1) Characteristics of hypothesis: A hypothesis should have the following characteristic features:- (i) A hypothesis must be precise and clear. If it is not precise and clear, then the inferences drawn on its basis would not be reliable. (ii) A hypothesis must be capable of being put to test. Quite often, the research programmes fail owing to its incapability of being subject to testing for validity. Therefore, some prior study may be conducted by the researcher in order to make a hypothesis testable. A hypothesis “is tested if other deductions can be made from it, which in turn can be confirmed or disproved by observation” (iii) A hypothesis must state relationship between two variables, in the case of relational hypotheses. (iv) A hypothesis must be specific and limited in scope. This is because a simpler hypothesis generally would be easier to test for the researcher. And therefore, he/she must formulate such hypotheses. (v) As far as possible, a hypothesis must be stated in the simplest language, so as to make it understood by all concerned. However, it should be noted that simplicity of a hypothesis is not related to its significance. (vi) A hypothesis must be consistent and derived from the most known facts. In other words, it should be consistent with a substantial body of established facts. That is, it must be in the form of a statement which Judges accept as being the most likely to occur. (vii) A hypothesis must be amenable to testing within a stipulated or reasonable period of time. No matter how excellent a hypothesis, a researcher should not use it if it cannot be tested within a given period of time, as no one can afford to spend a life-time on collecting data to test it. (viii) A hypothesis should state the facts that give rise to the necessity of looking for an explanation. This is to say that by using the hypothesis, and other known and accepted generalizations, a researcher must be able to derive the original problem condition. Therefore, a hypothesis should explain what it actually wants to explain, and for this it should also have an empirical reference.
2) Designing a Questionnaire: A list of questions properly selected and arranged pertaining to the investigation. A questionnaire should be designed or drafted with utmost care and caution so that all the relevant and essential information for the enquiry may be collected without any difficulty, ambiguity and vagueness. Drafting of a good questionnaire is a highly specialized job and requires great care skill, wisdom, efficiency and experience. No hard and fast rule can be laid down for designing or framing a questionnaire
3) Distinguish Correlation and regression
No
|
Correlation
|
Regression
|
1.
|
It is the relationship between two or more variables.
|
It is a mathematical showing the average
relationship between two variables.
|
2.
.
|
Both the variables x and y are random variables
|
Here x is a random variables and y is a fixed variable. Sometimes both the variables may be random variables.
|
3.
|
It finds out the degree of relationship between two variables and not the cause and effect of the variables.
|
It indicates the cause and effect relationship between the variables and establishes a functional relationship.
|
4.
.
|
It is used for testing and verifying the relation between two variables and gives limited information
|
Besides verification it is used for the
prediction of one value in relationship to
the other given value.
|
5.
|
The coefficient of correlation is a
relative measures. The range of
relationship lies between -1 and +1.
|
Regression coefficient is an absolute figure That is If we know the value of the independent variable, we can find the value of the dependent variable.
|
6.
|
It has limited application.
|
It has under application.
|
7
.
|
. It is not very useful for further
mathematical measure
|
It is widely used for further mathematical measure.
|
8
|
. Correlation coefficient is independent of the origin and scale.
|
Regression coefficient is independent in
origin but not have scale.
|
9.
|
If the coefficient of correlation is
positive, then the two variables are
positively correlated and vice-versa
|
The regression coefficient explains that
the decrease in one variable is associated with the increase in the other variable.
|
1) Editing for completeness: While editing, the editor should see that each schedule and questionnaire is complete in all respects. He should see to it that the answers to each and every question have been furnished. If some questions are not answered and if they are of vital importance, the informants should be contacted again either personally or through correspondence. Even after all the efforts it may happen that a few questions remain unanswered. In such questions, the editor should mark ‘No answer’ in the space provided for answers and if the questions are of vital importance then the schedule or questionnaire should be dropped.
2) Features of Research Design: The important features of research design may be outlined as follows: (i) it constitutes a plan that identifies the types and sources of information required for the research problem; (ii) it constitutes a strategy that specifies the methods of data collection and analysis which would be adopted; and (iii) it also specifies the time period of research and monetary budget involved in conducting the study, which comprise the two major constraints of undertaking any research.
3) Features of Research Design: The important features of research design may be outlined as follows: (i) it constitutes a plan that identifies the types and sources of information required for the research problem; (ii) it constitutes a strategy that specifies the methods of data collection and analysis which would be adopted; and (iii) it also specifies the time period of research and monetary budget involved in conducting the study, which comprise the two major constraints of undertaking any research.
