Research Method & Design

Research Method & Design By: RAZIEH TADAYON NABAVI Master of Science (Developmental Psychology)

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Table of content Introduction................................................................................................................................................... 3 Part one ......................................................................................................................................................... 3 Question number 1......................................................................................... Error! Bookmark not defined. Question number 2......................................................................................... Error! Bookmark not defined. Question number 3......................................................................................... Error! Bookmark not defined. Question number 4......................................................................................... Error! Bookmark not defined. Part two ...................................................................................................................................................... 17 Question number 1......................................................................................... Error! Bookmark not defined. Question number 2......................................................................................... Error! Bookmark not defined. Question number 3......................................................................................... Error! Bookmark not defined. References: .................................................................................................................................................. 35

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Introduction For elaborating my answers first of all I tried to bring some statements, ideas, findings and belifs from scholars that stated in their text books and articles then I focused on the exact answers.

Part one

Briefly describe the different steps involved in a research process. The following seven steps as an outline of simple and effective strategy for a research we’ree suggested by Ary, Jacobs and Razavieh (2005):

1- Choose the topic As we know a title should be include some conditions. 

It should be short, clear and precise.

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It should be investigative in nature, like the theme of a seminar.

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It points to the problem of a study and suggests the scope of problem to be investigated.

They also suggested that there are some ways to select topics: 1- Personal experience/ interest 2- Curiosity based on something in the media 3- The state of knowledge in the field 4- Solving a problem 3

5- Social premiums 6- Personal values 7- Everyday life 8- Practicality 9- Contribution to knowledge

2- Focus on research question: 

All research projects are built on the foundation of research questions.

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Research question define the nature and scope of a research project.

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We do research to get answe’res to questions; therefore to do research, we must start with a research questions that can be answe’reed.

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We limit the research questions to three types- what, why and how based on three research objectives description, explanation, understanding and change.

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Questions of a theoretical nature are those asking, “What is it?” or “How does it occur?” or “Why does it occur?” Research with a theoretical orientation may focus on either developing new theories or testing existing theories.

3- Design study: The investigator next plans how to conduct research to answe’re the question. The design is the researcher’s plan for the study, which includes the method to be used, what data will be gathered, where, how, and from whom. Quantitative researches maintain that once this research plan is set forth, it must be followed. Unhypothesized observed relationships among variables may be reported and proposed as topics for future research, but they should not replace the original intent

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of the study. In qualitative research the design is flexible and may change during the investigation if appropriate. The design of qualitative research is thus often described as “emergent”.

4- Collect data: The next step involves executing the research plan. Quantitative researchers use a wide variety of instruments to gather data, including tests, questionnaires, ratings, attitude scales, and so on. Qualitative researchers also have a toolbox of data- gathering techniques, including in-depth interviewing, participant observation, and document analysis.

5- Analyze data: The data collected in research must be analyzed. Quantitative data are usually in the form of numbers that researchers analyze using various statistical procedures. Even variable data, such as compositions written by high school students, would be converted through be scoring process to a numerical form. The analysis of the numerical is quantitative research provides evidence that supports or fails to support the hypothesis of the study. Quantitative data generally take the form of words (descriptions, observations, impressions, recordings and the like). The researcher must organize and categorize or code the large mass of data so that they can be described and interpreted. Although the qualitative researcher does not deal with statistics, analyzing qualitative data is not easy. It is a time-consuming and painstaking process.

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6- Interpret data The researcher next tries to interpret the findings in terms of the research problem. The quantitative researcher typically makes statements about the probability. That such a finding is due to chance and reaches a conclusion about the hypothesis. Quantitative researchers present their interpretations and explanations in narrative form. They do not talk about probability but try to emphasize the trustworthiness and credibility of the findings.

7- Inform others There are many ways for informing others about research. Such as: workshops, publishing in journals, internet, presenting articles, seminars.

