mac210 note 2.docx

**ELEMENTS OF RESEARCH** These are the ingredients common to scientific research, including communication research **Concepts** Concepts simply refer to building blocks or the ideas of theory and research. It refers to as an abstract idea. It refers to a term that expresses an abstract idea forme...

**ELEMENTS OF RESEARCH** These are the ingredients common to scientific research, including communication research **Concepts** Concepts simply refer to building blocks or the ideas of theory and research. It refers to as an abstract idea. It refers to a term that expresses an abstract idea formed by generalizing from particulars and summarising related observation. **A concept is formed from summarising an observed pattern of behaviour**. Examples of concepts are advertising effectiveness, media usage, effectiveness, satisfaction, impact, excellent, self-esteem, rich, domestic violence, extent or pattern of alcohol consumption etc. Concepts are important for at least two reasons: 1. They simplify the research process by combining particular characteristics, objects or people into general categories. The researcher tries to get a general term that is more inclusive and convenient to use in his research instead of describing each of that characteristics that make certain people unique. 2. Concepts simplify communication among those who have a shared understanding of them. Researchers use concepts to organize their observations into meaningful summaries and to transmit this information to others. Example, agenda setting is understood among researchers. In other words, people must share an understanding of a concept before it can be made useful **Construct** A construct is a concept that has three distinct characteristics. **It is also a concept, but it has added meaning of having been deliberately and consciously invented or adopted for a specific scientific purpose**. They are higher concepts. Wimmer & Dominick (2011) defines construct as a combination of concepts...which usually cannot be observed directly and designed for a specific research purpose so that its exact meaning relates only to the context in which it is found. Example, advertising involvement is a construct that is difficult to see directly and includes the concepts of attention, interest and arousal. In some context, involvement could mean subject involvement with product, message or medium; **its precise meaning depends on the research context.** In a research study, it is important that the concepts used should be operationalised in measurable terms so that the extent of variations in respondents' understanding is reduced if not eliminated. Thus, in operationalising concepts, the knowledge about variables plays an important role in reducing this variability and thus, fine tuning your research problem. **Variable** A variable is an image, perception of concept that is capable of being measured, hence capable of taking on different values. In other words, **a concept that can be measured is called a variable**. A variable is a property that takes on different values; it is something that varies; it is symbol to which numerals or values are attached" (Kerlinger 1986: 27) **Difference Between Concepts and Variables** Measurability is the main difference between a **concept** and a variable. Concepts are mental images or perceptions and therefore their meanings vary markedly from individual to individual, whereas variables are measurable, though, of course, with varying degrees of accuracy. A concept cannot be measured whereas a variable can be subjected to measurement by crude/refined or subjective/objective units of measurement. Concepts are subjective impressions which, if measured as such would cause problems in comparing responses obtained from different respondents. It is therefore important for the concepts to be converted into variables (either directly or through a set of indicators) as they can be subjected to measurement, even though the degree of precision with which they can be measured markedly varies from one measurement scale to another (*nominal*, *ordinal*, *interval* and *ratio*). Examples of variables include age, gender, marital status, attitude, income, weight, height, religion etc. **Converting Concepts into Variables** A ratio scale has all the properties of nominal, ordinal and interval scales and it also has a starting point fixed at zero. Therefore, it is an absolute scale -- the difference between the intervals is always measured from a zero point. This means the ratio scale can be used for mathematical operations. The measurement of income, age, height and weight are examples of this scale. A person who is 40 years of age is twice as old as a 20-year-old. A person earning \$60 000 per year earns three times the salary of a person earning \$20 000. One of the main differences between quantitative and qualitative research studies is in the area of variables. In qualitative research, as it usually involves studying perceptions, beliefs, or feelings, you do not make any attempt to establish uniformity in them across respondents and hence measurements and variables do not carry much significance. On the other hand, in quantitative studies, as the emphasis is on exploring commonalities in the study population, measurements and variables play an important role. **Concepts Indicators** **Variables Decision Level** **(Working Definition)** **Rich 1. Income 1. Income per year 1. If \>N 1,000,000** **2. Assets 2. Total value of home 2. If \> N 3,000,000** **Cars and investment** **High Academic 1. Average marks 1. % of marks 1. If \> 75%** **Obtained in exam** **Achievement 2. Average marks 2. % of marks 2. If \> 75%** **3. Average total marks 3. % of marks 3. If \> 80%** **Types of Variables** Variables are understood better with their classifications. Authors have attempted to explain variables based on general classifications such as Independent/dependent variables, discrete/continuous variables and simple/complex variables. However, Ranjit Kumar (2011) provides a detailed explanation of variables based on the following classifications. Variables can be classified in several ways: - the causal relationship; - the study design; - the unit of measurement **For this course, we viewed variable from the viewpoint of causal relationship** In studies that attempt to investigate a causal relationship or association, four sets of variables may operate. 