4) few tests for analysis of sampling data Anova, % Analysis, Average score analysis, Correlation, regression, rank correlation, KP correlation, chi square etc
5) Good Sample Design: The following are the characteristic features of a good sample design: (a) the sample design should yield a truly representative sample; (b) the sample design should be such that it results in small sampling error; (c) the sample design should be viable in the context of budgetary constraints of the research study; (d) the sample design should be such that the systematic bias can be controlled; and (e) the sample must be such that the results of the sample study would be applicable, in general, to the universe at a reasonable level of confidence
6) Hypothesis and procedure of developing a good hypothesis: “Hypothesis may be defined as a proposition or a set of propositions set forth as an explanation for the occurrence of some specified group of phenomena either asserted merely as a provisional conjecture to guide some investigation in the light of established facts” (Kothari, 1988). A research hypothesis is quite often a predictive statement, which is capable of being tested using scientific methods that involve an independent and some dependent variables. Testing a hypothesis refers to verifying whether the hypothesis is valid or not. Hypothesis testing attempts to check whether to accept or not to accept the null hypothesis. The procedure of hypothesis testing includes all the steps that a researcher undertakes for making a choice between the two alternative actions of rejecting or accepting a null hypothesis. The various steps involved in hypothesis testing are as follows: (i) Making a Formal Statement: (ii) Selecting a Significance Level: (iii) Deciding the Distribution to Use: (iv) Selection of a Random Sample and Computing an Appropriate Value: (v) Calculation of the Probability: (vi) Comparing the Probability: (i) Making a Formal Statement: This step involves making a formal statement of the null hypothesis (H0) and the alternative hypothesis (Ha). This implies that the hypotheses should be clearly stated within the purview of the research problem. For example, suppose a school teacher wants to test the understanding capacity of the students which must be rated more than 90 per cent in terms of marks, the hypotheses may be stated as follows: Null Hypothesis H0 : = 100 Alternative Hypothesis Ha : > 100 (ii) Selecting a Significance Level: The hypotheses should be tested on a pre-determined level of significance, which should be specified. Usually, either 5% level or 1% level is considered for the purpose. The factors that determine the levels of significance are: (a) the magnitude of difference between the sample means; (b) the sample size: (c) the variability of measurements within samples; and (d) whether the hypothesis is directional or non-directional (Kothari, 1988). In sum, the level of significance should be sufficient in the context of the nature and purpose of enquiry. (iii) Deciding the Distribution to Use: After making decision on the level of significance for hypothesis testing, the researcher has to next determine the appropriate sampling distribution. The choice to be made generally relates to normal distribution and the t-distribution. The rules governing the selection of the correct distribution are similar to the ones already discussed with respect to estimation. (iv) Selection of a Random Sample and Computing an Appropriate Value: Another step involved in hypothesis testing is the selection of a random sample and then computing a suitable value from the sample data relating to test statistic by using the appropriate distribution. In other words, it involves drawing a sample for furnishing empirical data. (v) Calculation of the Probability: The next step for the researcher is to calculate the probability that the sample result would diverge as far as it can from expectations, under the situation when the null hypothesis is actually true. (vi) Comparing the Probability: Another step involved consists of making a comparison of the probability calculated with the specified value for α, the significance level. If the calculated probability works out to be equal to or smaller than the α value in case of one-tailed test, then the null hypothesis is to be rejected. On the other hand, if the calculated probability is greater, then the null hypothesis is to be accepted. In case the null hypothesis H0 is rejected, the researcher runs the risk of committing the Type I error. But, if the null hypothesis H0 is accepted, then it involves some risk (which cannot be specified in size as long as H0 is vague and not specific) of committing the Type II error.
7) Probability Sampling: Probability sampling is also known as ‘choice sampling’ or ‘random sampling’. Under this sampling design, every item of the universe has an equal chance of being included in the sample. In a way, it is a lottery method under which individual units are selected from the whole group, not deliberately, but by using some mechanical process. Therefore, only chance would determine whether an item or the other would be included in the sample or not. The results obtained from probability or random sampling would be assured in terms of probability. That is, the researcher can measure the errors of estimation or the significance of results obtained from the random sample. This is the superiority of random sampling design over the deliberate sampling design. Random sampling satisfies the law of Statistical Regularity, according to which if on an average the sample chosen is random, then it would have the same composition and characteristics of the universe. This is the reason why the random sampling method is considered the best technique of choosing a representative sample. The following are the implications of the random sampling: (i) it provides each element in the population an equal probability chance of being chosen in the sample, with all choices being independent of one another and (ii) it offers each possible sample combination an equal probability opportunity of being selected.
