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]]>Displaying data through a table is the most preferred method in research writing. Tables are important because they make understanding of data easier. The lengthy amount of data that you might have received can become concise and precise with tables and figures. The tables can easily be created and formatted in Microsoft Word or any other software that you are using. Once you will be done with creating all the tables for the data you have received, you will see that your research is simplified and looks meaningful. While presenting data you will only need tables, figures, and diagrams to present your idea to the listeners. While displaying data in the form of tables you should make sure that the tables have a significance for your research. In some descriptive research, there is no need to display data in the form of tables, in descriptive research, you can simply state the data descriptively rather than displaying it in the form of tables.
Each table has a title and this title needs to be concise but precise, the writer should give a numeric identity to the table as well. Titles need to explain clearly what the reader is going to see in the table. In your research, every table should have a unique title and the tables are listed with these titles. The list of all the tables should be provided at the beginning of the research report alphabetically. If there are several chapters in the research, which mostly are, the chapter number should precede the table number to make it easy to find later on.
The body consists of the cells (columns and rows) that contain the main analyzed data. As tables are two-dimensional so to understand each point in the table body one needs to review it in light of both heading under which it falls vertically and horizontally.
There are two types of headings in the table, the headings along the y-axis and the headings along the x-axis. The headings along the y-axis describe one variable about which the information is presented in the body of the table. The headings along the x-axis represent another variable about which also the information or data is represented in the body.
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]]>The post Questionnaire Design Goal appeared first on Reading Craze.
]]>1. Respondents should feel at ease
2. Facilitate data processing
3. Wording of the questionnaire
References
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]]>The post Displaying Data in Research Methodology appeared first on Reading Craze.
]]>There are basically two ways to display data: tables and graphs. The tabulated data and the graphical representation both should be used to give more accurate picture of the research. In quantitative research it is very necessary to display data, on the other hand in qualitative data the researcher decides whether there is a need to display data or not. The researcher can use an appropriate software to help tabulate and display the data in the form of graphs. Microsoft excel is one such example, it is a user-friendly program that you can use to help display the data.
The use of tables to display data is very common in research. Tables are very effective in presenting a large amount of data. They organize data very well and makes the data very visible. A badly tabulated data also occurs, in case, you do not have knowledge of tables and tabulating data consult a statistician to do this step effectively.
To know the tables and to tabulate data in tables you should know the parts or structure of the tables. There are five parts of a tables, namely;
The title of the table speaks about the contents of the table. The title should have to be concise and precise, no extra details. The title should be written in sentence case.
The column at the left-most of the table is called as stub. A stub has a stub-heading at the top of the column, not all tables have stub. The stub shows the subcategories that are listed along Y-axis.
The caption is the column heading, the variable might have subcategories which are captioned. These subcategories are provided on the X-axis, the captions are provided on the top of each column.
The body of the table is the actual part of the table in which resides the whole values, results, and analysis.
There can be many different types of notes that you may have to provide at the end of the table. The footnotes are provided just below the table and labeled as the source. The source generally are provided when the table has been taken from some other source. They are also provided for explaining some point in the table. Sometimes there is some part of the table that is taken from a source so it should also be mentioned.
Tables are the most simple means to display data, they can be categorized into the following;
Univariate
Bivariate
Polyvariate
These categories are based on the numbers of variables that need to be tabulated in the table. A univariate table has one variable to be tabulated; a bivariate table, as the name suggests, has two variables to be tabulated and a polyvariate table has more than two variables to be tabulated.
The purpose of displaying data is to make the communications easier. Graphs should be used in displaying data when they can add to the visual beauty of the data. The researcher should decide whether there is a need for table only or he should also present data in the form of a suitable graph.
You can use a suitable graph type depending on the type of data and the variables involved in the data.
The histogram is a graph that is highly used for displaying data. A histogram consists of rectangles that are drawn next to each other on the graph. The rectangles have no space in between them. A histogram can be drawn for a single variable as well as for two or more than two variables. The height of the bars in the histogram represent the frequency of each variable. It can be drawn for both categorical and continuous variables.
