Research: The term research comes from the French word recercher, which means to investigate something thoroughly. This can be simply put as an attempt to discover something.
Empirical: Provable or verifiable by experience or experiment.
Cumulative: Which grows by successive additions.
Exploratory research: Exploratory research attempts to get better understanding of different dimensions of the problem.
Descriptive research: The major purpose of descriptive research is description of the state of affairs as it exists at present.
Explanatory research: This research explains the causes of social phenomena.
Pure and Applied research: Pure research, also called basic research, is concerned with quest for knowledge.
Applied research is concerned with search for ways of using scientific knowledge to solve practical problems.
Experimental and evaluation research: Experimental research involves both manipulation and observation. In the simplest form of an experiment, researches manipulate the independent variable and then observe the responses of subjects on the dependent variable.
Evaluation research is a study measuring the effectiveness of an action program.
Qualitative and quantitative research:
Example: What % of women leading unhappy marital life take initiative to divorce their husband? What was the cost of poll violence (in crores) in Lok Sabha elections in last 5 elections in
Qualitative research on the other hand is concerned with qualitative phenomenon i.e., phenomena relating to or involving quality or kind. It describes reality as experienced by the groups, communities, individuals etc.
Longitudinal and One-time research: This involves the study of the problem over a period of time.
Field testing or laboratory research: Is so called depending upon the environment it is carried out.
Historical research: Is that which utilizes historical sources like documents, remains, etc. to study events or ideas of the past, including the philosophy of persons and groups at any remote point of time.
Conclusion-oriented and decision oriented research:
Decision-oriented research is always for the need of a decision maker and the researcher in this case is not free to embark upon research to his own inclinations.
Hypothesis: A hypothesis is an assumption about relations between variables. It is a tentative explanation of the research problem or a guess about the research outcome which can be empirically verified.
Working hypotheses: Working hypotheses is a preliminary assumption of the researcher about the research topic and a step towards formulating the final research hypothesis.
Research hypothesis: Is a researcher’s proposition about some social fact which he believes that it is true and wants that it should be disproved.
Null hypothesis: Is reverse of research hypothesis. It is a hypothesis of no relationship. Null hypothesis do not exist in reality but are used to test research hypotheses.
Alternative hypothesis: Is a set of two hypotheses (research and null) which states the opposite of the null hypothesis.
Alternative hypothesis is usually the one which one wishes to prove and the null hypothesis is the one which one wishes to disprove. Thus, a null hypothesis represents the hypothesis we are trying to reject, and alternative hypothesis represents all other possibilities.
Scientific hypothesis: Contains statement based on or derived from sufficient theoretical and empirical data.
Research design: Is the detailed strategy to conduct a research.
One cell design: When the data collected in a single situations (S1) pertaining to single time period (T1).
Two cell design/longitudinal: When the data collected pertaining to one situation (or issue) in two time periods (T1).
Panel design: If the study covers two situations at two times.
Sampling: It is the process of obtaining information about an entire population by examining only a part of it.
Take a research project: Awareness of rights among women in rural areas.
Suppose, the study is being conducted in one village which is situated at a distance of 15 kms from the nearest city. It is decided to confine the study to married and unmarried women and widows belonging to the age group of 18-50 years.
The total population of the village is 4800 of whom 2,200 are females and 2,600 are males. Of the total females, 834 (38%) belong to 0-18 age group, 970 (44%) to 18-50 age group and 396 (18%) to 50+age group. Of the 970 women in 18-50 age group, 74 are widows, 87 are unmarried and 809 are married. Age is the main variable of stratifying them in groups while educational level, religion, caste, family structure, family income, and occupation of the head of the family are the variables chosen for analysis purposes.
Population: The class, the families living the city or electorates from which you select a few students, families, electors to question in order to find answers to your research questions are called the population or study population and are usually denoted by the letter (N). Here, the population: 2,200 women.
Target population: Is one to which the researcher would like to generalize his results. Here, 970 women (married, unmarried and widows) in the age group of 18-50 years.
Sample: It is a representative portion of total population. Here, it is 300 women. As the sample size can be decided by the formula: N/(1+N e²).
N= Total population. e= 0.05(confidence level). Here, it is = 970/(1+970* 0.0025)=283. Rounding off this figure, we decide to study 300 women.
Sample size: Here, 300 women.
Sampling design or strategy: The way the sample is selected is called sampling design or strategy.
Sample element: Each entity (person, family, group, organisation) from the population about which information is collected is called a sampling element. Here, all the 970 married and unmarried women and widows in 18-50 years age group will be sampling elements or sampling units.
Sampling frame: It is the list of all units/elements from which the sample is drawn. Here, the electoral roll of 970 women of 18-50 years age group.
Saturation point: In a qualitative research, when you reach a stage where no new information is coming from your respondents, this is called saturation point.
Sampling trait: It is the element on the basis of which we take out the sample from the total population. It could be qualitative (attribute) or quantitative (variable) element. Here, the sampling traits are gender (female), age (18-50 years).
