EDP 201 Research Methodology SOKOINE UNIVERSITY OF AGRICULTURE

EDP 201: Research Methodology. SOKOINE UNIVERSITY OF AGRICULTURE FACULTY OF SCIENCE DEPARTMENT OF EDUCATION

Module 2: Educational research strategies 2. 1. Theory and research • A theory is a set of interrelated concepts, definitions, and propositions that explains or predicts events or situations by specifying relations among variables. • A theory is a generalization about a phenomenon or behaviour, an explanation of how or why something or behavior occurs. Functions (Role) of Theory • Theory as orientation: Theoretical system narrows the range of facts to be studied. Any phenomenon or object may be studied in many different ways.

• Theory as a conceptualization and classification. Theory provides the relationship between concepts which are stated in "the facts of science. " • Theory in summarizing role. The theory summarizes concisely what is already known about the object of study, through (1) empirical generalizations, and (2) systems of relationships between propositions. • Theory predicts facts. The theory summarizes facts and states a general uniformity beyond the immediate observation, it also becomes a prediction of facts. The most obvious face of prediction is the extrapolation from the known to the unknown. • Theory points gaps in knowledge. Since theory summarizes the known facts and predicts facts which have not been observed, it must also point to areas which have not yet been explored.

Role of Facts (Research) Theory and fact are in constant interaction. Developments in one may lead to developments in the other. a) Facts initiate theory. Science(research) lead to important formulation of theories. This is what the public thinks of as a "discovery. " b) Facts lead to the rejection and reformulation of existing theory. Since research is continuing activity, rejection and reformulation are likely to be going on simultaneously. c) Facts redefine and clarify theory. New facts that fit theory will always redefine theory, for they state in detail what theory states in very general terms. They clarify that theory, for they throw further light upon its concepts.

Theory and Research: the dynamism • Theory and research are interrelated. The value of theory and its necessity for conducting good research should be clear. Researchers who proceed without theory rarely conduct top-quality research and frequently find themselves in confusion. Likewise, those who proceed without linking theory to research are in jeopardy of floating off into incomprehensible speculation and conjecture (assumption).

2. 2. Epistemological and ontological considerations Ontology is a branch of philosophy (metaphysics) concerned with the nature and relations of being (reality). It is a particular theory about the nature of being or the kinds of existents (Wand Weber, 1993: 220). It is about the nature and structure of the world. The ontological questions • What is the form and nature of reality (what is reality? ) • What is there that can be known about it? • What are the characteristics of things that exist? . For example, if someone has a positivist view of the world, the ontological assumptions would be based on the existence of social phenomena independent of our perceptions and values. From this perspective everything in the social world would be meaningless and would exist in the same way as physical objects. •

b) Epistemology is therefore the relationship between the researcher and the reality (Carson et al. , 2001) or how this reality is captured or known. The epistemological questions • What is the nature of the relationship between the knower and what can be known? • What Constitutes Valid Knowledge and How Can We Obtain It? • 'How do we acquire knowledge? ' "How do we know what we know? There are two dominant ontological and epistemological traditions or ideologies: 1) Positivism, 2) Interpretivism.

Why are Ontology and Epistemology important? • Ontology precedes epistemology: Whenever we do research on a social phenomenon, we would have to start with some ontological views about how reality exists, and those views, beliefs would subsequently lead us to some sort of knowledge. • According to how we will perceive the existence of the world, our search for knowledge will follow a certain path (method). The ontological and epistemological considerations are our starting point to study a certain topic. • While ontology refers to the nature of knowledge and reality, epistemology concerns the very basis of knowledge-whether this is hard, real, and transmittable in a concrete form, or whether it is softer and more subjective, based on personal experience and insight (Cohen, Manion and Morrison, 2006). •

• Epistemology raises many questions including the relationship between the knower and what is known, the characteristics, the principles, the assumptions that guide the process of knowing and the achievement of findings.

Module 3: The quantitative research approach 3. 1. Philosophical underpinnings Positivism (scientific paradigm) • The ontological position of positivism is one of realism. Realism is the view that objects have an existence independent of the knower (Cohen et al. , 2007, p. 7). Thus, most positivists assume that reality is not mediated by our senses. • The positivist epistemology is one of objectivism. Positivists go forth into the world impartially, discovering absolute knowledge about an objective reality. The researcher and the researched are independent entities.