Write short notes on criteria of good research. Characteristics of a good research: Scholars such as Kerlinger,(1986), Rosenthal, & Rosnow,(1991), Heppner, Kivlighan, & Wampold (1999), LaFountain,& Bartos, (2002), Ary, Jacobs and Razavieh (2005) and Godin (2007) in different text books and articles suggested different characteristics of a good research. Whatever may be the types of research works and studies, one thing that is important is that they all meet on the common ground of scientific method employed by them. The followings steps are the summery of their beliefs. One expects scientific research to satisfy the following criteria:

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(1) The purpose of the research should be clearly defined and common concepts be used. (2) The research procedure used should be described in sufficient detail to permit another researcher to repeat the research for further advancement, keeping the continuity of what has already been attained. (3) The procedural design of the research should be carefully planned to yield results that are as objectives as possible. (4) The researcher should report with complete frankness, flaws in procedural design and estimate their effects upon the findings. (5) The analysis of data should be sufficiently adequate to reveal its significance and the methods of analysis used should be appropriate. The validity and reliability of the data should be checked carefully. (6) Conclusions should be confined to those justified by the data of the research and limited to those for which the data provide an adequate basis. (7) Greater confidence in research is warranted if the researcher is experienced, has a good reputation in research and is a person of integrity.

LaFountain,& Bartos, (2002) pointed that the qualities of a good research are as under: 1. Good Research is Systematic: It means that research is structured with specified steps to be taken in a specified sequence in accordance with the well defined set of rules. Systematic characteristic of the research does not rule out creative thinking but it certainly does reject the use of guessing and intuition arriving at conclusions.

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2. Good Research is Logical: This implies that research is guided by the rules of logical reasoning and the logical process of induction and deduction are of great value in carrying out research. Induction is the process of reasoning from a part to the whole whereas deduction is the process of reasoning from the premise. In fact, logical reasoning makes research more meaningful in the context of decision making. 3. Good Research is Empirical: It implies that research is related basically to one or more aspects of a real situation and deals with concrete data that provides a basis for external validity to research results. 4. Good Research is Replicable: This characteristic allows research to be verified by replicating the study and thereby building a sound basis for decisions. Based on the above evidences and according to Ary, Jacobs and Razavieh (2005) and Godin (2007) I want to mention the characteristics of a good research as follow: 1- It should be clearly structured with appropriate sub-headings to provide a precise picture or problem, the objectives and method investigation. 2- It should be related to some theoretical base to provide the basics of analysis. 3- Relevant concepts should be clearly defined within the context of the study. I mean that a suitable diagrammatic presentation of the conceptual framework is always helpful to researcher as well as it is convincing to the reader. 4- The problem to be investigated should be clearly stated and the objectives should directly reflect the problem and show how they could answe’re the problem. The objectives should also be clear and attainable. 8

5- It should have a selection which shows the significance of the study, I mean that how the expected findings are going to benefit the human society in general or any specific party in particular.

Give your interpretation and elaboration on the following definition of science. “Science is a objective, accurate, systematic analysis of a determine body of empirical data, in order to discover recurring relationships among phenomenon.” Based on the text books and findings of some scholars such as Godin,B. (2007) science is very much more complex than pat definitions which we can memorize and then put the issue away. They pointed the process of science typically has one of three goals.

1. Research to Understand (pure research)

Pure research is concerned with developing valid, complete, and coherent descriptions and explanations. It is interested in organizing data into the most general and parsimonious laws or qualified statements of uniformity. The emphasis is on comprehension or understanding. It is motivated by curiosity and inquisitiveness about natural phenomena. It is interested in data and relationships for their own sake.

2. Research to Solve a Particular Problem (applied research)

Applied research is concerned with the discovery of solutions to practical problems and places its emphasis upon those factual data which have more immediate utility or application. The emphasis is on control. Applied research is like learning phrases needed to accomplish a variety of specific things in a foreign language without really understanding the whole language. The 9

search for a cure for cancer is an example of applied research; discovering a solution for manic depression is an example of applied research.