8) Meaning and significance of research design in research.: The most important step after defining the research problem is preparing the design of the research project, which is popularly known as the ‘research design’. A research design helps to decide upon issues like what, when, where, how much, by what means etc. with regard to an enquiry or a research study. A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. Infact, research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data (Selltiz et al, 1962). Thus, research design provides an outline of what the researcher is going to do in terms of framing the hypothesis, its operational implications and the final data analysis. Specifically, the research design highlights decisions which include: (i) the nature of the study (ii) the purpose of the study (iii) the location where the study would be conducted (iv) the nature of data required (v) from where the required data can be collected (vi) what time period the study would cover (vii) the type of sample design that would be used (viii) the techniques of data collection that would be used (ix) the methods of data analysis that would be adopted and (x) the manner in which the report would be prepared . In view of the stated research design decisions, the overall research design may be divided into the following (a) the sampling design that deals with the method of selecting items to be observed for the selected study; (b) the observational design that relates to the conditions under which the observations are to be made; (c) the statistical design that concerns with the question of how many items are to be observed, and how the information and data gathered are to be analysed; and (d) the operational design that deals with the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out.
9) Merits and demerits of mailed questionnaire method: Mailed Questionnaire Method: Under this method, a list of questions pertaining to the survey which is known as ‘Questionnaire’ is prepared and sent to the various informants by post. Sometimes the researcher himself too contacts the respondents and gets the responses related to various questions in the questionnaire. The questionnaire contains questions and provides space for answers. A request is made to the informants through a covering letter to fill up the questionnaire and send it back within a specified time. The questionnaire studies can be classified on the basis of: i. The degree to which the questionnaire is formalized or structured. ii. The disguise or lack of disguise of the questionnaire and iii. The communication method used. When no formal questionnaire is used, interviewers adapt their questioning to each interview as it progresses. They might even try to elicit responses by indirect methods, such as showing pictures on which the respondent comments. When a researcher follows a prescribed sequence of questions, it is referred to as structured study. On the other hand, when no prescribed sequence of questions exists, the study is non-structured. When questionnaires are constructed in such a way that the objective is clear to the respondents then these questionnaires are known as non- disguised; on the other hand, when the objective is not clear, the questionnaire is a disguised one. On the basis of these two classifications, four types of studies can he distinguished: i. Non-disguised structured, ii. Non-disguised non-structured, iii. Disguised structured and iv. Disguised non-structured. There are certain merits and demerits or limitations of this method of data collection which are discussed below: Merits: 1. Questionnaire method of data collection can be easily adopted where the field of investigation is very vast and the informants are spread over a wide geographical area. 2. This method is relatively cheap and expeditious provided the informants respond in time. 3. This method has proved to be superior when compared to other methods like personal interviews or telephone method. This is because when questions pertaining to personal nature or the ones requiring reaction by the family are put forth to the informants, there is a chance for them to be embarrassed in answering them. Demerits: 1. This method can be adopted only where the informants are literate people so that they can understand written questions and lend the answers in writing. 2. It involves some uncertainty about the response. Co-operation on the part of informants may be difficult to presume. 3. The information provided by the informants may not be correct and it may be difficult to verify the accuracy. However, by following the guidelines given below, this method can be made more effective: The questionnaires should be made in such a manner that they do not become an undue burden on the respondents; otherwise the respondents may not return them back. i. Prepaid postage stamp should be affixed ii. The sample should be large iii. It should be adopted in such enquiries where it is expected that the respondents would return the questionnaire because of their own interest in the enquiry. iv. It should be preferred in such enquiries where there could be a legal compulsion to provide the information.