• • Positivists usually use quantitative methods as research tools, as these are objective and the results generalizable and replicable. They look for explanation of behaviour, not for the meaning. Positivist methodology is directed at explaining relationships. Positivists attempt to identify causes which influence outcomes (Creswell, 2009, p. 7). Their aim is to formulate laws, thus yielding a basis for prediction and generalization. Thus, a deductive approach is undertaken. Correlation and experimentation are used to reduce complex interactions their constituent parts. Verifiable evidence sought via direct experience and observation.

The Interpretive Paradigm a) The ontological position of interpretivism is relativism. Relativism is the view that reality is subjective and differs from person to person (Guba & Lincoln, 1994, p. 110). Our realties are mediated by our senses. Reality is individually constructed; there as many realities as individuals. b) The interpretive epistemology is one of subjectivism which is based on real world phenomena. Knowledge and meaningful reality are constructed out of interaction between humans and their world.

a) Interpretive methodology is directed at understanding phenomenon from an individual’s perspective, investigating interaction among individuals as well as the historical and cultural contexts. It generates qualitative data. b) It uses inductive process that is qualitative in nature. The interpretivism uses inductive reasoning, starting from specific observations that is repeated and then drawing a general conclusion based on these; thus, moving from the particular to the general

3. 2. Research designs • Quantitative research is a anobjective, systematic process for obtaining information about the world by collecting quantitative data through statistical analysis. The goal of quantitative research is to test relationships, describe, examine cause and effect relations • Research design is the structure of any scientific work. It gives direction and systematizes the research process from formulating research question to the conclusion of the process. • True- experimental design • It is the most accurate form of experimental research, which attempts to prove or disprove a hypothesis mathematically, with statistical analysis. Its purpose is to determine cause and effects. For an experiment to be classed as a true experimental design, it must fit all of the following criteria.

a) The sample groups must be assigned randomly. b) There must be a viable control group. c) Only one variable can be manipulated and tested. It is possible to test more than one, but such experiments and their statistical analysis tend to be cumbersome and difficult. d) The tested subjects must be randomly assigned to either control or experimental groups. Advantages of experimental research • The results of a true experimental design can be statistically analyzed and so there can be little argument about the results. • It is also much easier for other researchers to replicate the experiment and validate the results.

• For physical sciences working with mainly numerical data, it is much easier to manipulate one variable, so true experimental design usually gives a yes or no answer. Disadvantages of experimental research • Firstly, they can be almost too perfect, with the conditions being under complete control and not being representative of real world conditions. • Secondly, there can be never be any guarantee that a human or living organism will exhibit ‘normal’ behavior under experimental conditions. • True experiments can be too accurate and it is very difficult to obtain a complete rejection or acceptance of a hypotheses because the standards of proof required are so difficult to reach.

• True experiments are also difficult and expensive to set up. They can also be very impractical. • While for some fields, like physics, there are not as many variables so the design is easy, for social sciences and biological sciences, where variations are not so clearly defined it is much more difficult to exclude other factors that may be affecting the manipulated variable. Quasi- experimental design • Quasi-experimental design is a form of experimental research used extensively in the social sciences and psychology. The method is, nevertheless, a very useful method for measuring social variables. • It involves selecting groups, upon which a variable is tested, without any random pre-selection processes. For example, to perform an educational experiment, a class might be arbitrarily divided by seating arrangement.

Advantages of quasi-experimental research a) Especially in social sciences, where pre-selection and randomization of groups is often difficult, they can be very useful in generating results for general trends. E. g. if we study the effect of maternal alcohol use when the mother is pregnant. This would be highly illegal because of the possible harm the study might do to the embryos. b) Quasi-experimental design is often integrated with individual case studies; the figures and results generated often reinforce the findings in a case study, and allow some sort of statistical analysis to take place. c) In addition, without extensive pre-screening and randomization needing to be undertaken, they do reduce the time and resources needed for experimentation.

Disadvantages of quasi-experimental research a) Without proper randomization, statistical tests can be meaningless. For example, these experimental designs do not take into account any pre-existing factors (as for the mothers: what made them drink or not drink alcohol), or recognize that influences outside the experiment may have affected the results. b) Due to lack of researcher control, a quasi experiment can produce the results which will not stand up to rigorous statistical scrutiny because the researcher also needs to control other factors that may have affected the results. This is really hard to do properly. c) One group of children may have been slightly more intelligent or motivated. Without some form of pre-testing or random selection, it is hard to judge the influence of such factors.