C. Dispensing Solutions (practitioner / technologist)

Practitioners are concerned with the direct application of principles and theories from one or more fields of science for the purpose of dispensing solutions to individual human problems rather than being concerned with the discovery and organization of knowledge. Strictly speaking, a practitioner is not a scientist, but that is not to say they are necessarily unscientific. Partitioning is like memorizing sounds of a song in a foreign language without necessarily knowing the language. It accomplishes an immediate specific end. While a practitioner may uncover a phenomenon of great importance to the understanding of nature, that is not their primary focus. A practitioner or technologist administers chemotherapy or psychotherapy. A physician or a psychotherapist is a practitioner.

Definitions of Science Here, based on the text books some definitions of science are presented: 

Science uses unconfounded empirical tests to develop, discover, and explain systematic frameworks within which relationships can be explored.

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Science is a knowledge generating activity which is based on systematically organized bodies of accumulated knowledge obtained through objective observations.

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Science is not so much concerned with accumulating highly precise and specific data (although it is necessary) but rather science seeks to discover uniformities and to

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formulate statements of uniformities and consistencies of relationship between natural phenomena. 

Science is to understand, explain, and predict by specifying the systematic relationships among empirical variables. It must be consensually valid and general. It must not be on authority, sloppy, or simply to “better” mankind.

In summary, I can conclude that the word science comes from the Latin "scientia," meaning knowledge. How do we define science? According to Webster's New Collegiate Dictionary, the definition of science is "knowledge attained through study or practice," or "knowledge covering general truths of the operation of general laws, esp. as obtained and tested through scientific method [and] concerned with the physical world."What does that really mean? Science refers to a system of acquiring knowledge. This system uses observation and experimentation to describe and explain natural phenomena.

The term science also refers to the organized body of knowledge people have gained using that system. Less formally, the word science often describes any systematic field of study or the knowledge gained from it. What is the purpose of science? Perhaps the most general description is that the purpose of science is to produce useful models of reality. Science as defined above is sometimes called pure science to differentiate it from applied science, which is the application of research to human needs. Fields of science are commonly classified along two major lines:

- Natural sciences, the study of the natural world, and

- Social sciences, the systematic study of human behavior and society.

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On the other hand, science is the effort to discover and increase human understanding of how physical reality works. Its purview is the portion of reality which is independent of religious, political, cultural, or philosophical outlook. Using controlled methods, scientists collect data in the form of observations, records of observable physical evidence of natural phenomena, and analyze this information to construct theoretical explanations of how things work. Knowledge in science is gained through research. The methods of scientific research include the generation of hypotheses about how natural phenomena work, and experimentation that tests these hypotheses under controlled conditions. The outcome or product of this empirical scientific process is the formulation of theory that describes human understanding of physical processes and facilitates prediction. Lavoisier (2006) says, "... the impossibility of separating the nomenclature of a science from the science itself is owing to this, that every branch of physical science must consist of three things: the series of facts which are the objects of the science, the ideas which represent these facts and the words by which these ideas are expressed."

A broader modern definition of science may include the natural sciences along with the social and behavioral sciences, as the main subdivisions of science, defining it as the observation, identification, description, experimental investigation, and theoretical explanation of phenomena and to discover recurring relationship among phenomenon . However, other contemporary definitions still place the natural sciences, which are closely related with the physical world's phenomena, as the only true vehicles of science.

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Below are listed several projects’ specific objectives. Decide whether each is more likely to be an explanatory or a descriptive research project, and briefly discuss the reasons for your decision:

Before I want to answer to this question, I would like to have a brief look at research and explanatory or a descriptive research. Based on the text books, there are three main purposes of research are to describe, explain, and validate findings. Description emerges following creative exploration, and serves to organize the findings in order to fit them with explanations, and then test or validate those explanations. (Krathwohl, 1993)

Most quantitative research falls into two areas: studies that describe events and studies aimed at discovering inferences or causal relationships. Descriptive studies are aimed at finding out "what is," so observational and survey methods are frequently used to collect descriptive data (Borg & Gall, 1989).

Descriptive research involves gathering data that describe events and then

organizes, tabulates, depicts, and describes the data collection (Glass & Hopkins, 1984). Descriptive statistics utilize data collection and analysis techniques that yield reports concerning the measures of central tendency, variation, and correlation. On the other hand, descriptive research might simply report the percentage summary on a single variable and descriptive studies can yield rich data that lead to important recommendations.