1\. *change* variables, which are responsible for bringing about change in a phenomenon, situation or circumstance. In research terminology, change variables are called **independent variables**, 2\. *outcome* variables, which are the effects, impacts or consequences of a change variable. outcome/effect variables are called **dependent variables**, 3\. variables which *affect or influence* the link between cause-and-effect variables are called **extraneous variables** 4\. *connecting* or *linking* variables, which in certain situations are necessary to complete the relationship between cause-and-effect variables. They are called **intervening variables** Hence, the types of variables in terms of causal relationship are: 1. Independent variable -- the cause supposed to be responsible for bringing about change(s) in a phenomenon or situation. It is the presumed cause of the dependent variable and can be manipulated or systematically varied by the researcher. 2. Dependent variable -- the outcome or change(s) brought about by introduction of an independent variable. This is the presumed effect, the consequent; it is what the researcher wishes to explain. It is measured and never manipulated as part of study. 3. Extraneous variable -- several other factors operating in a real-life situation may affect changes in the dependent variable. These factors, not measured in the study, may increase or decrease the magnitude or strength of the relationship between independent and dependent variables. 4. Intervening variable -- sometimes called the confounding variable, it links the independent and dependent variables. In certain situations, the relationship between To explain these variables let us consider some examples. Suppose you want to study the relationship between smoking and cancer. You assume that smoking is a cause of cancer. Studies have shown that there are many factors affecting this relationship, such as the number of cigarettes or the amount of tobacco smoked every day; the duration of smoking; the age of the smoker; dietary habits; and the amount of exercise undertaken by the individual. All of these factors may affect the extent to which smoking might cause cancer. These variables may either increase or decrease the magnitude of the relationship. In the above example the extent of smoking is the independent variable, cancer is the dependent variable and all the variables that might affect this relationship, either positively or negatively, are extraneous variables. To understand intervening variables, suppose you want to study the relationship between fertility and mortality. Your aim is to explore what happens to fertility when mortality declines. The history of demographic transition has shown that a reduction in the fertility level follows a decline in the mortality level, though the time taken to attain the same level of reduction in fertility varied markedly from country to country. As such, there is no direct relationship between fertility and mortality. With the reduction in mortality, fertility will decline only if people attempt to limit their family size. History has shown that for a multiplicity of reasons, people have used one method or another to control their fertility, resulting in lower fertility levels. It is thus the intervention of contraceptive methods that completes the relationship: the greater the use of contraceptives, the greater the decline in the fertility level and the sooner the adoption of contraceptive methods by people, the sooner the decline. The extent of the use of contraceptives is also affected by a number of other factors, for example attitudes towards contraception, level of education, socioeconomic status and age, religion, and provision and quality of health services. These are classified as extraneous variables. In the above example, decline in mortality is assumed to be the cause of a reduction in fertility, hence the mortality level is the independent variable and fertility is the dependent variable. But this relationship will be completed only if another variable intervenes -- that is, the use of contraceptives. **Measurement** Measurement has been viewed as a process of determining dimensions, values or degrees. It is a process of assigning numbers to phenomena according to rules. These rules must be understood and be used a s basis for measurement. From this explanation, there are three points to note in measurement: numerals, assignment and rules. A number has no implicit meaning except it is assigned to represent certain objects or events. **Levels of Measurement** Levels of measurement refer to the different ways in which variables or data can be quantified and classified. They are fundamental to the field of statistics and data analysis, as they dictate the types of statistical tests that can be performed. There are four main levels of measurement: **Nominal Level:** This is the most basic level of measurement, used for labeling variables without any quantitative value. It simply means categorizing elements to determine whether elements/objects of investigation are the same or not. Classification using a nominal scale ensures that individuals, objects, or responses within the same subgroup have a common characteristic or property as the basis of classification. The sequence or order in which subgroups are listed makes no difference as there is no relationship among subgroups. - Categories are mutually exclusive and exhaustive. - No inherent order or ranking among the categories. **Ordinal Level or rank measurement:** This level involves order or ranking among the categories, but the intervals between the ranks are not necessarily equal. In other words, an ordinal scale has all the properties of a nominal scale -- categorizing individuals, objects, responses or properties into subgroups based on a common characteristic -- but also ranks the subgroups in a certain order. They are arranged in either ascending or descending order according to the extent that a subcategory reflects the magnitude