10) Merits and demerits of sampling: Merits: 1. It saves time: Sampling method of data collection saves time because fewer items are collected and processed. When the results are urgently required, this method is very helpful. 2. It reduces cost: Since only a few and selected items are studied in sampling, there is reduction in cost of money and reduction in terms of man hours. 3. More reliable results can be obtained: Through sampling, more reliable results can be obtained because (a) there are fewer chances of sampling statistical errors. If there is sampling error, it is possible to estimate and control the results.(b) Highly experienced and trained persons can be employed for scientific processing and analyzing of relatively limited data and they can use their high technical knowledge and get more accurate and reliable results. 4. It provides more detailed information: As it saves time, money and labor, more detail information can be collected in a sample survey. 5. Sometimes only Sampling method to depend upon: Some times it so happens that one has to depend upon sampling method alone because if the population under study is finite, sampling method is the only method to be used. For example, if someone’s blood has to be examined, it will become fatal to take all the blood out from the body and study depending upon the total enumeration method. 6. Administrative convenience: 7. More scientific: shortcomings of this method which are discussed below: 1. Illusory conclusion: If a sample enquiry is not carefully planned and executed, the conclusions may be inaccurate and misleading. 2. Sample not representative: To make the sample representative is a difficult task. If a representative sample is taken from the universe, the result is applicable to the whole population. If the sample is not representative of the universe the result may be false and misleading. 3. Lack of experts: As there are lack of experts to plan and conduct a sample survey, its execution and analysis, and its results would be unsatisfactory and not trustworthy. 4. Sometimes more difficult than census method: Sometimes the sampling plan may be complicated and requires more money, labor and time than a census method. 5. Personal bias: There may be personal biases and prejudices with regard to the choice of technique and drawing of sampling units. 6. Choice of sample size: If the size of the sample is not appropriate then it may lead to untrue characteristics of the population. 7. Conditions of complete coverage: If the information is required for each and every item of the universe, then a complete enumeration survey is better.
11) Merits: of q’ method: 1. Questionnaire method of data collection can be easily adopted where the field of investigation is very vast and the informants are spread over a wide geographical area. 2. This method is relatively cheap and expeditious provided the informants respond in time. 3. This method has proved to be superior when compared to other methods like personal interviews or telephone method. This is because when questions pertaining to personal nature or the ones requiring reaction by the family are put forth to the informants, there is a chance for them to be embarrassed in answering them. Demerits: 1. This method can be adopted only where the informants are literate people so that they can understand written questions and lend the answers in writing. 2. It involves some uncertainty about the response. Co-operation on the part of informants may be difficult to presume. 3. The information provided by the informants may not be correct and it may be difficult to verify the accuracy. However, by following the guidelines given below, this method can be made more effective: The questionnaires should be made in such a manner that they do not become an undue burden on the respondents; otherwise the respondents may not return them back. i. Prepaid postage stamp should be affixed ii. The sample should be large iii. It should be adopted in such enquiries where it is expected that the respondents would return the questionnaire because of their own interest in the enquiry. iv. It should be preferred in such enquiries where there could be a legal compulsion to provide the information.
12) Methods of Collecting Primary Data: Primary data may be obtained by applying any of the following methods: 1. Direct Personal Interviews. 2. Indirect oral interviews. 3. Information from correspondents. 4. Mailed questionnaire methods. 5. Schedule sent through enumerators. Mailed Questionnaire Method: Under this method, a list of questions pertaining to the survey which is known as ‘Questionnaire’ is prepared and sent to the various informants by post. Sometimes the researcher himself too contacts the respondents and gets the responses related to various questions in the questionnaire. The questionnaire contains questions and provides space for answers. A request is made to the informants through a covering letter to fill up the questionnaire and send it back within a specified time. The questionnaire studies can be classified on the basis of: i. The degree to which the questionnaire is formalized or structured. ii. The disguise or lack of disguise of the questionnaire and iii. The communication method used. When no formal questionnaire is used, interviewers adapt their questioning to each interview as it progresses. They might even try to elicit responses by indirect methods, such as showing pictures on which the respondent comments. When a researcher follows a prescribed sequence of questions, it is referred to as structured study. On the other hand, when no prescribed sequence of questions exists, the study is non-structured. When questionnaires are constructed in such a way that the objective is clear to the respondents then these questionnaires are known as non- disguised; on the other hand, when the objective is not clear, the questionnaire is a disguised one. On the basis of these two classifications, four types of studies can he distinguished: i. Non-disguised structured, ii. Non-disguised non-structured, iii. Disguised structured and iv. Disguised non-structured.
13) Methods used in correlation: The following are the various methods used in correlation. _ Scatter diagram method. _ Graphic Method. _ karl pearson's coefficient of correlation. _ Concurrent Deviation Method. _ Method of least squares.