Non-experimental designs Correlational research design • A correlational study determines whether or not two variables are correlated or related. This means to study whether an increase or decrease in one variable corresponds to an increase or decrease in the other variable. There are three types of correlations that are identified: • Positive correlation: Positive correlation between two variables is when an increase in one variable leads to an increase in the other and a decrease in one leads to a decrease in the other. For example, the amount of money that a person possesses might correlate positively with the number of cars he owns.

• Negative correlation is when an increase in one variable leads to a decrease in another and vice versa. For example, the level of education might correlate negatively with crime. This means if by some way the education level is improved in a country, it can lead to lower crime. . • No correlation: Two variables are uncorrelated when a change in one doesn't lead to a change in the other and vice versa. For example, if happiness is found to be uncorrelated to money. This means an increase in money doesn't lead to happiness. A correlation coefficient is usually used during a correlation study. It varies between +1 and -1. A value close to +1 indicates a strong positive correlation while a value close to -1 indicates strong negative correlation. A value near zero shows that the variables are uncorrelated. • •

Limitations • It is very important to remember that correlation doesn't imply causation and there is no way to determine or prove causation from a correlational study. This is a common mistake made by people in almost all spheres of life. Descriptive research design • This is a scientific method which involves observing and describing the behaviour of a subject without influencing it in any way. Thus, descriptive research design allows observation without affecting normal behaviour

Advantages of descriptive research design • The subject is being observed in a completely natural and unchanged natural environment. • Descriptive research is often used as a pre-cursor to quantitative research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively. • It gives researcher an opportunity to use both quantitative and qualitative data in order to find data and characteristics about the population or phenomenon. • Data collected for descriptive research can provide a very multifaceted approach. Data can include case studies (which provide more personal account 0; observations and surveys (which can give statistics)

Disadvantages of descriptive research design • Because there are no variable manipulated, there is no way to statistically analyze the results. Pre-existing differences may be a plausible alternative explanation for any observed differences on the dependent variable of interest. • The observed results are not repeatable, and so there can be no replication of the experiment and reviewing of the results. • Descriptive research design is weak in its ability to reveal causal relationships • Objectivity and errors; there is a possibility of errors and subjectivity. For example researcher may records what he wants to hear and ignore data which does not conform to his research hypotheses.

Causal-comparative research attempts to establish causeeffect relationships among the variables of the study. It attempts to determine the cause or consequences of differences that already exist between or among groups of individuals. a) An independent variable is identified but not manipulated by the experimenter. b) The researcher does not randomly assign groups and must use ones that are naturally formed or pre-existing groups

• For example, a researcher measured the mathematical reasoning ability of young children who had enrolled in Montessori schools and compared the scores with a group of similar children who had not been to Montessori schools Advantages of causal-comparative research i. Causal-comparative research design can be defined as a research that permits researchers to study naturally occurring, cause and effect relationship through comparison of data from participant groups who exhibit the variables of interest ii. Uncovers relationships to be investigated experimentally. iii. Used to establish cause-effect when experimental design not possible. iv. Less expensive and time consuming than experimental research.

i. Casa-comparative allows us to study cause-and effect relationship under conditions where experimental manipulation is difficult or impossible. Therefore socially and ethically relevant. ii. Many relationships can be studied in a single research study. For example, students socio-economic background and gender on their academic achievement. iii. Less expensive and time consuming than experiments Limitations of causal-comparative research a) Subject Characteristics b) It is difficult in finding or creating Homogeneous Subgroups c) Researcher has limited control over the study and thus, extreme caution must be applied in interpreting results. The 3 rd variable may affect the results.

3. 3. Problem formulation and development of hypotheses • The formulation of a research problem is the first and most important step of the research process. This is more like identifying a destination prior to beginning a journey. a) Problem formulation can be a question or something you are wondering about. b) It should be something you can solve or give answers to c) A conclusion in report is always the answer to the problem formulation There are different types of problem formulation a) The descriptive problem formulation- always describes the problem (What). For example “ A school has trouble integrating new employees/teachers.

