Some concepts about research

Some concepts about research.

What is research?. A systematic means of problem solving Research is an organized and systematic way to find answers to questions Research is a creative process Why is research important? Knowledge obtained from sound research is transformed into practice The Research Idea Professional experience Burning questions Literature Professional meetings Discussions.

Types of Research Questions. 3 Types Descriptive questions Difference questions Relationship questions Descriptive Questions Purpose: To describe phenomena or characteristics of a particular group of subjects being studied Survey research Qualitative research.

Difference Questions. Purpose: To make comparisons between or within groups. Is there a difference? Experimental research Treatment vs. control Pre- vs. post-test comparisons Non experimental research Compare one group to another based on existing characteristics.

Relationship Questions. Purpose To investigate the degree to which two or more variables covary or are associated with each other Rather than analyzing the differences between groups, researchers characterize the relationships among them. Extent to which variables are related.

Theory vs. Hypothesis. Hypothesis A belief or prediction of the eventual outcome of the research A concrete, specific statement about the relationships between phenomena Based on deductive reasoning Theory A belief or assumption about how things relate to each other A theory establishes a cause-and-effect relationship between variables with a purpose of explaining and predicting phenomena Based on inductive reasoning.

Data. Classification of Data: based on 1. The source from which it is collected The nature of variables Time.

1. Based on source. Data can be two types of data i.e., primary and secondary data A. Primary data are those information gathered by researcher himself and are gathered for the first time, thus, happening to be original. Primary data is fresh data collected by the researcher original data collected for the investigation at hand.

Data collection methods of primary data. We collect primary data during the course of doing experiment in an experimental research. But in case of non-experimental research a researcher conducts survey to obtain primary data through: Many method of collecting primary data particularly in survey and descriptive research. Some of these are Observation Method Interview method Questionnaire Method Schedule (Interview Questionnaires) Method. Focus Group Discussion (FDG).

B. Secondary Data. Secondary data are information, which are gathered or obtained indirectly. The researcher does not obtain them himself or directly rather he gathered them from published and unpublished material. Secondary data are collected by the individuals and/or institution for their own use.

Cont’d. The secondary sources of information can be classified into: A. Personal Document B. Public document.

A. Personal document. These include the entire published and unpublished document by the individuals for different purpose. Personal documents have been classified: Biography / Autobiography Diaries : Letters Memories.

B. Public Documents. Public documents are information gathered from some governmental or non-governmental institutions. Public document can be either unpublished or published documents..

Cont’d. Some common public documents are of the following types 1. Records 2. Census report 3. Journals and magazines 4. Newspapers 5. Other documents: information sources like, television, film, radio and public speech..

2. The nature of variables. A. Quantitative Data Are data which are capable of being expressed numerically e.g. height, weight, length, export, price, wage etc. A data is said to be quantitative if it is presented in these forms which can be quantified using units of measurement. B. Qualitative Data Are data which are not capable of being expressed quantitatively e.g. sex, nationality, color, intelligence etc. Those characteristics are descriptive in nature..

3.Based on time. Four types of data may be available for empirical analysis: Time series, cross-section, pooled (i.e., combination of time series and cross section) and panel data..

1. Cross-Section Data. A cross-sectional data consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time..

2. Time Series Data. A time series data set consists of observations on a variable or several variables over time. Examples of time series data include stock prices, money supply, consumer price index, gross domestic product, annual homicide rates, and automobile sales figures. Such data may be collected at regular time intervals, such as daily weekly, monthly quarterly (e.g., GDP), annually etc Because past events can influence future events and lags in behavior are prevalent in the social sciences, time is an important dimension in a time series data set..

3. Pooled Cross Sections. Some data sets have both cross-sectional and time series features. For example, suppose that two cross-sectional household surveys are taken in the United States, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family size, and so on. In 1990, a new random sample of households is taken using the same survey questions. In order to increase our sample size, we can form a pooled cross section by combining the two years. Pooling cross sections from different years is often an effective way of analyzing the effects of a new government policy. The idea is to collect data from the years before and after a key policy change..

4. Panel or Longitudinal Data. A panel data (or longitudinal data) set consists of a time series for each cross sectional member in the data set. As an example, suppose we have wage, education, and employment history for a set of individuals followed over a ten-year period. Or We might collect information, such as investment and financial data, about the same set of firms over a five-year time period..

Cont’d. Population In statistics the term population is used to mean the totality of items in a given investigation. Example 1 . if a researcher wants to know the feeling of students for cost sharing in DTU; the population is all Debre Tabor university students. 2. If a certain enquiry is needed to determine the yield of wheat per hectare in Amhara region in the production year of 1997 E.C, the population includes all farms in Amahra Region which wheat was grown in 1997 E.C.

Cont’d. Universe – it is the totality of a given population beyond the items intended for a given investigation. The examples 1, and 2 show us Debre Tabor University students and all farms of wheat in Amhara Region are population for each investigation respectively. Look, for population Debre Tabor University students, its universe is all university students in Ethiopia. For population wheat farms in Amhara Region in 1997 E.C, the universe is all wheat farms in Ethiopia in the same year..