14) Non-Probability Sampling: Non-probability sampling is the sampling procedure that does not afford any basis for estimating the probability that each item in the population would have an equal chance of being included in the sample. Non-probability sampling is also known as deliberate sampling, judgment sampling and purposive sampling. Under this type of sampling, the items for the sample are deliberately chosen by the researcher; and his/her choice concerning the choice of items remains supreme. In other words, under non-probability sampling the researchers select a particular unit of the universe for forming a sample on the basis that the small number that is thus selected out of a huge one would be typical or representative of the whole population. For example, to study the economic conditions of people living in a state, a few towns or village may be purposively selected for an intensive study based on the principle that they are representative of the entire state. In such a case, the judgment of the researcher of the study assumes prime importance in this sampling design. Quota Sampling: Quota sampling is also an example of non-probability sampling. Under this sampling, the researchers simply assume quotas to be filled from different strata, with certain restrictions imposed on how they should be selected. This type of sampling is very convenient and is relatively less expensive. However, the samples selected using this method certainly do not satisfy the characteristics of random samples. They are essentially judgment samples and inferences drawn based on that would not be amenable to statistical treatment in a formal way.
15) Precautions while using Secondary data: secondary data are those data which have already been collected and analyzed by some earlier agency for its own use, and later the same data are used by a different agency. According to W.A.Neiswanger, “A primary source is a publication in which the data are published by the same authority which gathered and analyzed them. A secondary source is a publication, reporting the data which was gathered by other authorities and for which others are responsible.” The various sources of secondary data can be divided into two broad categories: 1. Published sources, and 2. Unpublished sources. Since secondary data have already been obtained, it is highly desirable that a proper scrutiny of such data is made before they are used by the investigator. In fact the user has to be extra-cautious while using secondary data. In this context Prof. Bowley rightly points out that “Secondary data should not be accepted at their face value.” The reason being that data may be erroneous in many respects due to bias, inadequate size of the sample, substitution, errors of definition, arithmetical errors etc. Even if there is no error such data may not be suitable and adequate for the purpose of the enquiry. Prof. Simon Kuznet’s view in this regard is also of great importance. According to him, “The degree of reliability of secondary source is to be assessed from the source, the compiler and his capacity to produce correct statistics and the users also, for the most part, tend to accept a series particularly one issued by a government agency at its face value without enquiring its reliability”. Therefore, before using the secondary data the investigators should consider the following factors: Suitability Adequacy, Relaibility. There are a lot of differences in the methods of collecting Primary and Secondary data. Primary data which is to be collected originally involves an entire scheme of plan starting with the definitions of various terms used, units to be employed, type of enquiry to be conducted, extent of accuracy aimed at etc. For the collection of secondary data, a mere compilation of the existing data would be sufficient. A proper choice between the type of data needed for any particular statistical investigation is to be made after taking into consideration the nature, objective and scope of the enquiry; the time and the finances at the disposal of the agency; the degree of precision aimed at and the status of the agency . In using the secondary data, it is best to obtain the data from the primary source as far as possible. By doing so, we would at least save ourselves from the errors of transcription which might have inadvertently crept in the secondary source. Moreover, the primary source will also provide us with detailed discussion about the terminology used, statistical units employed, size of the sample and the technique of sampling (if sampling method was used), methods of data collection and analysis of results and we can ascertain ourselves if these would suit our purpose. Now-a-days in a large number of statistical enquiries, secondary data are generally used because fairly reliable published data on a large number of diverse fields are now available in the publications of governments, private organizations and research institutions, agencies, periodicals and magazines etc. In fact, primary data are collected only if there do not exist any secondary data suited to the investigation under study. In some of the investigations both primary as well as secondary data may be used
16) Primary data: There is no problem if secondary data are used for research. However, if primary data are to be collected, a decision has to be taken whether (i) census method or (ii) sample technique is to be used for data collection. In census method, we go for total enumeration i.e., all the units of a universe have to be investigated. But in sample technique, we inspect or study only a selected representative and adequate fraction of the population and after analyzing the results of the sample data we draw conclusions about the characteristics of the population. Selection of a particular technique becomes difficult because where population or census method is more scientific and 100% accuracy can be attained through this method, choosing this becomes difficult because it is time taking, it requires more labor and it is very expensive. Therefore, for a single researcher or for a small institution it proves to be unsuitable. On the other hand, sample method is less time taking, less laborious and less expensive but a 100% accuracy cannot be attained through this method because of sampling and non-sampling errors attached to this method. Hence, a researcher has to be very cautious and careful while choosing a particular method.
