@17703 module 4 quantitative research by stou.onlinemedia

17703 Paradigm and Communication Arts Research

Module 4 Quantitative Research Asst.Prof.Dr.Kanchana Meesilapavikkai, Sripatum University, Thailand E-mail Address: kanchana.me@spu.ac.th

Lesson 4.1: Quantitative Research Issues to be discussed: Topic 4.1.1 An Overview of Quantitative Research Topic 4.1.2 Quantitative Research Question Lesson 4.2: Quantitative Method Issues to be discussed: Topic 4.2.1 Population and Sample Topic 4.2.2 Sample Size Topic 4.2.3 Research Instrument Topic 4.2.4 Data Collection Lesson 4.3: Quantitative Method Issues to be discussed: Topic 4.3.1 Data Analysis Topic 4.3.2 Data Presentation Lesson 4.3: Quantitative Research Example

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Module 4 Quantitative Research

Objectives of Module 4:

After exploring the Research Methodology, students will be able to: 1. Understand the methods of quantitative research. 2. Analyze and identify correctly population and sample, sample size,

and research instruments. 3. Identify data collection; analyze data, and present data.

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Highlight Contents of Module 4:

Lesson 4.1: Quantitative Research Overview Issues to be discussed: Topic 4.1.1 An Overview of Quantitative Research Topic 4.1.2 Quantitative Research Question

4.1.1 An Overview of Quantitative Research

Quantitative research is a formal, objective, systematic empirical investigation of observable phenomena via statistical, mathematical or numerical data or computational techniques and collecting numerical data that are analyzed by using mathematically based methods in particular statistics (Aliaga and Gunderson, 2002). This research method is used to describe variables, to examine relationships among variables, and to determine cause-and-effect interactions between variables (Burns & Grove 2005). Phenomena are key elements of all research and they could be questions like ‘What are the factors that influence advertisers achievement in Thailand?’, ‘How does the relationship between public relations and advertising for consumer behavior in Malaysia?’ and so on. The idea is linked to what are seen as the philosophies and world views of researchers in quantitative research ‘paradigms’, described as being ‘realist’ or Sukhothai Thammathirat Open University

Module 4 Quantitative Research

sometimes ‘positivist. Realists take the view that what research does is to uncover an existing reality. ‘The truth is out there,’ and it is the job of the researcher to use objective research methods to uncover that truth. This means that the researcher needs to be as detached from the research as possible, and use methods that maximize objectivity and minimize the personal involvement of the researcher in the research process. This is best done by methods taken largely from the natural sciences, which are then brought to social research settings. Positivism is the most extreme form of this worldview. According to positivists, the world works according to fixed laws of cause and effect. Scientific thinking is used to test theories about these laws, and either reject or provisionally accept them. In this way, researchers will finally get to understand the truth about how the world works by developing reliable measurement instruments and objectively study the physical world. This view that there is a true reality out there that can be measured completely objectively is problematic. Historical research has shown that what is studied, and the findings that are produced, are influenced by people’s beliefs doing the research and the political/social climate at the time the research is done.

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Figure 4.1: Quantitative Research - Defined

Click here: https://www.youtube.com/watch?v=gml1RfLNS5E

The objective of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena.

Figure 4.2: The Objective of Quantitative Research:



To quantify data and generalize results from a sample to the population of interest. To measure the incidence of various views and opinions in a chosen sample. Sometimes followed by qualitative research which is used to explore some findings further.

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Module 4 Quantitative Research

Quantitative research involves several methods of data collection, such as mail surveys, telephone survey, and Internet surveys. Following these methods, the questioning is static or standardized; this means, all respondents are asked the same questions. Quantitative research requires the measurement of the variables under consideration; generally, this is expressed through numbers. For example, Quantitative research may help assess and report the increase or decrease of violence in news media.

Topic 4.1.2 Quantitative Research Question/Problem With a pragmatic approach to research methods, the main question that needs to be answered is: ‘what kind of questions or problems is best answered by using quantitative methods?’ However, the first thing is to understand ‘what is a research question/ problem.’ The University of Southern California: USC (2015) defines a research problem as:

Figure 4.3: Research Problem Definition A research problem is a statement about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or in practice that point to the need for meaningful understanding and deliberate investigation. In some social science disciplines the research problem is typically posed in the form of one or more questions. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. School of Communicatio n Arts

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There are four main types of research questions that may be answered at best through quantitative research: 1. The first type of question is demanding for a quantitative answer. Examples: ‘How many students choose to study film production?’ or ‘How many lab instructors do we need for film production departments in Myanmar?’ 2. Numerical change can likewise be accurately studied only by using quantitative methods.