• So problem formulation could be [What kind of the problems appear for the school and the employees in the beginning of the employment? ] Why It is the explanatory problem formulation- it wants to explain a problem. For example, why does it seem that the employees/teachers have trouble adapting the culture in the school? How It is the normative problem formulation- it tries to solve a problem. For example, how can the school do to ensure that new employees get all the support they need? Hypotheses A problem formulation can be in the form of a hypotheses. E. g the school is hiring the wrong kind of people

Considerations in selecting and formulating research problem • Interesting – keeps the researcher interested in it throughout the research process • Researchable (data availability) – can be investigated through the collection and analysis of data • Significant (relevance) – contributes to the improvement and understanding of educational theory and practice • Manageable (Expertise) – fits the level of researcher’s level of research skills, needed resources, and time restrictions • Ethical – does not embarrass or harm participants

Development of hypotheses Hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable (Creswell, 1994). They are tentative guesses, intended to be given a direct experimental test when possible. Hypotheses allows to: a) Identify the research objectives b) Identify the key abstract concepts involved in the research c) Identify its relationship to both the problem statement and the literature review Types of hypotheses • Null hypotheses • Positive hypotheses

• Null hypotheses states no relationship/difference exists between variables and Statistical test is performed on the null. It is assumed to be true until support for the research hypothesis is demonstrated. For example, there is no significance difference in academic achievement between students from different socio-economic background at SUA. • Alternative hypothesis is a statement that suggests a potential outcome that the researcher may expect. For example, there is significance difference in academic achievement between students from different socioeconomic background at SUA.

• Directional hypothesis specifies the direction of the relationship between independent and dependent variables • Non-directional hypothesis shows the existence of a relationship between variables but no direction is specified Characteristics of research hypotheses a) Testable and measurable; hypotheses can be tested – verifiable or falsifiable b) Hypotheses are not moral or ethical questions c) Prediction: hypothesis predicts the anticipated outcome of the experiment d) Hypotheses gives insight into a research question e) Describe clearly, and provide identification of the most important variables in operational terms.

• Hypotheses specify expected relationships among independent, and control variables. • Hypotheses are value free in the sense that they exclude the personal biases of the researcher. 3. 4. Sampling and sampling techniques Definitions and conceptualizations • Sampling is the process of selecting participants from the population. • The target population is the total group of individuals from which the sample might be drawn and generalizations are made. • Sampling frame is a list of subjects from which a sample of subjects is selected • Population is the entire set of individuals or other entities to which study findings are to be generalized.

• Sample is a subset of a population that is used to study the population as a whole. • Elements is the individual members of the population whose characteristics are to be measured. In quantitative research, a sample is normally picked using accepted statistical methods based on the laws of probability. Some of the common methods used in probability or quantitative sampling are; a) b) c) d) e) Simple random sampling Cluster sampling Systematic sampling Stratified sampling Multi-stage sampling

• The primary goal of sampling (why sample) a) researcher can study the smaller group and produce accurate generalizations about the larger group. b) Quantitative researchers tend to use a type of sampling based on theories of probability from mathematics, called probability sampling. c) In reality there is simply not enough; time, energy, money, labour/man power, equipment, access to suitable sites to measure every single item or site within the parent population or whole sampling frame. d) Therefore an appropriate sampling strategy is adopted to obtain a representative, and statistically valid sample of the whole.

Types of sampling Probability Sampling is sampling technique in which each unit in a population has a specifiable chance of being selected. Non-probability sampling is based on human choice rather than random selection. Population unit has no specifiable chance of being selected. Simple Random Sampling • A simple random sampling is a sample of a given size in which all such subsets of the frame are given an equal probability to be chosen. Each member of the population has an equal and known chance of being selected.

• Advantages of Simple random sampling a) One of the great advantages of simple random sampling method is that it needs only a minimum knowledge of the study group of population in advance. b) It is free from errors in classification. c) This is suitable for data analysis which includes the use of inferential statistics. d) Simple random sampling is representative of the population e) It is totally free from bias and prejudice f) The method is simple to use. g) It is very easy to assess the sampling error in this method.

Disadvantages of Simple random sampling a) This method carries larger errors from the sample size than that are found in stratified sampling. b) In simple random sampling, the selection of sample becomes impossible if the units or items are widely dispersed. c) One of the major disadvantages of simple random sampling method is that it cannot be employed where the units of the population are heterogeneous in nature. d) This method lacks the use of available knowledge concerning the population. Sometimes, it is difficult to have a completely cataloged universe.