Sampling fraction: It is the proportion of total population to be included in the sample = Size of sample/Total population = 300/2200 = About one seventh of the total population.
Biased sample: When the sample is chosen that some elements are more likely to be represented than other elements, it is called biased sample. Here, suppose of the 300 women of 18-50 years age group, we take 250 women of 18-30 years age group (young) and 25 women of 30-40 years (early middle-aged) and 25 women of 40-50 years (late middle-aged), it would be a biased sample tilted heavily towards young women.
Parameters: Characteristics of a population are called parameters. Like, average age of population at some specified time.
Sampling error: It is the difference between total population value and the sampling value. Suppose the average age (statistical) of a population is 20 years. Average age of the sample is 23 years. The sampling error will be 3 years.
Probability sampling: Is one in which every unit of the population has an equal probability of being selected for the sample. This remains the primary method for selecting large, representative samples for social science and business researches.
Simple random: In this sampling the sampling units are selected randomly.
Stratified random: When the population is divided into different strata or sub-groups and sample units are taken in a simple random method from each group. The final sample contains sample units from all these strata.
Systematic (interval): The sampling is obtained by collecting of elements by drawing every nth person from a pre-determined list of persons. It is randomly selecting the first respondent and then every nth person after that. The number ‘n’ is called the sampling interval.
Cluster: This sampling implies dividing population into clusters and drawing random sample either from all clusters or selected clusters.
Multi-stage: In this method, sampling is selected in various stages but only the last sample of subjects is studied.
Non-probability sampling: Mass media researchers frequently use non-probability sampling.
This is the kind of sampling where all sampling units don’t have the equal chance of selection.
Convenience/Available sampling: This is known as accidental or haphazard sampling. This is a collection of readily accessible subjects for study.
Purposive sampling: It is also known as judgmental sampling. It includes subjects or elements selected for specific characteristics or qualities and eliminates those who fail to meet those criteria.
Quota sampling: Subjects are selected to a predetermined or known percentage/quota.
Snowball sampling: In this technique, the researcher begins the research with a few respondents who are known and available to him. Subsequently, the respondents give other names who meet the criteria of research, who in turn give more names.
Volunteer sampling: This is a technique in which the respondents themselves finally volunteer to give information they hold.
Open-ended question: It requires respondents to generate their own answers. It gives the respondents freedom in answering questions and an opportunity to provide in-depth responses.
Closed-ended questions: Are the fixed choice-questions. They require the respondents to choose a response from a set of responses provided by the researcher.
Direct: Direct questions are personal questions which elicit information about the respondent himself/herself.
Example: Do you believe in media censorship?
Indirect: Indirect questions seek information about other people.
Example: Do you think people of your status and age believe in media censorship?
Nominal: When its response falls in two or more categories.
Example: Rich/Poor, Married/Muslim, rural/urban, Shia/Suni and so on.
Ordinal: Questions in which responses are placed in rank and order of categories.
Example:
Relations with class mates: Excellent/satisfactory/dissatisfactory/can’t say
Interval: Questions which contain a range or interval.
Example:
Income per annum: Below 18,000/ 18,000-36,000/36,000-54,000/54,000-72,000/Above 72, 000.
Contingency: A contingency question is one whose relevance to the respondent is determined by his response to an earlier screening question.
Example:
Q1. Are you in favour of using some method in controlling birth?
Q2. Do you prefer vasectomy/condom/pill/safe period?
The second question is a contingency question.
Filter: These questions aim at eliciting information related to a general aspect of the research topics and are usually followed by more specific question.
Example:
Do you smoke?-Filter question.
Do you (being a girl) smoke?-Contingency question.
Primary data: The primary data are those which are collected afresh and for the first time, and thus happen to be original in character.
Secondary data: The secondary data, on the other hand, are those which have already been collected by someone else and which have already been passed through the statistical process.
Observation: This method implies the collection of information by way of investigator’s own observation, without interviewing the respondents.
Questionnaires: Are a set of questions mailed to the respondents with a request to return after completing the same.
Schedules: Under this method the enumerators are appointed and given training. They are provided with schedules containing relevant questions. These enumerators go to respondents with these schedules.
Depth interviews: Depth interviews are those interviews that are designed to discover underlying motives and desires and are often used in motivational research.
Content analysis: Content analysis consists of analyzing the contents of documentary materials such as books, magazines, newspapers and the contents of all other verbal materials which can be either spoken or printed.
Unstructured interview: There are no specifications in the wording of the questions or the order of the questions. The interviewer forms questions as and when required. The structure of the interview is flexible.
Structured interview: Is based on the structured interview-guide which is little different from the questionnaire. It is a set of specific points and definite questions prepared by the interviewer.
Standardized interview: In standardized interviews, answer to each question is standardized as it is determined by a set of response categories given for this purpose. The respondents are expected to choose one of the given options as the answer.
