminant analysis, you can compute more than one discriminant function. For a researcher, it is important to understand the relationship of discriminant analysis with Regression and Analysis of Variance (ANOVA) which has many similarities and differences.
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Often we can find similarities and differences with the people we come across. Similarly, there are some similarities and differences with discriminant analysis along with two other procedures. The similarity is that the number of dependent variables is one in discriminant analysis and in the other two procedures, the number of independent variables are multiple in discriminant analysis. The difference is categorical or binary in discriminant analysis, but metric in the other two procedures. The nature of the independent variables is categorical in Analysis of Variance (ANOVA), but metric in regression and discriminant analysis.
The steps involved in conducting discriminant analysis are as follows:
1. Researchers formulate the problem before conducting the analysis.
2. They estimate the discriminant function coefficients.
A simple search on the internet will yield thousands of entries about APA citation. This is because there are, indeed, hundreds and thousands of sites that all tout valuable information about APA citation. The problem with most of these sites is that the APA citation help they offer only gives “quick-fix” solutions.
APA stands for American Psychological Association and it is a way of formatting everything in a dissertation or science related paper. APA citation is not necessarily hard, but it has a numerous amount of rules that govern how everything should be done within a paper. This includes the way that charts and graphs are formatted, the way that the table of contents is laid out, the documentation of statistics, the citing of sources, and so on. APA citation, then, must be carefully followed if a dissertation is going to be approved and accepted.
One of the most difficult parts of APA citation is simply having the time, energy, and patience to research everything that is needed in the dissertation if it is to have proper formatting. Most students, however, worry about APA citation at the conclusion of their dissertation. At this point, it is very difficult for that student to have the tolerance necessary to go back over every aspect of the dissertation and record and format it properly. Further, this is when the student goes on-line and must navigate through hundreds and thousands of APA citation web-sites that he/she hopes will give him/her the exact formatting for his/her exact problem and dissertation. It is certainly easy for a student to be in this predicament, for who wants to worry about the inconvenient hassles of what seems to be “nit-picky” details that do not actually affect the data or research or conclusions?
This is exactly where our consultants come in. They have immense knowledge of APA citation and can quickly convert every aspect of the dissertation into proper citation. There is no shame in seeking help as APA citation is not often taught to most students who. If it is taught, it is simply explained in its most basic understanding, and the rest is left to the student to research and figure out. With there being so many rules governing APA citation, and because students are strapped for time as it is, there is little wonder as to why students seek help.
A good analogy for APA citation help would be to compare it to an editor. All authors have editors, and these editors “clean-up” whatever it is the author writes. The editor then sends it back to the author who agrees with the changes. APA citation help also “cleans-up” what the student has written, calculated and observed and it standardizes all of the results as every dissertation is written with APA citation.
Consultants can do more than simple APA citation help. Good dissertation consultants can guide students every single step of the way—offering them help and guidance for every single part of their dissertation. It is up to the student, however, to decide what kind of help they are looking for. If a student is simply looking for help on APA citation, that is readily available by consultants who specialize in APA citation. Additionally, they have up-to-date information about all of the current changes to APA citation. If a student needs more help than simple APA citation help, that too is available. In other words, whatever help the student seeks, be it APA citation help in the closing stages of their dissertation or dissertation guidance from the very beginning, dissertation consultants can ensure that the student seeking his/her doctoral degree is successful.
What is dissertation research?
Dissertation research is the culminating project that brings together all of your learning through your doctorate.
What does dissertation research entail?
The dissertation is a five chapter research project that begins with Chapter 1, the introduction to the study. The introduction sets the stage for the rest of the research. Included in the introduction is the background of the study, problem statement, purpose of the study, research question(s) and hypotheses, theoretical foundation, conceptual framework, nature of the study, definitions, assumptions, scope and delimitations, limitations, and significance of the study, significance to theory, practice, and significance to social change. The chapter ends with a summary and transition.
Next is Chapter 2, the literature review. The literature review explains the literature search strategy, literature review summary, and conclusions. Here you are showing what’s been researched on your topic.
After the argument is built for why the study needs to be conducted, you’re off to Chapter 3, the research methodology. This chapter needs to discuss the research design and rationale, population, sampling and sampling procedures, recruitment, participation, and data collection, the intervention, instrumentation and operationalization of constructs, intervention or manipulation of an independent variable, data analysis plan, threats to validity (external, internal, construct), ethical procedures, and a summary. Think about the methodology as a cookbook that tells the reader exactly how you will conduct the study.
After data is collected, the data needs to be analyzed in the chapter 4 results. These results should involve the thematizing of qualitative data or statistical analysis of quantitative data, and a summary of these results.
The dissertation final chapter is 5, the discussion chapter. Discussion chapter includes conclusions, and recommendations, interpretation of findings, limitations of the study, and implications.
Can I get help on my research?
Fortunately, you can get editing help with your original dissertation research. Scholarly writing is typically a new skill set for doctoral students, so support gets you over the writing challenges often confronted by students. A dissertation research consultant can edit each of the five chapters, explain why the edits were made, and support you to effectively address committee feedback.
Is a dissertation research consultant expensive?
No, it is not expensive to hire a dissertation consultant relative to the opportunity cost of sitting in the dissertation process for an additional 6 months or a year. Typically research consultants range from an hourly rate to thousands of dollars for the usual 60-100 hours of editing that is required for committee acceptance.