Cont’d. Census – is a method of enumeration which an investigator adopt for the purpose of his inquiry to observe each item constituting the population and obtain an information relevant to his purpose. In short, census means a system of collecting data by considering the whole population to obtain the required information. Example: to know the number of population (people) in Ethiopia, enumerators will consider each individual over all the country..

Cont’d. Sample – Is part of the population which is assumed to represent the population in acquiring relevant information for the intended investigation. It is a subset of the population, selected using some sampling technique in such a way that they represent the population. Look, for the population Debre Tabor University students, the sample may be only students of faculty of Business and economics..

Cont’d. Parameter: Any measurable characteristic of population is called parameter. Example: If data is collected from a population and we are interested to calculate the average value, or variation or standard deviation, are considering the parameter. Thus parameter includes population mean, median, mode, standard deviation or variance. Statistic: Any measurable characteristic of a sample is called statistic. If we collect data by using some samples and want to estimate the mean, median, mode or standard deviation (variance) we are considering statistic. Thus, statistic means sample mean, median, mode etc..

Cont’d. Sampling -: It is the process of selecting a number of individuals for a study in such a way that the individuals represent the larger group from which they were selected. The act, process, or technique of selecting a suitable sample, or a representative part of population for the purpose of determining parameters or characteristics of the whole population In short, sampling is the process of choosing samples from a given population. Sampling frame – It is the physical material from which samples are drawn (chosen). A map, telephone Director, a list of workers in the payroll system are all examples of frame..

Cont’d. Why sampling Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population..

Cont’d. Reasons for Sampling Sampling can save money. Sampling can save time. A sample may provide you with needed information quickly. For example, you are a Doctor and a disease has broken out in a village within your area of jurisdiction, the disease is contagious and it is killing within hours nobody knows what it is. You are required to conduct quick tests to help save the situation. The destructive nature of the observation, the sample can save product. Good examples of this occur in quality control. For example to test the quality of a fuse, to determine whether it is defective, it must be destroyed. To obtain a census of the quality of a lorry load of fuses, you have to destroy all of them. This is contrary to the purpose served by quality-control testing. In this case, only a sample should be used to assess the quality of the fuses If accessing the population is impossible; sampling is the only option. There are some populations that are so difficult to get access to that only a sample can be used. Like people in prison, like crashed aeroplanes in the deep seas, presidents e.t.c . The inaccessibility may be economic or time related. Accuracy: A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample..

Cont’d. Reasons for Taking a Census Eliminate the possibility that a random sample is not representative of the population. The person authorizing the study is uncomfortable with sample information. Moreover , you might sample the entire population. —When your population is very small. —When you have extensive resources. —When you don’t expect a very high response..

Variables. Any aspect of an individual that is measured and take any value for different individuals or cases, income, demand, or records, like age, sex is called variables. Two types of variables: qualitative (categorical) or quantitative (numerical) variables.

Cont’d. Qualitative Variables are nonnumeric variables and can't be measured. Examples: gender, religious affiliation, and state of birth. Quantitative Variables are numerical variables and can be measured. Examples include balance in checking account, number of children in family. Quantitative variables are two types: Discrete variable are variables which can assume only certain values, and there are usually "gaps" between the values, such as the number of bedrooms in your house, number of households in a kebele , number of VAT registered cafés’ in each Sub city at Addis Ababa. Continuous variables are variables which can assume any value within a specific range, such as income, weight, age of household head etc..

Variable Measurements level. You can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string (alphanumeric) or numeric. Nominal variable: A variable can be treated as nominal when its values represent categories with no intrinsic ranking No arithmetic and relational operation can be applied. Examples Nominal variable : o Political party preference (Republican, Democrat, or Other,) o Sex (Male or Female.) o Marital status(married, single, widow, divorce) o Country code o Regional differentiation of Ethiopia ..

Cont’d. Ordinal Variable: A variable can be treated as ordinal when its values represent categories with some intrinsic ranking. Level of measurement which classifies data into categories that can be ranked. Differences between the ranks do not exist . Arithmetic operations are not applicable but relational operations are applicable. Ordering is the sole property of ordinal scale ..

Cont’d. Examples of Ordinal variables : Letter grades (A, B, C, D, F). Rating scales (Excellent, Very good, Good, Fair, poor). Rate of satisfaction (very satisfied, satisfied, less than satisfied, very unsatisfied) Socio- economic class (low, middle, high) Country status (Undeveloped, developing, developed).

Cont’d. Interval Scales are measurement systems that possess the properties of Order and distance, but not the property of fixed zero. Level of measurement which classifies data that can be ranked and differences are meaningful. However, there is no meaningful zero, so ratios are meaningless. Relational operations are also possible Examples of Interval scales : o Temperature.

Cont’d. Ratio scales are measurement systems that possess all three properties: order, distance, and fixed zero. Level of measurement which classifies data that can be ranked, differences are meaningful, and there is a true zero. True ratios exist between the different units of measure. All arithmetic and relational operations are applicable. Examples of Ratio scales : o Weight o Height o Number of students.

Cont’d. But SPSS does not differentiate between interval and ratio levels of measurement, both of these quantitative variable types are lumped together as " scale ”..