Descriptive research is unique in the number of variables employed. Like other types of research, descriptive research can include multiple variables for analysis, yet unlike other methods, it requires only one variable (Borg & Gall, 1989). Descriptive studies report summary data such as measures of central tendency including the mean, median, mode, deviance from the mean, variation, percentage, and correlation between variables. 13

Explanatory research is research conducted in order to explain any behavior in the market. The explanatory style of study indicates the way a certain event is explained. The event can be either negative or positive. People first of all use this style to explain to themselves about certain events and then to other people. When events are studied using this style, there are three components: 

The person who experiences something might hold himself responsible for the cause of the event.

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The other component is the permanence and the experience might be considered to be unchangeable.

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The last component is the pervasiveness of the event which means the way a certain event affects life. Explanatory studies can make a person both optimistic and pessimistic.

Some experts Heppner, Kivlighan, & Wampold, (1999) believe that explanatory research is research conducted in order to explain any behavior in the market and it could be done through using questionnaires, group discussions, interviews, random sampling, etc. They also pointed to eight goals for explanatory research as below:

1. Explain things not just reporting. Why? Elaborate and enrich a theory's explanation. 2. Determine which of several explanations is best. 3. Determine the accuracy of the theory; test a theory's predictions or principle. 4. Advance knowledge about underlying process. 5. Build and elaborate a theory; elaborate and enrich a theory's predictions or principle.

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6. Extend a theory or principle to new areas, new issues, and new topics: 7. Provide evidence to support or refute an explanation or prediction. 8. Test a theory's predictions or principles. In other words, I can say that when we encounter an issue that is already known and have a description of it, we might begin to wonder why things are the way they are. The desire to know "why," to explain, is the purpose of explanatory research. It builds on exploratory and descriptive research and goes on to identify the reasons for something that occurs. Explanatory research looks for causes and reasons. For example, a descriptive research may discover that 10 percent of the parents abuse their children, whereas the explanatory researcher is more interested in learning why parents abuse their children. After the above discussion I think I can answer the questions more easily.

i. To test the hypothesis that social isolates suffer more from anxiety than those who are not social isolates.

This subject is an explanatory research project .because Research that tries to explain why something happens in the manner that they do. This research includes looking at all the possible combinations of social interaction. Explanatory research builds on exploratory and descriptive research and goes on to identify the reasons things occur. ii. To determine if popular magazines generally present persons of ethnic “minority” (such as Orange Asli – aborigines) background unfavorably?

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This subject is a descriptive research project .This research presents a picture of specific data such as social setting, or relationships. Here the researcher begins with a well-defined subject and then sets to describing what he or she observes. A descriptive study shows a picture of people in their social setting, participating in social activities. Descriptive research focuses on questions of how and why (why are people behaving in this manner at this time and for what reason), who is involved.

iii. To determine fathers’ and sons’ views on politics: This is an explanatory research because there is causal relationship (causality ) between variables fathers views on political its effective by sons views on politics.

iv. To explore the truth of saying “Familiarity breeds contempt.” This is a descriptive research because there non- causal relationship between the variables.

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Part two Indicate the level of measurement (nominal, ordinal, interval, and ratio) of each of the following variables. Explain your answer. Some researchers such as Kerlinger,(1986), Rosenthal, & Rosnow,(1991),believe that there can be three types of data: 

Nominal Data: This is data in the form of frequencies fitting discrete, distinct categories. For example, we can count the number of boys and girls in a class. Each individual is either a boy or a girl and there is no sense in which the boys and the girls can be placed into a rank order.

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Ordinal Data: Ordinal data are measures of physical quantities that can be ranked. For example, the variable X could measure the number of days individuals have been subject to a special diet; the variable Y could measure the position of those individuals in a race. Here, it is meaningful to ask how does the position of an individual that is his rank, in terms of values of X correlate with his position, or rank, in terms of Y.