Example: Are the numbers of students in advertising department rising or falling? Is achievement going up or down?

3. Quantitative research seeks to find out about the state of something or explain a particular phenomenon.

Examples: What factors predict the recruitment of an art instructor based on his/her performance? What factors are related to changes in students’ achievements in school over time? These kinds of questions can be answered through quantitative research methods.

4. Finally, quantitative research is especially suitable for testing a hypothesis that wants to explain particular phenomena. For example, through quantitative Sukhothai Thammathirat Open University

Module 4 Quantitative Research

research, one may assess whether there is a relationship between the ways students perform in film production courses and their self-esteem and social backgrounds. Research shows that in some contexts, underprivileged students suffer from low self-esteem compared to their peers coming from more privileged backgrounds. This may result in studentsâ&#x20AC;&#x2122; lower academic performance.

There is no "one best way" to structure a quantitative research question. However, the following four steps may be a good start in framing your question:

Figure 4.4: Four Steps for Quantitative Research Question 1. Choose the type of quantitative research question that you think best suits your research (for example, descriptive, comparative or relationshipbased);

2. Identify the different types of variables that you are trying to measure, manipulate and/or control, as well as any groups that may be involved;

3. Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved;

4. Write out the problems or issues you may encounter in the form of a School of Communicatio n Arts complete research question.

17703 Paradigm and Communication Arts Research

In quantitative research, a variable is an object, event, idea, feeling, time period, or any other type of category that we are trying to measure. There are two types of variables: independent and dependent variables. An independent variable is a variable that stands alone and that is not changed by the other variables that we are trying to measure. For instance, someone's age might be an independent variable. Other factors such as: what a person eats or how much television he/she watches, aren't going to change that person's age. The independent variable causes some change in the other variables, also called dependent variables. Dependent variables, on the other hand, depend on other factors. For example, the test score of a public relations exam might be a dependent variable. The score might change depending on several factors such as: the amount of time the student studied for that exam, or the amount of sleep the student had the night before the exam. Usually, researchers are trying to find out what causes changes in the dependent variables. In quantitative research, researchers create hypothesis in order to speculate on the outcomes of a research study. Hypothesis is a statement or proposition set forth as an explanation for the occurrence of some phenomenon, such as a statement proposed to explain or answer a research question. Research hypothesis typically describes an expected difference in variables across groups, a change in variables, or an expectation of one or more variables on one or more response variables. We can identify three types of hypotheses:

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Module 4 Quantitative Research

1. Research Hypothesis: is a hypothesis from observation, the literature, and/or the theory in the study. A research hypothesis states the relationship one expects to find as a result of the research. 2. Null Hypothesis: is the same as a statistical hypothesis because it states that there is no relationship between the variables in the study. Null hypothesis does not foresee an expected outcome. 3. Alternative Hypothesis: for the purposes of statistical testing, the research hypothesis is often referred to as the alternative hypothesis, denoted H1 or Ha. For example: Do male students and female students show the same levels of participation in activities ongoing at the Communication Department? ď&#x201A;§

ď&#x201A;§

H0: Male students and female studentsâ&#x20AC;&#x2122; participation in activities at the Communication Department are at the same rate. Ha: Male students participate in activities at the Communication Department at a lower rate compared to female students.

Self-Assessment Exercise 1: 1. What is quantitative research? 2. Can you give some examples of quantitative research questions?

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Bibliography Aliaga, M & Gunderson, B (2002) Interactive Statistics, 2nd Ed, Upper Saddle River, N.J. prentice Hall. In Hartas, D (2010) Educational Research and Inquiry: Qualitative and Quantitative Approaches. London, Continuum International publishing Group.

Allen, M., Tisworth, S., & Hunt, S.K. (2009). Quantitative research in communication. Sage Publications.

Burns N., Grove SK (2005) The Practice of Nursing Research: Conduct, Critique, and Utilization (5th Ed.). St. Louis, Elsevier Saunders.

