The post Sampling Terminologies appeared first on Reading Craze.
]]>In an ideal situation the whole population should be taken to study any variable that you want to study. It is unfortunately impossible to study the whole population. In most of the cases population is large and cannot be studied as a whole. A sample is a part of the population that is drawn from the population. It makes the collection of data and its analysis easy and feasible. Sample size is denoted by lowercase letter “n”.
Population is the total entity of people upon which you want to generalize your research. In research population size is denoted by uppercase letter “N”.
Target population is different from the actual population in one way. Target population can be very large and the researcher might select a small group of that population or people living in one area to make the research easy. For example, the researcher wants to study the effect of one beauty product on elderly ladies. The target population in this case will be all ladies that use that product. This can be very large group spread over several states or even countries. He decides to select a population that is accessible or lives in his region. This will be called as sampling population or population. Hence target population can be very large as compared to the population that the researcher selects for generalizing his findings.
Sampling framework is the entire population of people, situation, incidents or households from which the researcher has to take the sample. There might be several sampling framework but it is not always possible to draw samples from them. Some frameworks are difficult to research because of social, moral or ethical issues. Sampling framework is that population from which you can draw sample feasibly.
Sampling design is a technique or a procedure to select samples from the target population. The sampling design ensures that each element in the population has an equal and unbiased chances of becoming part of the sample. Sampling design can be nonrandom and, in this case, some compromises are made to get the desired sample.
Standard error is the standard deviation of the sampling distribution. Standard error thus describes the chances of error that may occur in the statistic calculated from the sample. As sample could just estimate the characteristics of the population, hence standard error describes the possibility of error in the sampling distribution.
Standard deviation determines variability of the actual population from which the sample has been drawn. It is denoted by Greek letter sigma and often written as sd. Standard deviation is also the square root of the variance of the population. In statistical terms it can be defined as the deviation of the data points on each side from the mean.
Confidence interval and confidence level are the two terms that are constantly used in sampling and in analyzing the data. Confidence level is the degree or percentage of confidence that the researcher has on the estimates that he has drawn from the sample. It means that higher the percentage of confidence level, greater will be its reliability.
In statistics mean is the most common type of average that is used. It is used in sampling to describe the characteristics of sampling distribution.
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]]>The post Difference between Quantitative and Qualitative Research appeared first on Reading Craze.
]]>This is an important question for every researcher, as a researcher you should know the basic difference between the two. There is a clear and well defined divide between these two types of researches that one can easily understand. In quantitative research the type of information sought, type of data, variables, study design and analysis of data will be completely different from that in qualitaitve research. This means that quantitative research is different from qualitaitve research because both research studies uses different methodology and typology and hence it is a broad classification of research. Between these two methods of research there is another research method and hence another calssification of research called as mixed-method research. Mixed-method research is comparatively latest method of conducting research. Mixed-method research uses both qualitative and quantitative data, variables and analysis to undertake the research. This method is gaining popularity because it is more research-friendly and has greater possibilities. In depth information is obtained using mixed method research.
Another clear difference between the two types of research is the paradigm that form the basis of the research. Quantitiave research follows positivism while qualitiative research follows the paradigm of constructivism. Positivist researchers believe that the answer to the research question lies in logical, mathematical or statistical treatment of data. Antipositivists or constructivism based researchers believe that in depth information can only be obtained by studying the behaviors of individuals and groups. Both paradigms have importance in research and in different fields of life the reserarchers use one or the other paradigm and hence the method of research. In a mixed-method research the researcher uses both paradigms and some researchers working on qualitiative research uses positivist paradigm. However, there is a clear difference between the two types of research qualitiative research and quantitative research. No matter what paradigm the researcher works within but within that paradigm the researcher should keep to certain values of research like objectivity and validity.
Quantitative research is different from qualitiative research in three regards which are as follows.
Every research study has an aim, a purpose and some goals that the researcher wants to achieve, from this view point the research can be classified as quantitative and qualitative. In research the researcher studies a situation, phenomenon, problem or issue and if the researcher aims at studying a quatifyible situation the research is classified as quantitative research. Quantitative research quantifies data, variables and uses statistical analysis to reach at the conslusion. Some times the purpose or aim of the research is to describe a situation, phenomenon or problem and such research is qualitaitve in nature. The researcher investigates an in depth information and describes the situation. The purpose of the research is to describe, enumerate or explore and not to explain. Most of the exploratory studies that researchers conduct before starting the actual research are qualitative in nature.