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Interval Data: Data is said to be at interval level when there is a meaningful continuous scale of measurement such that equal differences between values in the scale genuinely correspond to real differences between the physical quantities that the scale measures. An example of a set of interval level data would be a collection of measurements of height. Here it is meaningful to say that the difference of height between a person who is 1.80m and one who is 1.70m tall is equal to the difference of height between a person who is 1.90m and one who is 1.80m tall. Equal differences in the scale correspond to equal 17

differences in the physical quantities they measure. All interval level data can be placed in rank order; in other words, interval level data can be "reduced" to ordinal level data. Ordinal level data cannot necessarily be promoted to interval level data. Interval level data contain more information than ordinal level data.

But some other researcher such as Ary, Jacobs and Razavieh (2005) and Godin (2007) pointed that there are four types of data that may be gathered in social research, each one adding more to the next. Thus ordinal data is also nominal, and so on.

Ratio

Interval

Ordinal

Nominal

Here, according to the text books and findings of scholars I am going to explain them one by one

Nominal

The name 'Nominal' comes from the Latin nomen, meaning 'name' and nominal data are items which are differentiated by a simple naming system. The only thing a nominal scale does is to say that items being measured have something in common, although this may not be described.

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Nominal items may also have numbers assigned to them. This may appear ordinal but is not these are used to simplify capture and referencing. Nominal items are usually categorical, in that they belong to a definable category, such as 'employees'. For example: The number pinned on a sports person or A set of countries.

Ordinal

Items on an ordinal scale are set into some kind of order by their position on the scale. This may indicate such as temporal position, superiority, etc. On the other hand, the order of items is often defined by assigning numbers to them to show their relative position. Letters or other sequential symbols may also be used as appropriate. Ordinal items are usually categorical, in that they belong to a definable category, such as '1956 marathon runners'. So we cannot do arithmetic with ordinal numbers because they show sequence only. For example: The first, third and fifth person in a race or pay bands in an organization, as denoted by A, B, C and D.

Interval

Interval data (also sometimes called integer) is measured along a scale in which each position is equidistant from one another. This allows for the distance between two pairs to be equivalent in some way. This is often used in psychological experiments that measure attributes along an arbitrary scale between two extremes and interval data cannot be multiplied or divided. For example: My level of happiness, rated from 1 to 10 or temperature, in degrees Fahrenheit.

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Ratio

In a ratio scale, numbers can be compared as multiples of one another. Thus one person can be twice as tall as another person. Important also, the number zero has meaning. So the difference between a person of 35 and a person 38 is the same as the difference between people who are 12 and 15. A person can also have an age of zero. On the other hand, ratio data can be multiplied and divided because not only is the difference between 1 and 2 the same as between 3 and 4, but also that 4 is twice as much as 2. On this point, Interval and ratio data measure quantities and hence are quantitative. Because they can be measured on a scale, they are also called scale data. For example: A person's weight or the number of pizzas I can eat before fainting.

Parametric vs. Non-parametric

Interval and ratio data are parametric, and are used with parametric tools in which distributions are predictable (and often Normal). Nominal and ordinal data are non-parametric, and do not assume any particular distribution. They are used with non-parametric tools such as the Histogram.

A. Seriousness of criminal offense: measured by having judges rank offenses from the most to the least severe. The level of measurement is ordinal. As it mentioned above, ordinal provides the identity of the entities and their rank order on some underlying give richer information than nominal scales property continuum. Numbers assigned in ranking order .Arrange from lowest to highest or vice versa.