How to structure quantitative research questions. The Free Press. Available at: http://dissertation.laerd.com/how-to-structure-quantitative-research-questions.php

Nardi, P.M. (2003). Doing survey research: A guide to quantitative methods. Boston: Allyn and Bacon.

Tantawutho, V. (2009). The quantitative data analysis guide: For adult and vocational education research. Kasetsart University.

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Module 4 Quantitative Research

Wimmer, R.D. & Dominick, J.R. (2013). Mass media research: An introduction, 10th ed. Thomson Wadsworth.

USC Libraries. (2015). The research problem/ question. University of Southern California. Available at: http://libguides.usc.edu/content.php?pid=83009&sid=618412

Key Readings Balnaves, M. & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach. Sage Publications. Available at: https://books.google.co.th/books?id=nQxL0Ooq61oC&printsec=frontcover&dq=Balnaves,+M.+%26+Caputi,+P.+%282001%29.+Introduction+t o+quantitative+research+methods:+An+investigative+approach.+Sage+Publications.&hl=en&sa=X&ei=jmvcVNfF9CKuwSZg4GYDg&ved=0CB0Q6AEwAA#v=onepage&q&f=false

How to structure quantitative research questions. The Free Press. Available at: http://dissertation.laerd.com/how-to-structure-quantitative-research-questions.php

Wimmer, R.D. & Dominick, J.R. (2013). Mass media research: An introduction, 10th ed. Thomson Wadsworth. Available at: https://books.google.co.th/books?id=FTukyzrOED0C&printsec=frontcover&dq=Wimme r,+R.D.+%26+Dominick,+J.R.+%282006%29.+Mass+media+research:+An+introductio n,+8th+ed.+Thomson+Wadsworth.&hl=en&sa=X&ei=cG3cVODPHpKVuAS_s4GQDQ &ved=0CDMQ6wEwBA#v=onepage&q&f=false School of Communicatio n Arts

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Recommended Readings

Wimmer, R.D. & Dominick, J.R. (2013). Mass media research: An introduction, 10th ed. Thomson Wadsworth. https://books.google.co.th/books?id=FTukyzrOED0C&printsec=frontcover&dq=Wimme r,+R.D.+%26+Dominick,+J.R.+%282006%29.+Mass+media+research:+An+introductio n,+8th+ed.+Thomson+Wadsworth.&hl=en&sa=X&ei=cG3cVODPHpKVuAS_s4GQDQ &ved=0CDMQ6wEwBA#v=onepage&q&f=false

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Module 4 Quantitative Research

Lesson 4.2: Quantitative Method

Issues to be discussed:

Topic 4.2.1 Population and Sample Topic 4.2.2 Sample Size Topic 4.2.3 Research Instrument Topic 4.2.4 Data Collection

4.2.1 Population and Sample One goal of scientific research is to describe the nature of a population: a group or class of subjects, variables, concepts, or phenomena that usually a large number of cases representing of interest. Wimmer & Dominick (2006) points that in many situations, an entire population cannot be examined due to time and resource constraints. Studying every member of a population is also generally cost-prohibitive and may confound the research because measurements of large numbers of people often affect measurement quality. When focusing on the term â&#x20AC;&#x2DC;population,â&#x20AC;&#x2122; usually we think of people in a town, region, state or country and their respective characteristics such as gender, age, marital status, ethnic membership, religion and so forth. In statistics this term takes on a slightly different meaning that includes all members of a defined group that researchers are studying or collecting information on for data driven decisions. School of Communicatio n Arts

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The usual procedure in these instances is to take a sample from the population. A sample is a subset or representative of the population, so an investigation is often restricted to one or more samples drawn from it. A well chosen sample will contain most of the information about a particular population parameter but the relation between the sample and the population must be such as to allow true inferences to be made about a population from that sample. See Figure 4.5 Population and Sample

Sampling is done usually because it is impossible to test every single individual in the population. Researchers must keep in mind that the ideal scenario is to test all the individuals to obtain reliable, valid and accurate results. If testing all the individuals is impossible, the only time to rely is on sampling techniques. 4.2.2 Sample Size and Sampling The aim of statistical testing is to uncover a significant difference when it actually exists. In its simplest form this involves comparing samples between one regime and another. Sample size is important because larger samples increase the chance of finding a significant difference however larger samples cost more money. Sukhothai Thammathirat Open University