The bar chart is similar to a histogram except in that it is drawn only for categorical variables. Since it is used for categorical variables, therefore, it is drawn with space between the rectangles.
A frequency polygon is also very much like a histogram. A frequency polygon consists of frequency rectangles drawn next to each other but the values taken to draw the rectangles is the midpoint of the values. The height of the rectangles describes the frequency of each interval. A line is drawn that touches the midpoints at the highest frequency level on Y-axis and it touches the X-axis on each extreme end.
The cumulative frequency polygon is also a frequency polygon, it is drawn using the cumulative frequencies on the Y-axis. The values on the X-axis are taken by using the endpoints of the interval. The endpoints of the interval are joined to each other the reason being that the cumulative frequency is always based on the upper limit of an interval.
The stem and leaf display is another easy way to display data. The stem and leaf display if rotated to 90 degrees become a histogram.
The pie chart is a very different way to display data. The pie chart is a circle, as a circle has 360 degrees so it is taken in percentage and the whole pie or circle represent the whole population. The pie or circle is divided into slices or sections, each section represents the magnitude of the category or the sub-category.
The trend curve is also called as the line diagram. It is drawn by plotting the midpoints on the X-axis and the frequencies commensurate with each interval on the Y-axis. The trend curve is drawn only for a set of data that has been measured on the continuous, interval or ratio scale. A trend diagram or the line diagram is most suitable for plotting values that show changes over a period of time.
The area chart is a variation of the trend curve. In area chart, the sub-categories of a variable can be displayed. The categories in the chart are displayed by shading them with different colors or patterns. For example, if there are both males and females category in the dataset both can be highlighted in this chart.
A scattergram is a very simple way to plot the data on a chart. The scattergram is used for data where the change in one variable affects the change in the other variable. The frequency against each interval is plotted with the help of dots.
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]]>The post Data Coding in Research Methodology appeared first on Reading Craze.
]]>A code in research methodology is a short word or phrase describing the meaning and context of the whole sentence, phrase or paragraph. The code makes the process of data analysis easier. Numerical quantities can be assigned to codes and thus these quantities can be interpreted. Codes help quantify qualitative data and give meaning to raw data.
Data coding is the process of driving codes from the observed data. In qualitative research the data is either obtained from observations, interviews or from questionnaires. The purpose of data coding is to bring out the essence and meaning of the data that respondents have provided. The data coder extract preliminary codes from the observed data, the preliminary codes are further filtered and refined to obtain more accurate precise and concise codes. Later, in the evaluation of data the researcher assigns values, percentages or other numerical quantities to these codes to draw inferences. It should be kept in mind that the purpose of data coding is not to just to eliminate excessive data but to summarize it meaningfully. The data coder should ascertain that none of the important points of the data have been lost in data coding.
Few examples are mentioned here to understand the data coding in a better manner.
“I prefer to shop from a store that provides a large inventory of the same product, every brand and every style in that product range. Usually in these stores you get maximum range of products you want to purchase. You get profits through deals and sales.”
The data coder can assign different codes to what the respondent narrated above. These codes might be as following;
“Preference for horizontal markets”
“Horizontal integration”
“Shopping preference”
When data coder assigns codes to the observed data, he cannot manage to assign well-refined codes in the first instance. He has to assign some preliminary codes first so that the data has become concise. He later on, further refines the codes to get the final codes. It must be kept in mind that codes are not the final words or phrases on the basis of which evaluation will be made. The researcher will filter the preliminary codes and then the final codes. He needs a pattern on the basis of which he can categorize the human behavior, action or likes and dislikes.
The final codes will help you observe a better pattern in the data. This pattern is necessary to reach the final evaluation or analysis stage of the data. The final codes in data coding mean finding out meaningful words and phrases from the observed data. The respondents often do not choose meaningful words in their responses. The coder needs to extract the meaning out of the respondent’s wording. The codes in their final stage are like topics and themes, these themes generate a whole discussion to get the final results. Sometimes the interviewer or the observer writes down some codes as he observes the behavior of the respondent. Such codes are really worthy in the research because these codes cannot be derived from the written responses that the respondents provide. The data coder should look for the verbs and the actions that the respondent has mentioned in the text. He should also observe the behavior and where ever possible derive codes. One thing should be kept in mind that qualitative data analysis is all about finding out the meanings and interpretations, so the coder should have an eye for such things.