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B. Political activism: measured by the total number of politically related activities in which an individual participates. The level of measurement is ratio. Ratio scale is highest level of measurement .It possesses all the properties of an interval scale plus in zero point representing an absence of the characteristic measured .For example, highest. True zero (represents absence of the characteristics) .In this study we have true zero. C. Ethnic group membership: measured by asking respondents to check one of these categories: Chinese, Malay, Indian, Kadazan. The level of measurement is Nominal. Based on the text books, nominal is the most primitive level of measurement. The numbers assigned to the entity are merely to identify individual or classify a set of entity based on certain similarities: The number can make qualitative or quantitative distinction .For example assigning 1 to male and 2 to female. Here we have names categories. D. Educational attainment: measured by asking respondents to check one of the following categories: eight grad or less; 9 to 11 years; high school graduate; some college; college graduate. The level of measurement is interval scales. Interval scales represent more than an ordinal scale .An example temperature Arbitrary zero (no absolte zero).Zero dose not represents absence of the characteristics. In this study we don’t have true zero. E. An item measuring an attitude or opinion that uses the following response format: strong agree, agree undecided, disagree, and strongly disagree.

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The level of measurement is interval scales. Interval scales represent more than an ordinal scale .An example temperature Arbitrary zero (no absolte zero).Zero dose not represents absence of the characteristics. In this study we don’t have true zero.

Suppose that you want to test the hypothesis that students who have taken a course in methods of social research will receive higher grades in subsequent sociology course than students who have not taken such course.

A. Identify the independent and dependent variable. Rosenthal & Rosnow, (1991) stated that "Dependent variable “DV” refers to the status of the 'effect'(or outcome) in which the researcher is Interested; the Independent Variable”IV” refers to the status of the presumed 'cause,' changes in which lead to changes in the status of the dependent variable…any event or condition can be conceptualized as either an independent or a dependent variable. For example, it has been observed that rumor-mongering can sometimes cause a riot to erupt, but it has also been observed that riots can cause rumors to surface. Rumors are variables that can be conceived of as causes (IVs) and as effects (DVs)." (, p. 71)

When researchers are not able to actually control and manipulate an IV, it is technically referred to as a status variable (e.g., gender, ethnicity, etc.). Even though researchers do not actually control or manipulate status variables, researchers can, and often do, treat them as IVs (Heppner, Kivlighan & Wampold, 1999). In experiments, the IV is the variable that is controlled and manipulated by the experimenter; whereas the DV is not manipulated, instead the DV is 22

observed or measured for variation as a presumed result of the variation in the IV."In nonexperimental research, where there is no experimental manipulation, the IV is the variable that 'logically' has some effect on a DV. For example, in the research on cigarette-smoking and lung cancer, cigarette-smoking, which has already been done by many subjects, is the independent variable." (Kerlinger, 1986, p.32)

A variable is something that can change, such as 'gender' and are typically the focus of a study. In different text books which some of them mentioned above the authors stated that there are different types of variables? 

Descriptive variables are those that which will be reported on, without relating them to anything in particular.

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Categorical variables result from a selection from categories, such as 'agree' and 'disagree'. Nominal and ordinal variables are categorical. Numeric variables give a number, such as age.

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Discrete variables are numeric variables that come from a limited set of numbers. They may result from, answering questions such as 'how many', 'how often', etc.

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Continuous variables are numeric variables that can take any value, such as weight.

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 Control variables note that in an experiment there may be many additional variables beyond the manipulated independent variable and the measured dependent variables. It is critical in experiments that these variables do not vary and hence bias or otherwise distort the results. There is a struggle between controls vs. authenticity in managing this. 

Correlation variables point with perfect correlation, the X-Y graph of points (as a scatter diagram) will give a straight line. Whilst this may happen in physics, it seldom happens in social research and a probabilistic relationship is the best that can be determined. Correlation can be positive (increasing X increases Y), negative (increasing X decreases Y) or non-linear (increasing X makes Y increase or decrease, depending on the value of X). Correlation can also be partial, that is across only a range of values X. As all possible values of X can seldom be tested, most correlations found are at best partial.

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Cause variable will be used when correlation is determined, a further question is whether varying the independent variable caused the independent variable to change. This adds complexity and debate to the situation. Sometimes a third variable is the cause, such as when a correlation between ice-cream sales and drowning is actually due to the fact that both are caused by warm weather.

 Continuous variable are measured along a continuous scale which can be divided into fractions, such as temperature. Continuous variables allow for infinitely fine sub-division, which means if we can measure sufficiently accurately, we can compare two items and determine the difference.