Module 4 Quantitative Research

Sample size as an important and controllable influence on the statistical precision or sample error, population values can be estimated. In most applied research settings, however, limited resources restrict the number of individuals that can be sampled. When choosing a sample size, researcher must consider about what population parameters they want to estimate, cost of sampling, spread of the population, how hard is to collect data, how precise does the researcher wants the final estimates to be. Sometimes researchers need to sound more “scientific” in order to be taken seriously. In that case, they can use sample size calculation such as the Yamane’s Formula. Figure 4.6: Yamane’s Formula: Sample Size Calculation

Figure 4.7 Table for Sample Size:

Sample size for ±5%, ±7% and ±10% Precision

Levels Where Confidence Level is 95% and P=. 5

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To illustrate, if in the School of Communication Arts the population is of 400 students, therefore, assume p=.5 (maximum variability), the sample size is of 201 students. Furthermore, suppose that the researcher desires a 95% confidence level and Âą5% precision. Although tables can provide a useful guide for determining the sample size, one must always calculate the necessary sample size for a different combination of levels of precision, confidence, and variability.

Sampling is the process by which inference is made to the whole by examining only a part (Som, 1996 p.1). There are many types of sampling. Some sampling methods are as follows:

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Module 4 Quantitative Research

1. Probability sampling is any method of sampling that utilizes some form of random selection. In order to have a random selection method, the researcher must set up some process or procedure that assures that the different units in a population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking the name of a student out of a hat. These days, researchers tend use computers to generate random numbers for random selection. 2. Simple random sampling is the most widely-used probability sampling method, probably because it is easy to implement and easy to analyze. Moore and McCabe (2006) state that a simple random sample (SRS) of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected. Each member of the population is equally likely to be chosen at any stage in the sampling process.

3. Systematic Random Sampling is a random sampling technique which is frequently chosen by researchers for its simplicity and its periodic quality. First, researchers randomly pick the first item or subject from the population. Then, they select each n'th subject from the list. The results are representative of the population unless certain characteristics of the population are repeated for every n'th individual. For example, an instructor has a population of a total of 100 students and needs to select 12 students. He/she first picks his starting number, 5. Then, the instructor picks his/her interval, 8. The members of his/her sample will be individuals 5, 13, 21, 29, 37, 45, 53, 61, 69, 77, 85, 93.

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4. Stratified Sampling is a probability sampling technique wherein the researcher first divides the entire population into different subgroups or strata, then, he/ she randomly selects the final subjects proportionally from the different strata. It is important to note that the strata must be non-overlapping. Having overlapping subgroups will grant some individuals higher chances of being selected as subjects. This completely negates the concept of stratified sampling as a type of probability sampling. Examples of stratified sampling can be students enrolled in the School of Communication Arts from the departments of advertising, public relations, film, and performance arts. Then, the researcher can use any method of sampling that is provided by the research, for instance, systematic random sampling in each department.

5. Cluster Sampling occurs when instead of selecting all the subjects from the entire population, the researcher takes several steps in gathering his/her sample population. First, the researcher selects groups or clusters, then, from each cluster, the researcher selects the individual subjects by either simple random or systematic random sampling. The researcher can even select to include the entire cluster and not just a subset from it. The most common cluster used in research is a geographical cluster. For example, if a researcher wants to survey academic performance of instructors in the School of Communication Arts in Malaysia, he/she can divide the entire population (the entire Schools of Communication Arts in Malaysia) into different clusters (cities). Then, the researcher can select a number of clusters, depending on the research study, through simple or systematic random sampling. The important thing to remember is to give all the clusters equal chances of being selected.

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Module 4 Quantitative Research

6. Availability Sampling is a statistical method used to draw representative data by selecting people who volunteer to join the research. Interviewing people on a street corner or at the mall are a few examples.

7. Purposive Sampling is a method used when the selection of units entirely depends on the choice of the investigator. This type of sampling is adopted when it is not possible to adopt any random procedure for selection of sampling unit (Agarwal, 2003 p.186). Purposive sampling can be very useful for situations where researchers need to reach a targeted sample quickly and where sampling for proportionality is not the main concern. For instance, selecting Deans at the School of Communication Arts.