• In a simple random sample of a given size, all such subsets of the frame are given an equal probability (minimizes bias). Each element has an equal probability of selection. • However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population Multi-stage sampling • Multi-stage sampling is a more complicated form of cluster sampling in which larger clusters are further subdivided into smaller, more targeted groupings for the purposes of study. • This is sampling within previously sampled clusters. Next, the investigator identifies which elements to sample from within the clusters

• Advantages of multi-stage sampling a) Multi-stage sampling can be easier to implement and can create a more representative sample of the population than a single sampling technique. b) Multi-stage sampling gives researchers with limited funds and time a method to sample from such larger populations(simplification and cost consideration) c) The multi-stage form of sampling is flexible in many senses. it allows researchers to employ random sampling or cluster sampling after the determination of groups. there are no restrictions on how researchers divide the population into groups.

a) Due to the fact that multi-stage sampling cuts out portions of the population from the study, the study's findings can never be 100% representative of the population (possibility of lost data). b) The method however, may be considered overlyexpensive or time consuming for the investigator. Stratified Sampling • Stratified Sampling is a method which makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. These groups are called strata. An individual group is called a stratum.

Advantages of stratified sampling a) With fairly homogeneous groups, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample. b) It ensures a high degree of representativeness of all the strata or layers in the population. Therefore we can generalize from the results obtained (even minority can be represented proportionally). c) The results are more accurate because sampling error is reduced because of the grouping of similar units. d) Stratification permits separate analyses on each group and allows different interests to be analyzed for different groups. .

Disadvantages of stratified sampling a) Gathering such a sample would be extremely time consuming and difficult to do (tedious). b) Also, it may be difficult in some populations to divide into strata especially when some characteristics (criteria) are unknown to researcher.

Systematic Sampling • Systematic Sampling chooses subjects in a systematic (i. e. orderly / logical) way from the target population, like every nth participant on a list of names. • By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call nth- case. • in systematic sampling , a random start, and then selecting elements at regular intervals through that ordered list. • The advantage of systematic sampling a) it ensures a high degree of representativeness, and no need to use a table of random numbers

a) Systematic random sampling is often easier to use than simple random sampling, especially for large samples, as only one random number (the random start) is required, Disadvantages of systematic sampling a) The method is less random than simple random sampling

Cluster Sampling is when population is divided into (geographical) clusters where some clusters are chosen at random - within cluster units are randomly chosen or sampled. Thus, each cluster should be heterogeneous. • Thus, the statistical analysis used with cluster sampling is not only different, but also more complicated than that used with stratified sampling. • Advantages; firstly, it is more cost-effective to select respondents in groups ("clusters"). Clustering can reduce travel and administrative costs. Secondly, it also means that one does not need a sampling frame listing all elements in the target population. Thirdly, sometimes it is too expensive to make a complete list of all the elements of the population that we want to study. Fourthly, this sampling method has the advantage that it simplifies the collecting of the sample information. •

• Finally, cluster sampling addresses two problems especially when researchers lack a good sampling frame for a geographically dispersed population and when the cost to reach a sampled element is very high. • The main disadvantage is that if the clusters are not homogeneous among them, the final sample may not be representative of the population. Thus, cluster sampling may generally increase the variability of sample estimates

3. 5. Instrumentation and data collection • Research instrument is the device that researchers use for a measurement purpose (survey, test, questionnaire, etc. ). • Instrumentation is the course of action or the process of developing, testing, and using the device. • The instrumentation plan is composed of a number of decisions to include; a) What data are needed to answer the research questions, b) How to gather the data, c) When to gather the data, d) Where to gather the data, and e) How to analyze the data.