17) Probability sampling and non probability sampling: Sample designs may be classified into different categories based on two factors, namely, the representation basis and the element selection technique. Under the representation basis, the sample may be classified as: I. non-probability sampling II. probability sampling. While probability sampling is based on random selection, the non-probability sampling is based on ‘non-random’ sampling. I. Non-Probability Sampling: Non-probability sampling is the sampling procedure that does not afford any basis for estimating the probability that each item in the population would have an equal chance of being included in the sample. Non-probability sampling is also known as deliberate sampling, judgment sampling and purposive sampling. Under this type of sampling, the items for the sample are deliberately chosen by the researcher; and his/her choice concerning the choice of items remains supreme. In other words, under non-probability sampling the researchers select a particular unit of the universe for forming a sample on the basis that the small number that is thus selected out of a huge one would be typical or representative of the whole population. For example, to study the economic conditions of people living in a state, a few towns or village may be purposively selected for an intensive study based on the principle that they are representative of the entire state. In such a case, the judgment of the researcher of the study assumes prime importance in this sampling design. Quota Sampling: Quota sampling is also an example of non-probability sampling. Under this sampling, the researchers simply assume quotas to be filled from different strata, with certain restrictions imposed on how they should be selected. This type of sampling is very convenient and is relatively less expensive. However, the samples selected using this method certainly do not satisfy the characteristics of random samples. They are essentially judgment samples and inferences drawn based on that would not be amenable to statistical treatment in a formal way. II. Probability Sampling: Probability sampling is also known as ‘choice sampling’ or ‘random sampling’. Under this sampling design, every item of the universe has an equal chance of being included in the sample. In a way, it is a lottery method under which individual units are selected from the whole group, not deliberately, but by using some mechanical process. Therefore, only chance would determine whether an item or the other would be included in the sample or not. The results obtained from probability or random sampling would be assured in terms of probability. That is, the researcher can measure the errors of estimation or the significance of results obtained from the random sample. This is the superiority of random sampling design over the deliberate sampling design. Random sampling satisfies the law of Statistical Regularity, according to which if on an average the sample chosen is random, then it would have the same composition and characteristics of the universe. This is the reason why the random sampling method is considered the best technique of choosing a representative sample. The following are the implications of the random sampling: (i) it provides each element in the population an equal probability chance of being chosen in the sample, with all choices being independent of one another and (ii) it offers each possible sample combination an equal probability opportunity of being selected.
18) Procedure for Testing Hypothesis
19) Procedure for Testing Hypothesis: _ Set up the Null hypothesis: Ho _ Set up the alternative hypothesis: H1 _ Choose an appropriate level of significance _ Calculate the test statistic _ Compare the computed value with the table value.
20) Quantitative and qualitative research with suitable examples: There are two main approaches to research, namely quantitative approach and qualitative approach. The quantitative approach involves the collection of quantitative data, which are put to rigorous quantitative analysis in a formal and rigid manner. This approach further includes experimental, inferential, and simulation approaches to research. Meanwhile, the qualitative approach uses the method of subjective assessment of opinions, behaviour and attitudes. Research in such a situation is a function of the researcher’s impressions and insights. The results generated by this type of research are either in non-quantitative form or in the form which cannot be put to rigorous quantitative analysis. Quantitative research relates to aspects that can be quantified or can be expressed in terms of quantity. It involves the measurement of quantity or amount. The various available statistical and econometric methods are adopted for analysis in such research. Some such includes correlation, regressions and time series analysis. On the other hand, Qualitative research is concerned with qualitative phenomena, or more specifically, the aspects related to or involving quality or kind. For example, an important type of qualitative research is ‘Motivation Research’, which investigates into the reasons for human behaviour. The main aim of this type of research is discovering the underlying motives and desires of human beings by using in-depth interviews. The other techniques employed in such research are story completion tests, sentence completion tests, word association tests, and other similar projective methods. Qualitative research is particularly significant in the context of behavioural sciences, which aim at discovering the underlying motives of human behaviour. Such research helps to analyse the various factors that motivate human beings to behave in a certain manner, besides contributing to an understanding of what makes individuals like or dislike a particular thing. However, it is worth noting that conducting qualitative research in practice is considerably a difficult task. Hence, while undertaking such research, seeking guidance from experienced expert researchers is important.