8. Quota Sampling requires that representative individuals be chosen out from a specific subgroup. A sampling method of gathering representative data from a group, as opposed to random sampling occurs for example, when a researcher might ask for a sample of 100 female television viewers, or 100 individuals between the ages of 2030.

9. Snowball Sampling is a subset of a purposive sampling, a non-probability sampling technique that is appropriate to use in research when the members of a population are difficult to locate. A snowball sample is one in which the researcher collects data among few members of the target population he or she can locate. Then, the researcher may ask those individuals to provide information needed to locate other

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members of that same population. A snowball sampling is achieved by asking a participant to suggest someone else who might be willing to join the same study.

4.2.3 Research Instrument It is very important to design a good research instrument that will enable us to collect data. In quantitative research, the researcher needs to design a questionnaire; when designing the research instruments, ensure that: the focus and aims of the research project are well defined, how the personâ&#x20AC;&#x2122;s data will be used, confidentiality respondentsâ&#x20AC;&#x2122; data, how long will it take to complete the questionnaire, use ageappropriate language, use appropriate scales, among others. The questionnaire is a reliable instrument and can be online or on paper. Online questionnaires save a lot of time for the researcher to collect data. Joppe (2000) defines reliability as: Figure 4.8 Definition of Reliability by Joppe (2000) â&#x20AC;ŚThe extent to which results are consistent over time and an accurate representation of the total population under study is referred to as reliability and if the results of a study can be reproduced under a similar methodology, then the research instrument is considered to be reliable. (p. 1) Reliability can be thought of as consistency. Does the instrument consistently measure what it is intended to measure? It is not possible to calculate reliability; however, there are four general estimators that you may encounter in reading research: Sukhothai Thammathirat Open University

Module 4 Quantitative Research

1. Inter-Rater/Observer Reliability: The degree to which difference. ? 2. Test-Retest Reliability: The consistency of a measure evaluated over time. 3. Parallel-Forms Reliability: The reliability of two tests constructed the same way, from the same content. 4. Internal Consistency Reliability: The consistency of results across items, often measured with Cronbachâ&#x20AC;&#x2122;s Alpha. Reliability is the extent to which an experiment, test, or any measuring procedure yields the same result on repeated trials. Without the agreement of independent observers able to replicate research procedures, or the ability to use research tools and procedures that yield consistent measurements, researchers would be unable to satisfactorily draw conclusions, formulate theories, or make claims about the generalizability of their research. Joppe (2000) provides the following explanation of what validity is in quantitative research: Figure 4.9 Definition of Validity by Joppe (2000) Validity determines whether the research truly measures that which it was intended to measure or how truthful the research results are. In other words, does the research instrument allow you to hit "the bullâ&#x20AC;&#x2122;s eye" of your research object? Researchers generally determine validity by asking a series of questions, and will often look for the answers in the research of others. (p. 1) Wainer and Braun (1998) describe the validity in quantitative research as construct validity. The construct is the initial concept, notion, question or hypothesis School of Communicatio n Arts

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that determines which data is to be gathered and how it is to be gathered. They also assert that quantitative researchers actively cause or affect the interplay between construct and data in order to validate their investigation, usually by the application of a test or other process. Validity is the extent to which an instrument measures what it is supposed to measure and performs as it is designed to perform. It is rare, if nearly impossible, that an instrument be 100% valid, so validity is generally measured in degrees. As a process, validation involves collecting and analyzing data to assess the accuracy of an instrument. There are numerous statistical tests and measures to assess the validity of quantitative instruments, which generally involves pilot testing. The remainder of this discussion focuses on external validity which the results of a study can be generalized from a sample to a population and content validity which refers to the appropriateness of the content of an instrument. Validity and reliability are presented in this video.

Click here:https://www.youtube.com/watch?v=DudgZkTayw&index=2&list=PLWTk8cJjsFISIi0e6n4Fxg7O6bAh9Wknl

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Module 4 Quantitative Research

4.2.4 Data Collection All social science researchers look for data to answer their research questions and achieve their research objectives. To a great extent, the data collecting methods affect the quality, quantity, adequacy, and appropriateness of data. There are four main methods of data collection: Figure 4.10: Four Main Methods of Data Collection

1. Census. A census is a study that obtains data from every

2. 3. 4. 5. 6. 7.

member of a population. It is not practical, because of the cost and/or time required. 2. Sample survey. A sample survey is a study that obtains data from a subset of a population, in order to estimate population attributes. 3. Experiment. An experiment is a controlled study in which the researcher attempts to understand cause-and-effect relationships. The study is "controlled" subjects or treatment. 4. Observational study. Like experiments, observational studies attempt to understand cause-and-effect relationships. The researcher is not able to control subjects or treatment.