• Data collection refers to assembling data required to answer the research question from respondents, using instruments. • Data it is a collection of information, in the form of numerical measures of respondents’ attributes (in quantitative research) or , texts, voices, or images (in qualitative research). Methods of data collection in quantitative research • Questionnaires • Structured Interview • Structured Observations • Using scales • Testing

Questionnaire (also called survey) • A questionnaire is a data collection instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. The questionnaire was invented by Sir Francis Galton. Steps required to design and administer a questionnaire • Defining the Objectives of the Study • Define the target respondents and methods to reach them. • Questionnaire Design • Pilot Testing • Questionnaire Administration • Results Interpretation

Advantages of questionnaires • They can reach a large number of people relatively easily and economically. � • They provide quantifiable answers. � Relatively easy to analyze. � • Less time consuming than interview or observation (Bailey, 1982) • They are relatively quick and inexpensive. • In the absence of an interviewer standing over them, respondents are more likely to answer truthfully or give answers to personal questions. • Questions are structured and asked in the same way. So that respondents’ answers an be more easily (standadized)

Disadvantages of questionnaires • Questions may be misunderstood or missed out (inflexibility). • It may be filled in by the person for whom it was not intended. • The response rate is usually low. • Respondents are forced to choose between alternatives answers provided especially with closed-ended questions in structured questionnaires. Structured interviews • A structured interview (also known as a standardized interview) is a quantitative research method commonly employed in survey research. The instrument is intended to ensure that each interview is presented with exactly the same questions in the same order.

Characteristics of the Structured Interview • The interviewer asks each respondent the same series of questions. • The questions are created prior to the interview, and often have a limited set of response categories. • There is generally little room for variation in responses and there are few open-ended questions included in the interview guide. • Questioning is standardized and the ordering and phrasing of the questions are kept consistent from interview to interview. • The interviewer plays a neutral role and acts casual and friendly, but does not insert his or her opinion in the interview.

Strengths of structured interviews • Structured interviews can produce consistent data that can be compared across a number of respondents. • All respondents are asked the same questions in the same way. This makes it easy to repeat (“replicate”) the interview. In other words this type of research method is easy to standardize • They provides a reliable source of quantitative data. • The researcher is able to contact large numbers of people quickly, easily and efficiently. • The structured interview can be easily repeated to check the reliability of the data because standardized questions allow all respondents answer the same questions

Limitations of the structured interviews • The format of questionnaire design makes it difficult for the researcher to examine complex issues and opinions. . • There is limited scope for the respondent to answer questions in any detail or depth. Even where open-ended questions are used, the depth of answers the respondent can provide tend to be more limited than with almost any other method • The interview effect – the personality of the interviewer may influence what answers are given by the interviewee. This may make the results unreliable.

Structured observation is a quantitative method of data gathering and evaluation, which uses observation schedules and predetermined behavioral categories. • The observational method of research concerns the planned watching, recording, and analysis of observed behavior as it occurs in a natural setting. Strengths of structured observation • Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability.

• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i. e. numerical) - making this a less time consuming method compared to naturalistic observations • Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. • It may allow control of extraneous variables Limitations of structured observation • Controlled observations can lack validity due to the Hawthorne effect/demand characteristics. When participants know they are being watched they may act differently and affect their behavious. •

• It can influence observer bias or effect • It is not possible to know intentions behind behaviors. Test as research instrument • Test is a means of measuring the knowledge, skills, feelings, intelligence or aptitude of an individual or group. • Tests can be used to produce numerical scores that can be used to identify, classify or evaluate test takers. (Gay, 1996). One form of testing you may be familiar with as a student is an exam. • Types of tests v Norm-reference tests are those which produce a score that tells how individuals performance compares with other individuals.

• It describes performance such as achievement, in relative terms. For example, How does the overall achievement of students in class A compares to the students in class B. v Domain-reference test are those which measure the learners absolute level of performance in a precisely defined content area or domain. They estimate individuals domain status (in the domain covered by a test) For example, what percent of addition, problems john can solve correctly. v. Individual versus group tests. A group test is designed so that a sample of subjects can take the test all at one time whereas individual test measures one individual at a time. v. Standardized tests are constructed by experts and they are characterized by the following; a) Involves individual test items revised analyzed to meet standards of quality.

a) Directions for carrying out tests are readily available b) The standardized tests are objective Types of standardized tests a) Intelligence tests for example Wechsler adult intelligence scale (suitable for testing late adolescents and adults) b) Aptitude tests such as modern language aptitude test c) Achievement test such as wide range achievement test (in areas of reading, spelling and arithmetic) d) Diagnostic test such as stand ford diagnostic reading test. e) Attitude scale such as Thurston type scale (individual expresses agreement or disagreement with a series of statements about attitude object f) Measures of vocational interests such as career assessment inventory g) Self-report measures of personality such as checklist. A good example is adjective checklist (measures adjectives such as imaginative, stubborn, relaxed etc.