21) Quantitative vs. qualitative research : There are two main approaches to research, namely quantitative approach and qualitative approach. The quantitative approach involves the collection of quantitative data, which are put to rigorous quantitative analysis in a formal and rigid manner. This approach further includes experimental, inferential, and simulation approaches to research. Meanwhile, the qualitative approach uses the method of subjective assessment of opinions, behaviour and attitudes. Research in such a situation is a function of the researcher’s impressions and insights. The results generated by this type of research are either in non-quantitative form or in the form which cannot be put to rigorous quantitative analysis. Quantitative research relates to aspects that can be quantified or can be expressed in terms of quantity. It involves the measurement of quantity or amount. The various available statistical and econometric methods are adopted for analysis in such research. Some such includes correlation, regressions and time series analysis. On the other hand, Qualitative research is concerned with qualitative phenomena, or more specifically, the aspects related to or involving quality or kind. For example, an important type of qualitative research is ‘Motivation Research’, which investigates into the reasons for human behaviour. The main aim of this type of research is discovering the underlying motives and desires of human beings by using in-depth interviews. The other techniques employed in such research are story completion tests, sentence completion tests, word association tests, and other similar projective methods. Qualitative research is particularly significant in the context of behavioural sciences, which aim at discovering the underlying motives of human behaviour. Such research helps to analyse the various factors that motivate human beings to behave in a certain manner, besides contributing to an understanding of what makes individuals like or dislike a particular thing. However, it is worth noting that conducting qualitative research in practice is considerably a difficult task. Hence, while undertaking such research, seeking guidance from experienced expert researchers is important.
22) Questionnaire: A list of questions properly selected and arranged pertaining to the investigation. A questionnaire should be designed or drafted with utmost care and caution so that all the relevant and essential information for the enquiry may be collected without any difficulty, ambiguity and vagueness. Drafting of a good questionnaire is a highly specialized job and requires great care skill, wisdom, efficiency and experience. No hard and fast rule can be laid down for designing or framing a questionnaire
23) Research : Research in simple terms refers to search for knowledge. It is a scientific and systematic search for information on a particular topic or issue. It is also known as the art of scientific investigation. Several social scientists have defined research in different ways Although every research study has its own specific objectives, the research objectives may be broadly grouped as follows: 1. to gain familiarity with new insights into a phenomenon (i.e., formulative research studies); 2. to accurately portray the characteristics of a particular individual, group, or a situation (i.e., descriptive research studes); 3. to analyse the frequency with which something occurs (i.e., diagnostic research studies); and 4. to examine the hypothesis of a causal relationship between two variables (i.e., hypothesis-testing research studies).
24) Research Design? What are its features: The most important step after defining the research problem is preparing the design of the research project, which is popularly known as the ‘research design’. A research design helps to decide upon issues like what, when, where, how much, by what means etc. with regard to an enquiry or a research study. A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. Infact, research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data (Selltiz et al, 1962). Thus, research design provides an outline of what the researcher is going to do in terms of framing the hypothesis, its operational implications and the final data analysis. Specifically, the research design highlights decisions which include: (i) the nature of the study (ii) the purpose of the study (iii) the location where the study would be conducted (iv) the nature of data required (v) from where the required data can be collected (vi) what time period the study would cover (vii) the type of sample design that would be used (viii) the techniques of data collection that would be used (ix) the methods of data analysis that would be adopted and (x) the manner in which the report would be prepared . In view of the stated research design decisions, the overall research design may be divided into the following (a) the sampling design that deals with the method of selecting items to be observed for the selected study; (b) the observational design that relates to the conditions under which the observations are to be made; (c) the statistical design that concerns with the question of how many items are to be observed, and how the information and data gathered are to be analysed; and (d) the operational design that deals with the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out.
25) Research Methods vs. Methodology
26) Research Methods vs. Methodology: Research methods include all those techniques/methods that are adopted for conducting research. Thus, research techniques or methods are the methods that the researchers adopt for conducting the research studies. On the other hand, research methodology is the way in which research problems are solved systematically. It is a science of studying how research is conducted scientifically. Under it, the researcher acquaints himself/herself with the various steps generally adopted to study a research problem, along with the underlying logic behind them. Hence, it is not only important for the researcher to know the research techniques/methods, but also the scientific approach called methodology.
27) Research objectives may be broadly grouped as follows: 1. to gain familiarity with new insights into a phenomenon (i.e., formulative research studies); 2. to accurately portray the characteristics of a particular individual, group, or a situation (i.e., descriptive research studies); 3. to analyse the frequency with which something occurs (i.e., diagnostic research studies); and 4. to examine the hypothesis of a causal relationship between two variables (i.e., hypothesis-testing research studies).
28) Research Techniques: Research methods include all those techniques/methods that are adopted for conducting research. Thus, research techniques or methods are the methods that the researchers adopt for conducting the research studies. On the other hand, research methodology is the way in which research problems are solved systematically. It is a science of studying how research is conducted scientifically. Under it, the researcher acquaints himself/herself with the various steps generally adopted to study a research problem, along with the underlying logic behind them. Hence, it is not only important for the researcher to know the research techniques/methods, but also the scientific approach called methodology.