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer School of Communicatio n Arts

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stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

Lesson 4.3 Data Analysis 4.3.1 Quantitative data analysis principle Can provide a powerful and extremely critical tool to use in research that can complement and expand the understanding through the research. Researchers can do has so much potential if done properly and appropriately. Quantitative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. Quantitative data can be analyzed in a variety of different ways. Connolly (2007) focuses on getting started with SPSS; exploring, displaying, and summarizing data; analyzing relationships between variables in his book â&#x20AC;&#x2DC;Quantitative data analysis in education: A critical introduction using SPSS.â&#x20AC;&#x2122; Before beginning the analysis, researchers must identify the level of measurement associated with the quantitative data. The level of measurement can influence the type of analysis. There are four levels of measurement: nominal, ordinal, interval, and ratio (scale). Once you have identified your levels of measurement, you can begin using some of the quantitative data analysis procedures, due to sample size restrictions. However, there are several procedures researchers can use to determine what narrative data is telling, as data tabulation (frequency distributions & percent

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Module 4 Quantitative Research

distributions), descriptive data, data disaggregation, moderate and advanced analytical methods. Watch this video on ‘Quantitative Analysis.’

Click here: https://www.youtube.com/watch?v=X-GcLmE5YoI Therefore, data analysis is the process of finding the right data to answer research questions, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating the results to have the biggest possible impact. 4.3.2 Data Presentation Data can be summarized and presented in various forms. These include the ‘Data Presentation Techniques’ as following: 1. Tabulation: This deals with presentation of data in tabular form. A table is an array of data in rows and columns (Adedayo, 2000) Tabulation condenses a large mass of data and brings out the distinct pattern in a data in an attractive form. It enables comparison to be made easily among classes of data and takes up less space than data presented in narrative School of Communicatio n Arts

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form. A table has the contents as: a title at the top describing the content of the table.

Figure 4.11 Data Presentation: Tabulation

2. Diagrammatic representation: For better visual impact, data can be represented in many forms as: ď&#x201A;ˇ Pictogram: A pictogram (short for picture diagram) presents a pictorial symbol that represents the data of interests.

Figure 4.12 Data Presentation: Pictogram

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Pie chart: A Pie chart consists of a circle, divided into sectors, which are proportional to the data. The sum of angles in circle is 360 degrees. A total of all cases are found and the percentage of each case is found in relation to 360 degrees. Pie chart is usually for not too many categories.

Figure 4.13 Data Presentation: Pie Chart

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Bar chart: A bar graph is a way of summarizing a set of categorical data. It displays the data using a number of rectangles, of the same width, each of which represents a particular category. Bar graphs can be displayed horizontally or vertically and they are usually drawn with a gap between the bars (rectangles).

Figure 4.14 Data Presentation: Bar Chart

Histogram: A histogram is a way of summarizing data that are measured on an interval scale (either discrete or continuous). It is often used in exploratory data analysis to illustrate the features of the distribution of the data in a convenient form.

Figure 4.15 Data Presentation: Histogram

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Line graph: A line graph is particularly useful when researchers want to show the trend of a variable over time. Time is displayed on the horizontal axis (x-axis) and the variable is displayed on the vertical axis (y- axis). Figure 3.16 Data Presentation: Line Graph

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In quantitative research, there are methods available for data analysis and interpretation as:  The Percentage: This refers to the proportion or rate of a particular value in relation to 100. It is used to convert values to a uniform standard for ease of comparison.  The Mean: This is the arithmetic average of a set of data (e.g. scores). It is obtained by summing up all the scores and dividing by the total number of cases.  The Median: This is the middle value in a set of values, dividing the data into equal parts. It is a measure of position rather than of magnitude. The scores are first arranged in the order of magnitude (ascending or descending). If the number of items is odd, the median is the single score in the middle. But with even scores, it is the average of the two scores in the middle.  The Mode: It is the most frequently occurring value in a set of observations. A distribution may be unimodal, bimodal, trimodal or multimodal. Types of statistics in quantitative research refer to Descriptive Statistics, which describe the relationship between variables; for examples frequencies, means, standard deviation and Inferential Statistics, which make inferences about the population, based on a random sample. Data presentation techniques, and data analysis and interpretation are both important. Researchers have to select the type of statistics valid for their research. Although results can be expressed within the text of a report, data is usually more digestible if it is presented in the form of a table or graph.