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Discrete variables are measured across a set of fixed values, such as age in years (not microseconds). These are commonly used on arbitrary scales, such as scoring we’re level of happiness, although such scales can also be continuous.

Researchers which mentioned above based on their findings stated that variables may have the following characteristics: 

Period: When it starts and stops.

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Pattern: Daily, weekly, ad-hoc, etc.

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Detail: Overview through to 'in depth'.

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Latency: Time between measuring dependent and independent variable (some things take time to take effect).

According to the above statements I can say that in an experiment, the independent variable is the variable that is varied or manipulated by the researcher, and the dependent variable is the response that is measured. An independent variable is the presumed cause, whereas the dependent variable is the presumed effect. The IV is the antecedent, whereas the DV is the consequent.

B. List of three extraneous variables. One of them must be of the kind that reasonably could be expected to produce a spurious relationship between the independent and dependent variables. Age-sex (gender)-IQ- attitude- level of education - level of knowledge-methods of teaching – Previous learning- Previous learning is extraneous variable that it could be expected to produce a spurious relationship between the independent and dependent variables.

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C. Briefly explain how one of your extraneous variables could make the hypothesized relationship spurious. (You may assume that methods of social research are not a required course). One of extraneous variables could make the hypothesized relationship spurious is previous learning. It can be as a independent variable and affected on dependent variable (The grad subsequent).

You would like to do a survey of students on … campus to find out how much time on the average they spend studying per week. You obtain from the registrar a list of all students currently enrolled and draw your sample from this list. Some of the researchers such as Heppner, Kivlighan, & Wampold (1999), LaFountain,& Bartos, (2002), Ary, Jacobs and Razavieh (2005) and Godin (2007) in their text books and articles stated that sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Here, I am going to begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, I'll talk about some of the statistical terms used in sampling. Finally, I'll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in each.

They also pointed that the major question that motivates sampling in the first place is: "Who do we want to generalize to?" Or should it be: "To whom do we want to generalize?" In most social

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research we are interested in more than just the people who directly participate in our study. We would like to be able to talk in general terms and not be confined only to the people who are in our study. Now, there are times when we aren't very concerned about generalizing.

Maybe we're just evaluating a program in a local agency and we don't care whether the program would work with other people in other places and at other times. In that case, sampling and generalizing might not be of interest. In other cases, we would really like to be able to generalize almost universally. When psychologists do research, they are often interested in developing theories that would hold for all humans. But in most applied social research, we are interested in generalizing to specific groups. The group we wish to generalize to is often called the population in we’re study. This is the group we would like to sample from because this is the group we are interested in generalizing to.

Let's imagine that we wish to generalize to urban homeless males between the ages of 30 and 50 in the United States. If that is the population of interest, we are likely to have a very hard time developing a reasonable sampling plan. We are probably not going to find an accurate listing of this population, and even if we did, we would almost certainly not be able to mount a national sample across hundreds of urban areas. So we probably should make a distinction between the population we would like to generalize to, and the population that will be accessible to us. We'll call the former the theoretical population and the latter the accessible population.

Once we've identified the theoretical and accessible populations, we have to do one more thing before we can actually draw a sample -- we have to get a list of the members of the accessible population. (Or, we have to spell out in detail how we will contact them to assure representativeness). 27

The listing of the accessible population from which we'll draw we’re sample is called the sampling frame. If we are doing a phone survey and selecting names from the telephone book, the book would be we’re sampling frame. That wouldn't be a great way to sample because significant subportions of the population either don't have a phone or have moved in or out of the area since the last book was printed. Notice that in this case, we might identify the area code and all three-digit prefixes within that area code and draw a sample simply by randomly dialing numbers (cleverly known as random-digit-dialing). In this case, the sampling frame is not a list per se, but is rather a procedure that we follow as the actual basis for sampling. Finally, we actually draw we’re sample (using one of the many sampling procedures). The sample is the group of people who we select to be in we’re study. Notice that I didn't say that the sample was the group of people who are actually in we’re study. We may not be able to contact or recruit all of the people we actually sample, or some could drop out over the course of the study. The group that actually completes we’re study is a subsample of the sample -- it doesn't include nonrespondents or dropouts. The problem of nonresponse and its effects on a study will be addressed when discussing "mortality" threats to internal validity.