29) Significance of Correlation
30) Significance of Correlation : Correlation analysis deals with the association between two or more variables. There are some kinds of relationship between variables. For example relationship between price and supply, income and expenditure etc. _ The two variables are closely related. That is the estimate the value of one variable given the value of another. _ The effect of correlation is to reduce the range of uncertainty.
31) State the properties of correlation coefficient? : 1. The coefficient of correlation of correlation lies between – 1 and + 1. 2. The coefficient of correlation is independent of change of scale and origin of the variable x and y. 3. The coefficient of correlation is the geometric mean of two regression coefficients. 4. The degree of relationship between the two variables is symmetrical.
32) steps involved in conduct of research: Research process consists of a series of steps or actions required for effectively conducting research. The following are the steps that provide useful procedural guidelines regarding the conduct of research: (1) formulating the research problem; (2) extensive literature survey; (3) developing hypothesis; (4) preparing the research design; (5) determining sample design; (6) collecting data; (7) execution of the project; (8) analysis of data; (9) hypothesis testing; (10) generalization and interpretation, and (11) preparation of the report or presentation of the results. In other words, it involves the formal write-up of conclusions.
33) Type I & Type II Errors: Rejection of the hypothesis when it should be accepted is known as Type I error. Acceptance of a hypothesis when it should be rejected is known as Type II error. As regards the testing of hypotheses, a researcher can make basically two types of errors. He/she may reject H0 when it is true, or accept H0 when it is not true. The former is called as Type I error and the latter is known as Type II error. In other words, Type I error implies the rejection of a hypothesis when it must have been accepted, while Type II error implies the acceptance of a hypothesis which must have been rejected. Type I error is denoted by α (alpha) and is known as α error, while Type II error is usually denoted by β (beta) and is known as β error.
34) types of correlation analysis: Positive, negative, perfect positive, perfect negative, zero, linear, simple, partial, multiple
35) Types of Research Design: There are different types of research designs. They may be broadly categorized as: (1) Exploratory Research Design; (2) Descriptive and Diagnostic Research Design; and (3) Hypothesis-Testing Research Design
36) types of research designs: There are different types of research designs. They may be broadly categorized as: (1) Exploratory Research Design; (2) Descriptive and Diagnostic Research Design; and (3) Hypothesis-Testing Research Design
37) types of Sample designs: Sample designs may be classified into different categories based on two factors, namely, the representation basis and the element selection technique. Under the representation basis, the sample may be classified as: I. non-probability sampling II. probability sampling While probability sampling is based on random selection, the non-probability sampling is based on ‘non-random’ sampling.
38) Properties of Normal Distribution: 1. The normal curve is perfectly symmetrical about the mean (μ) and is bell shaped 2. It has only one mode. 3. The mean and median coincide with mode 4. The points of inflexion are at x = μ±s 5. The maximum ordinate is at x =μ Its value is1/sÖ2p
39) The word 'population' or Universe denotes aggregate or group of individual objects of any nature whose general characteristics are studied by a statistical investigation. The population may finite or infinite Sample is a finite sub set of the population and the number of items in a sample is called size of a sample. It may be large or small sample. Statistical constants of population namely mean (μ) and variance (s2) etc, which are usually referred as parameter The statistical measures from sample observation are known as mean (x) and S.D (S), variable (S2)
40) "A hypothesis in statistics is simply a quantitative statement about a population". It is based on assumptions. Null hypothesis is the hypothesis, which is tested for possible rejection under the assumption that it is true and is denoted as Ho. It is the statement about the population, which gives an alternative to the null hypothesis and is denoted by H1
41) Rejection of the hypothesis when it should be accepted is known as Type I error. Acceptance of a hypothesis when it should be rejected is known as Type II error. In testing a given hypothesis, the maximum probability with which we could be willing to risk is called level of significance of the test. The value of the test statistic, which separates the sample space into rejection region and the acceptance region, is called the critical value.
42) The tests, which do not depend upon the population parameters such as mean and the variance, they are called non-parametric tests.
43) Positive correlation: If two variables tend to move together in the same direction. That is an increase in the value of one variable is accompanied by an increase in the value of other variable.
44) negative correlation: If two variables tend to move together in opposite directions so that an increase or decrease in the value of one variable is accompanied by a decrease or increase in the value of other variable then the correlation is called negative or inverse correlation
45) linear correlation: If the amount of change in one variable bears constant ration to the amount of change in the other variable then the correlation is said to be linear
46) non-linear correlation: If the amount of change in one variable does not bear constant ratio to the amount of change in other variable. It is also known as curvilinear correlation
Regression is the measure of the average relationship between two or more variables in terms of the original units of data