It makes sense to get the expert support to complete your research. There is no better way to make sure that you perform rigorous, quality dissertation research and complete it in a timely manner than to hire the help of a dissertation research consultant to help you every single step of the way.
Statistics analysis plays a crucial role in any dissertation as it is necessary for a doctoral students. It helps the students to prove his or her thesis. With improper statistics analysis, the dissertation will neither be approved nor accepted.
Though statistics analysis is necessary for all dissertations and for all PhD candidates to complete, many PhD students have a very difficult time completing the statistic analysis properly and accurately. This struggle with the statistical analysis is quite common. The reason for this struggle is because PhD students have not had enough experience with statistics. The statistics guide them to complete the complicated statistical procedures and statistical analysis necessary for the dissertation. Granted, PhD students have spent years and years in school—but they have not spent years and years studying statistics. Statistics is often necessary to complete the statistics analysis with ease and success.
It is obviously very important to complete the statistics analysis properly for all PhD students. However, a student can get help on the statistics analysis that can save the student from frustration. This help on statistics analysis usually comes in the form of a statistical consultant. A statistical consultant is a trained statistician who helps a PhD student with every single statistical process of the dissertation. A statistical consultant guide the student through the statistics methodologies. This guidance ensures that the PhD student is always be on the right track. As a result, the statistics analysis will be accurate and clear. And so, the PhD student will no longer have to struggle with the statistics analysis.
A student can complete the statistics analysis properly with a help of a statistical consultant. A statistical consultant is also very easily attainable and he/she can start at any point in the dissertation process. Obviously, a student should not waste time, struggling through the statistical procedures and the statistics analysis. So, a dissertation writing student should seek help on the statistics analysis and the statistical procedures early on. The sooner a PhD student seeks help from a statistical consultant, the more help can be provided.
It is also incredibly important for the PhD student to realize that a statistical consultant providing help on the statistics analysis will be able to instruct the student. In fact, this is often the most valuable part of getting help on the statistics analysis from a statistical consultant. One on one help with statistics and the statistics analysis can be incredibly useful because the statistical consultant can go at the pace of the PhD student.
Unlike what happens in a room full of students when the PhD student is enrolled in a class, the instruction between a statistical consultant and a PhD student is one on one. And this can mean all the difference between a student struggling to keep up with a room full of students, or a student no longer feeling intimidated by peers around him/her. Thus, the help provided by a statistical consultant is absolutely unmatched as the statistical consultant is able to give individualized attention to the student and the statistical consultant is able to make sure that the PhD student actually understands the statistical procedures and the statistics analysis.
There is no better way to get help on the dissertation and on the statistics analysis than to seek professional help in the form of a statistical consultant. Once the PhD student does get this much needed help, he or she will see results immediately.
If you are a dissertation writing student, you have no-doubt experienced the difficult aspects that go along with writing your dissertation: the frustration when you realize you make a mistake, the unending hours you spend going through your data, the annoyance of having to wait for your advisor to become available so you can ask him or her a question. This, however, does not have to be the case as there are trained professionals who can offer each and every single dissertation writing student help as he or she completes the very lengthy and very difficult stages of the dissertation.
Perhaps the most difficult and the most lengthy stage of the dissertation is the statistical parts of the dissertation. The dissertation must be grounded in statistics as the statistics will actually serve to prove whatever it is that the dissertation writing student has set out to prove. And because the dissertation relies on the statistical parts of the dissertation, the statistics are often the most challenging and difficult parts of the dissertation to complete.
Because the dissertation statistics are indeed so challenging for a dissertation writing student to complete, it is very important for the dissertation writing student to seek help on the statistical parts of the dissertation. And while the student’s advisor is the perfect person to ask for help, the student’ s advisor, as mentioned above, is not always available. The next place most dissertation writing students turn to is the internet. And while the internet can provide useful information, it usually takes hours and hours and hours just wading through all the websites before a doctoral student finds one that he or she can use. Thus, the best solution in terms of getting help on the statistical parts of the dissertation is to seek the professional help of statistics consultants.
Statistics consultants can help any dissertation writing student and statistics consultants can guarantee success on the dissertation statistics. Statistics consultants, as the name suggests, are trained professionals when it comes to the statistical procedures that are contained within a dissertation. Statistical consultants can therefore ‘jump in’ at any time and statistical consultants can make the difference between a student doing the statistical procedures correctly, and a student struggling and making mistake after mistake—only delaying his or her completion of the dissertation.
Statistics consultants, then, can provide the help a doctoral student needs and statistics consultants can ensure that every single statistical procedure is done correctly the first time. Statistics consultants can start from the very beginning of a dissertation as statistics consultants can help a student choose a topic, or phrase that topic so that it makes sense statistically. Statistics consultants can then help the student do the dissertation research and statistics consultants can help the student with the proposal for the dissertation. Next, statistics consultants will help the student as he or she follows the rules of statistics (like sample size justification—something that statistics consultants are well versed in) to gather the data for the dissertation. Statistics consultants will then make sense of that data and statistics consultants will help the student as he or she applies those statistics to the dissertation. Finally, statistics consultants can even proofread the dissertation for the student and this will ensure that the student gets his or her dissertation approved and accepted the very first time around. Statistics consultants, then, can help with every single aspect of the dissertation and statistics consultants can guarantee that the student finishes both on time and with much success. There is therefore no substitute to what statistics consultants can do for a doctoral degree seeking student.