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Module 4 Quantitative Research

Graphs and charts can quickly convey to the reader the essential points or trends in the data. Graphs and charts are particularly useful when data is being presented to an audience, especially when information must be conveyed in a short period of time.

Lesson 4.4: Quantitative Research Example

Self-Assessment Exercise 2 (Continued from Self-Assessment Exercise 1): 1. What are the population and sample of your research? 2. How many sample sizes did you identify? 3. From question 2. What is the method you chose? Explain why.

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Bibliography Adebayo, O. A. (2000). Understanding statistics. Lagos: JAS. Agarwal, B. L. (2003). Programmed statistics (question-answer), 2nd Ed. New Age International, p.186.

Chitode, J. S. (2007). Communication systems-1. Technical Publications Pune.

Cochran, W.G. (2007). Sampling techniques, 3rd edition. Wiley India.

Connolly, P. (2007). Quantitative data analysis in education: A critical introduction using SPSS. Routledge.

Dattalo, P. (2008). Determining sample size: Balancing power, precision, and practicality. Oxford University Press. Available at: http://www.amazon.com/Determining-Sample-Size-BalancingPracticality/dp/0195315499#reader_0195315499

Joppe, M. (2000). The Research Process. Available at: http://www.ryerson.ca/~mjoppe/rp.htm

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Module 4 Quantitative Research

Moore, D. S. and George, P. M. (2006), Introduction to the Practice of Statistics, 5th edition, Freeman, p. 219.

Som, R. K. (1996). Practical sampling techniques, 2nd ed. Marcel Dekker. Wainer, H., & Braun, H. I. (1988). Test validity. Hilldale, NJ: Lawrence Earlbaum Associates.

Wimmer, R.D. & Dominick, J.R. (2006). Mass media research: An introduction, 8th ed. Thomson Wadsworth.

Winston, B. (1998). Media, technology, and society. London: Routledge.

Yamane, T. (1967). Statistics: An introduction analysis, 2nd edition. New York: Harper and Row.

School of Communicatio n Arts

17703 Paradigm and Communication Arts Research

Key Readings: Dattalo, P. (2008). Determining sample size: Balancing power, precision, and practicality. Oxford University Press. Available at http://www.amazon.com/Determining-Sample-Size-BalancingPracticality/dp/0195315499#reader_0195315499

Kennedy, S. B., Nolen, S., Applewhite, J., Pan, Z., Shamblen, S. & Vanderhoff, K.J. (2007). A Quantitative Study on the Condom-Use Behaviors of Eighteen- to TwentyFour-Year-Old Urban African American Males. AIDS Patient Care STDS. May; 21(5): 306â&#x20AC;&#x201C;320. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950730/

Yamane, T. (1967). Statistics: An introduction analysis, 2nd edition. New York: Harper and Row. Available at:

Sukhothai Thammathirat Open University

Module 4 Quantitative Research

http://www.worldcat.org/title/statistics-an-introductoryanalysis/oclc/39121222&referer=brief_results

Recommended Readings Connolly, P. (2007). Quantitative data analysis in education: A critical introduction using SPSS. Routledge.

Levine, T.R. (2013). Quantutative Communication Research: Review, Trends, and Critique. Review of Communication Research Vol.1, No.1, 69-84. Assignment of Module 4 Assignment 1 "Starbucks Case Analysis” 1. Study from "Starbucks Case Analysis." 123HelpMe.com. 12 Feb 2015 http://www.123HelpMe.com/view.asp?id=167941 2. If, you have to do the quantitative research ‘Attitudes and Satisfaction of Consumers for Starbucks Brand.’ Please go through the research methodology and answer: 2.1 What are the population, samples, and sampling? 2.2 What research instrument is used? 2.3 How is data collected? 2.4 How is data analyzed? 2.5 How is data presented?

School of Communicatio n Arts