The codes are given meaningful names and they are put in categories. These categories help refine the research a lot. When data is coded again and again, it get refined. The refined data itself leads to patterns and themes. The patterns are the key to find out the true results of the research. These patterns or categories determine where does the large amount of the data inclines.
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]]>The post Data Editing in Research Methodology appeared first on Reading Craze.
]]>With editing the data the researcher makes sure that all responses are now very clear to understand. Bringing clarity is important otherwise the researcher can draw wrong inferences from the data. Sometimes the respondents make some spelling and grammatical mistakes the editor needs to correct them. The respondents might not be able to express their opinion in proper wording. The editor can rephrase the response, but he needs to be very careful in doing so. Any bias can be introduced by taking the wrong meanings of the respondents point of view.
The editor may also need to make some omissions in the responses. By chance or by some mistake some responses are left incomplete, the editor has to see what has been an oversight by the respondent.
It depends on the target population how well you get the questionnaires filled. An educated respondent will fill the questionnaire in a better manner than a person who is not very educated. It also depends on how much interested the respondent is in filling the questionnaire. Sometimes the respondents are very reluctant to fill it out. In case, you think that your respondents are not very much interested, you should take an interview rather than submitting a questionnaire. In the questionnaire, the respondents will leave blank spaces and you might get “noreponse”. On the other hand, in an interview you can better assess what they want to tell and what they are trying to hide.
The editor has a great responsibility to edit the surveyed data or other form of responses. The editor needs to be very objective and should not try to hide or remove any information. He should not add anything in the responses without any sound reason. He should have to be confident in making any changes or corrections in the data. In short, he should make least changes and only logical changes. He should not add anything that shows his opinion on the issue.
Sometimes the respondents leave something incomplete, to complete the sentence or a phrase the editor has to make a judgement. He should have to have good judgement to do so. He should do it so well that his personal bias do not involve in the responses.
Handwriting issues needs also be resolved by the editor. Some people write very fast and in this way they write so that comprehension of the text becomes difficult. In electronically sent questionnaires this problem never arises.
Logical adjustments must be made or otherwise the data will become faulty. There might be need for some logical corrections, for example, a respondent gives these three answers to the three questions that have been asked form him;
#1: What is your age?
Ans: 16 years
#2: What is your academic qualification?
Ans: Bachelors
#3: What academic qualifications you want to achieve in the future?
Ans: Bachelors in fine arts
Looking at the answers he has provided, he could not be 16 years of age and done with bachelors degree. By looking at other answers he has provided you can guess his age. If he is 16 years of age then he could not be done with bachelors and you can guess in which class he will be. In case, it is possible to contact with the respondent you can ask him about these answers. You can make logical changes in these answers because it is clearly evident that 16-year boy or girl could not be in bachelors. He might got confused between the two questions and give wrong response. Such corrections are pretty easy to make but there can be some other responses that are tricky and clearly wrong. The editor must have knowledge how to correct the answers and what to do in such situation.
If some information is least comprehendible and no logical meaning can be taken, interviewees can be re-contacted to know what they meant by that. In case, the data in the questionnaire is not correct and the editor cannot take any meaning from it. The editor should ask the respondents, recontact with them and get their help.
In recent years, most of the researchers prefer to submit electronic questionnaires wherever it is possible. Electronically sent questionnaires are easy to edit, because in the electronic questionnaire you can set some parameters. The computer can edit the questionnaire itself and the job of the editor becomes easy. You can avoid inconsistencies in the electronic questionnaire. The logical errors can be completely avoided. No response answers are few in electronic questionnaires.
The data editor should have three qualities; he should have to be Intelligent, objective and experienced in editing the data. He should know that how important is the handling of data to the researcher. He should try to avoid the slightest chances of bias, which means that he should also be honest with his work. His data editing will play a major role on the final inferences that the researcher will draw from the data.
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]]>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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