Unstandardized interview: Is one in which the responses are left open to the respondent. This is used mainly in qualitative research.
Individual interview: Where the interviewer interviews only one respondent at a time.
Group interview: More than one respondent are interviewed simultaneously. The group can be small, say, of two individual (e.g., husband and wife, or two co-workers in a factory) or large, say, of 10 to 20 persons (say students in a class).
Self-administered interview: The respondent is supplied a list of questions along with instructions for writing answers in the appropriate place on the interview form.
Other-administered interview: The interviewer himself writes answers to questions on the response sheet.
Unique interview: Is one which the interviewer collects entire information in one interview.
Panel interviews interview: The interviewer collects information from the same group of respondents two or more times at regular intervals. If different respondents are involved in various stages for asking the same question, it is called Trend Study.
Soft interview: Here the interviewer guides the respondents without putting any pressure on them.
Hard interview: Here, the interview resembles a police interrogation. The interviewer questions the validity and completeness of the answers obtained, often warning the respondents not to lie and forcing them to give an answer when they hesitate.
Personal interviews: There is face to face contact between the interviewer and the interviewee.
Non-personal interviews: No face-to-face contact, but the information is collected through telephone, computer or some other medium.
Case study: Is an intensive study of a case which may be an individual, an institution, a system, a community, an organization, an event, or even the entire culture.
Data processing: Mainly involves various manipulations necessary for preparing the data for analysis. The process (of manipulation) could be manual or electronic. It involves editing, categorizing the open-ended questions, coding, computerization and preparation of tables and diagrams.
Editing data: Information gathered during data collection may lack uniformity. Bringing uniformity to the collected data, checking error and re-arranging.
Coding of data: Coding is translating answers into numerical values or assigning numbers to the various categories of a variable to be used in data analysis. Coding is done by using a code book, code sheet, and a computer card. Coding is done on the basis of the instructions given in the codebook. The code book gives a numerical code for each variable.
Data classification/distribution: Distribution of data as a form of classification of scores obtained for the various categories or a particular variable. There are four types of distributions:
Frequency distribution: In social science research, frequency distribution is very common. It presents the frequency of occurrences of certain categories. This distribution appears in two forms:
Percentage distribution: It is also possible to give frequencies not in absolute numbers but in percentages. For instance instead of saying 200 respondents of total 2000 had a monthly income of less than Rs. 500, we can say 10% of the respondents have a monthly income of less than Rs. 500.
Cumulative distribution: It tells how often the value of the random variable is less than or equal to a particular reference value.
Statistical data distribution: In this type of data distribution, some measure of average is found out of a sample of respondents. Several kind of averages are available (mean, median, mode) and the researcher must decide which is most suitable to his purpose. Once the average has been calculated, the question arises: how representative a figure it is, i.e., how closely the answers are bunched around it. Are most of them very close to it or is there a wide range of variation?
Tabulation of data: After editing, which ensures that the information on the schedule is accurate and categorized in a suitable form, the data are put together in some kinds of tables and may also undergo some other forms of statistical analysis.
Author-date system of referencing: Author-date" (also called "Harvard style", "Harvard referencing", or the "Harvard system"). In the author-date method, the in-text citation is placed in parentheses after the sentence or part thereof that the citation supports, and includes the author's name, year of publication, and a page number where appropriate (Smith 2008, p. 1) or (Smith 2008:1). A full citation is given in the references section:
Smith, John (2008). Playing nicely together.
Author-tile referencing: In the author-title or author-page method, the in-text citation is placed in parentheses after the sentence or part thereof that the citation supports, and includes the author's name (a short title only is necessary when there is more than one work by the same author) and a page number where appropriate (Smith 1) or (Smith, Playing 1). A full citation is given in the references section:
Smith, John. Playing Nicely Together.
Reference by number system: For numbered references, the reference list is ordered in the order of their appearance in the paper: Example: Nothing seemed so certain as the results of the early studies (1). It was precisely this level of apparent certainty, however, which led to a number of subsequent challenges to the techniques used to process the data (2). There were a number of fairly obvious flaws in the data's aspect: consistencies and regularities that seemed most irregular, upon close scrutiny (1,2).
References
1. Smith, J.P. Studying certainty. Science and Culture 9 (1989) 442.
2. Jones, M.R. Cooking the data? Science News 8 (1990) 878.
Data analysis: Is the ordering of data into constituent parts in order to obtain answers to research questions.
Variable: A concept which can take on different quantitative values is called a variable. The concepts like weight, height, income are all examples of variables.
Dependent variable: If one variable depends upon or is a consequence of other variable, it is termed a dependable variable.
Example: If we say, height of a child depends on age, ‘height’ is dependent variable and ‘age’ is independent variable.
Independent variable: The variable that is independent of other variable is called independent variable.
Reliability: The ability of an instrument that consistently gives the same result at different times.
Validity: The ability to produce findings that are in agreement with conceptual or theoretical values.