The way you measure the variables in a study highly determines whether the study is qualitative or quantitative in nature. Quantitative research is different form qualitaitve research in the measurement of the variables. Both studies use different scales of measurement in the research. Qualitiative measurement scales include nominal and ordinal scales and the purpose of these scales is to measure mostly the attitudes and behaviors of people. Qualitaitve variables can have one, two or three values or categories. In a quantitative research the variables are measured on interval or ratio scales. In quantitative research the variables can have more than one, two or three values and the variable can take any form. For example age of the population can be 1 year, 2 years, 3 years and so on and similarly number of years in a service can be 1, 2, 3 or so on. There is a distinctive divide between the qualitative variables and their measurement scales scales.
In quantitative research the analysis of the data is done using the statistical tools. Stastistics is not an integral part of quantitative research but it is a tool that helps the researcher reach the analysis. In quantitative research there is a clear null hypothesis and the researcher clearly accepts or rejects the null hypothesis. The use of statistical analysis in quantitaive research helps the researcher put confidence in his research findings. The use of statistics also adds to the validity and generalizability of the research so statistics is an important tool in quantitative research. In qualitative research the researcher can or cannot use statistical analysis. In some qualitative researches the researcher converts the qualitative variables into measureable variables by using measurement scales and hence he can use statistical analysis. Some time the information obtained in qualitative research is purely descriptive in nature like in historical and ethnographic research, you cannot apply statistical tools to a descriptive research neither you need to apply statistical tools to such research.
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]]>The post How to analyze data in research appeared first on Reading Craze.
]]>Research analysis is one of the main steps of the research process, it is by far the most important steps of the research. How to analyze the data is an important question that every researcher asks. The researcher collects the data using one of the qualitative or quantitative methods of data collection. Data analysis highly depends on whether the data is a qualitative data or a quantitative data.
Data analysis is the process of scanning, examining and interpreting data available in tabulated form. The purpose of data analysis is to understand the nature of the data and reach a conclusion. Data analysis actually provides answers to the research questions or research problems that you have formulated. Without data analysis you cannot draw any conclusion. Data organization alone cannot help you in drawing conclusions but data analysis helps you in this regard. After analyzing data you get an organized and well examined form of data that can help you know whether your hypothesis got accepted or rejected.
There is not a single hard and fast rule for data analysis but you need to look at your data and decide on the method of data analysis. There are some basic tips you need to follow to analyze data in research papers and dissertations.
Organize your data before scanning, examining or interpreting it. Data organization is necessary because you cannot analyze haphazard data. You can arrange and organize data in tables or groups. This is easier to do if your data is quantitative on the other hand qualitative data is difficult to tabulate. You can first arrange your data in groups or categories and under each category you can tabulate the data. For qualitative data you have to follow different methods of data organization. Well organized data lends itself easily to analysis.
Now look at the tabulated data and make graphs to show the data in more clear form. Plotting graphs is necessary because it helps you in looking at the extreme points as well as the average points. You can use any one of the methods of graphs. You can use a statistical software to make graphs. Otherwise if you are good in statistics you can make the graphs yourself. Graphs will make the data more presentable and easy to comprehend.
In the next step explain the data that is present in both tabulated and graphical forms. This explanation will help you draw main conclusions. Explore the graphs and tables and find out how you can write down the interpretation of your research study. You can correlate the variables and you can also explain the results. Try to make the interpretation specific and to the point. Extremely lengthy explanations are unnecessary in most cases, on the other hand a specific interpretation of the data is easy to understand.
In the last stage the hypothesis is rejected or accepted in the light of your interpretations. You have to confirm that your hypothesis proved right or it proved wrong. You can use any one of the statistical methods for confirmation of the hypothesis. Generally you can use ANOVA , t-test, z-test or chi square to test the hypothesis. There are also software that can help you in this regard. You can also get help of a statistician to apply statistical methods to your research. Statistical application is important because it makes your research valid and generalizable.
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