Advantages of test instrument v. Tests yield information on personality traits, emotional states, aptitudes, abilities. v. Test provide accurate performance indicator when used appropriately. They can provide an accurate snapshot of how well students are performing in various subjects against a national comparison group. v. Standardized testing results are quantifiable. Thus, the test results can provide information regarding an examinee's areas of strength and weakness. v. They are relatively cheap and easy to administer when compared to other approaches (cost-effective).

v Test results allow researcher to compare students in terms of their skills or knowledge. v Standardized tests are practical, they're easy to administer, and they consume less time to administer versus other assessments. Limitations v. Not all skills are measured equally; No achievement test, no matter how unbiased it seems, can equally measure what children learn. Additionally, the multiple choice format of such tests measures knowledge and skills, not creativity and problem-solving v. It is difficult to construct tests that are reliable and valid. Questions about Fairness; although public attitudes remain broadly supportive of standardized testing as a concept, questions persist whether the scores reflect a level playing field. .

v There are numerous tests on the market. Unfortunately, it is very difficult for untrained people to distinguish these from good instruments v Standardized test items are not parallel with typical classroom skills and behaviors (frequently are unrelated to those tasks and behaviors required in the classroom setting. Thus, most items assess general knowledge and understanding.

Data collection • Data collection methods in educational research are used to gather information that is then analyzed and interpreted. As such, data collection is a very important step in conducting research and can influence results significantly. Why is Level of Measurement Important? v. First, knowing the level of measurement helps you decide how to interpret the data from that variable. When you know that a measure is nominal (like the one just described), then you know that the numerical values are just short codes for the longer names. v. Second, knowing the level of measurement helps you decide what statistical analysis is appropriate on the values that were assigned. If a measure is nominal, then you know that you would never average the data values or do a t-test on the data.

There are typically four levels of measurement that are defined: (Nominal , Ordinal , Interval and Ratio). v Levels of measurement imply that there are the different ways numbers can be used. v. Nominal level; In nominal measurement the numerical values just "name" the attribute uniquely. No ordering of the cases is implied. • Numbers can be used as tags or labels, where the size of the number is arbitrary. For example, arbitrary numbers to code variables such as religion, ethnicity or gender. • Can assign number codes but calculations would be meaningless

• Ordinal level • In ordinal measurement the attributes can be rank-ordered. Here, distances between attributes do not have any meaning. For example, on a survey you might code Educational Attainment as 0=less than high school; 1=some high school. ; 2=high school degree; 3=some college; 4=college degree; 5=post college. In this measure, higher numbers mean more education. But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is not interpretable in an ordinal measure • In interval measurement the distance between attributes does have meaning. For example, when we measure temperature (in Fahrenheit), the distance from 30 -40 is same as distance from 70 -80. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable, where it doesn't make sense to do so for ordinal scales. But note that in interval measurement ratios don't make any sense

• Ratio level • in ratio measurement there is always an absolute zero that is meaningful. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable. • Thus, with true zero point, ratio can make assumptions about the ratio of two measurements. 6 grams is twice as much as 3 grams because you can have zero weight. • Thus, in collecting quantitative data, the following features that are key to quantitative studies v. All variables must be measurable v. All data collection procedures must be objective v. All data collection procedures must be able to be duplicated (replicable)

When do we use quantitative methods? v. The first type of research question is that demanding a quantitative answer. Examples are: ‘How many students choose to study education? ’ or ‘How many Maths teachers do we need and how many have we got in our school district? ’ v. Numerical change can likewise accurately be studied only by using quantitative methods. Is achievement going up or down? We’ll need to do a quantitative study to find out. v. As well as wanting to find out about the state of something or other, we often want to explain phenomena. What factors predict the recruitment of Maths teachers? What factors are related to changes in student achievement over time? v. The final activity for which quantitative research is especially suited is the testing of hypotheses. Using quantitative research, we can try to test this kind of model.

3. 6. Data analysis techniques There are two major types of analysis in quantitative research studies v. Descriptive Statistics is when statistics used to describe a study’s sample or population. The common descriptive statistics include measures of the central tendency and dispersion) v. Inferential Statistics are used to determine the probability that a relationship (between, say, two variables) found in the study sample also exists within the population from which the sample was drawn. The common inferential statistics are the Analysis of Variance (ANOVA), t-test and regression.