People often confuse what is meant by random selection with the idea of random assignment. We should make sure that we understand the distinction between random selection and random assignment.

At this point, we should appreciate that sampling is a difficult multi-step process and that there are lots of places we can go wrong. In fact, as we move from each step to the next in identifying a sample, there is the possibility of introducing systematic error or bias. For instance, even if we are able to identify perfectly the population of interest, we may not have access to all of them. 28

And even if we do, we may not have a complete and accurate enumeration or sampling frame from which to select. And, even if we do, we may not draw the sample correctly or accurately. And, even if we do, they may not all come and they may not all stay. Depressed yet? This is a very difficult business indeed. At times like this I'm reminded of what Donald Campbell used to say (I'll paraphrase here): "Cousins to the amoeba, it's amazing that we know anything at all!"

It is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.

Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.

Sampling methods are classified as either probability or nonprobability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In nonprobability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In nonprobability sampling, the degree to which the sample differs from the population remains unknown. 29

Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.

Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.

Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and nonmanagers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.

Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This nonprobability method is often used during preliminary

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research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

Judgment sampling is a common nonprobability method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.

Quota sampling is the nonprobability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.

Snowball sampling is a special nonprobability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lowe’re search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.

A. What is your sampling frame?

For sampling frame is a list of all the people that are in the population .Here is a list of all names of students in this population, and they are numbered. We can choose systematic random 31

sampling with use of list of all students currently enrolled. At first we select one number of this list and then continue with Nth number of list.

B. What is your target population Target population: All of student currently enrolled in this semester.

C. Explain how you would draw a simple random sample for this study? If all students that currently enrolled have equal chance of being in the final s ample. It

is the

most basic and well know, however (regardless to major gender and so on). With use of one table of random numbers and indicate a place to start, move in one direction (e.g.move down the columns).once we get the set of randomly selected numbers ,find out who those people are and try to get them to participate in our research study.

D. Assume that the registrar’s list also contains information about each student’s program (example arts, science, medicine, etc). One then could select a stratified random sample, stratifying on program. What main benefit can result from using a stratified random sample instead of a simple random sample? Would you expect this benefit to be obtained by stratifying on program? Explain.

If I select one of the major’s for example: Science as a stratified random sampling in this research. Benefits of this sampling are as follow: -Production of estimates and corresponding confidence intervals for each stratum.

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- Increased precision. -In stratified random sampling is only possible to indicate proportion of the study population belongs to each group we are interested in. Then we would expect to be obtain a by stratified on major because one of major for example science or art are proportion of the study population belongs to each group we are interested in .

E. How might you obtain a cluster sample? When should you consider using this type of sampling design? If we select one class or one semester in one faculty or one major it is cluster sampling. Cluster sampling is often geographic units or organizational units. For example semester one science major in science faculty. With cluster sampling, we can indicate sample of all units in a subset of subgroups and cluster sampling will produce estimates with the largest variance. So with both clusters and stratification we partition the population into subgroups. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. The technique works best when most of the variation in the population is within the groups, not between them.

F. Which type of sampling design is most appropriate for this research problem? Explain.

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Simple random sampling is the most appropriate for this research. It is an equal probability sampling method. All subjects have equal chance to being the sample group. It’s the most basic type of random sampling.

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Hurworth, R. (1996). Qualitative Methodology: Common Questions about Running Focus Groups During Evaluations. Evaluation News and Comment, 5 (1): pp. 48-52.

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[URL:http://pages.cpsc.ucalgary.ca/~kremer/courses/451/DataGather.ht

ml]

Date

assessed:

16/2/11.

LaFountain, R. M., & Bartos, R. B. (2002). Research and statistics made meaningful in counseling and student affairs. Pacific Grove, CA: Brooks/Cole.

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Marof,R. (2008).Australia .Research methods and designs : a review of the his lecture , University Putra Malaysia .

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