of variation in the variable. **Characteristics:** - Categories are mutually exclusive. - There is a logical order to the categories. - The differences between ranks are not quantifiable. **Examples:** Education level (high school, bachelor\'s, master\'s, doctorate), Satisfaction rating (very unsatisfied, unsatisfied, neutral, satisfied, very satisfied), income level (this can be measured either quantitatively or qualitatively (N100, 000 -- below average, 100,000 -250,000 - average and N250,000 -- above average). The difference between below average and average, for instance are not the same and cannot be quantified. It is called ordinal because it allows the researcher to rank people or issues along a continuum from the greatest amount to the smallest or from the smallest to the greatest amount of the characteristics being measured. **Interval Level:** Description: This level involves ordered categories with equal intervals between the values, but there is no true zero point. Put simply, An interval scale has all the properties of nominal and ordinal scales and includes equal intervals between consecutive values, allowing for meaningful differences to be measured. However, it does not have a true zero point, meaning that zero does not indicate the absence of the variable being measured. Therefore, ratios of numbers on an interval scale are not meaningful. **Characteristics:** - Categories are mutually exclusive. - There is a logical order. - Intervals between values are equal. - No true zero point (zero does not indicate the absence of the variable). **Examples:** Temperature in Celsius or Fahrenheit, IQ scores, Calendar years. - Temperature Example: Consider temperatures in Celsius. The difference between 30°C and 20°C is the same as the difference between 20°C and 10°C, which is 10 degrees. However, 0°C does not represent the complete absence of temperature; it is simply another point on the scale. Because there is no true zero, it is not meaningful to say that 20°C is \"twice as warm\" as 10°C. - IQ Scores Example: IQ scores are also measured on an interval scale. The difference between an IQ score of 120 and 100 is the same as the difference between 100 and 80, which is 20 points. However, an IQ of 0 does not imply the absence of intelligence; it is just a point on the scale. **Ratio Level:** The highest level of measurement, with ordered categories, equal intervals, and a true zero point, which allows for the calculation of ratios. Invariably, a ratio scale has all the properties of nominal, ordinal and interval scales and it also has a starting point fixed at zero. Therefore, it is an absolute scale -- the difference between the intervals is always measured from a zero point. This means the ratio scale can be used for mathematical operations. The measurement of income, age, height and weight are examples of this scale. A person who is 40 years of age is twice as old as a 20-year-old. A person earning \$60 000 per year earns three times the salary of a person earning \$20 000. - **Characteristics:** - Categories are mutually exclusive. - There is a logical order. - Intervals between values are equal. - True zero point (zero indicates the absence of the variable). - **Examples: Height, Weight, Age, Income.** **Summary of Levels of Measurement:** **Level** **Description** **Characteristics** **Examples** -------------- -- --------------------------------------------------------- -- ------------------------------------------------------------------ -- ---------------------------------------- **Nominal** Labels variables without quantitative value Categories are mutually exclusive; no order Gender, Hair color, Nationality **Ordinal** Order or ranking among categories Mutually exclusive; logical order; unequal intervals Education level, Satisfaction rating **Interval** Ordered categories with equal intervals Mutually exclusive; logical order; equal intervals; no true zero Temperature, IQ scores, Calendar years **Ratio** Ordered categories with equal intervals and a true zero Mutually exclusive; logical order; equal intervals; true zero Height, Weight, Age, Income Understanding these levels helps in selecting the appropriate statistical methods and tests for analyzing data. **Approaches to Research: Quantitative, Qualitative and Mixed Methods** The research approaches applicable in communication research emerge from European and American media research traditions. In America, researchers tended to study media quantitatively to achieve objectivity while European researchers tended towards a qualitative approach. **Quantitative Research Method** A quantitative research method refers to the research approach whereby evidence is observed and measured using statistical or mathematical tools. It is characterized by the deductive approach to the research process aimed at proving, disproving, or lending credence to existing theories. This type of research involves measuring variables and testing relationships between variables to reveal patterns, correlations, or causal relationships. Researchers may employ the linear data collection and analysis methods that result in statistical data. The values underlying quantitative research include neutrality, objectivity, and the acquisition of a sizeable scope of knowledge. This method was adopted from the ideology of science originally meant for studying phenomena in natural science, especially physics. It originated in the approach to, or ideology of, science known as positivism. Positivism originated in 17th-century Europe. The development that the French Revolution brought about, together with industrialization and urbanization, led to the need to study society scientifically to restore it to its former harmony and stability. The quantitative method displays all or some of the above characteristics. In other words, the knowledge obtained is the result of objective observation. The approaches that are used are typically utilized in the natural sciences and research is undertaken in society as it stands intending to alleviate social problems. To a large extent, quantitative media research assumes an empirical theory of knowledge. It uses the scientific method. It is based on observation and testing of assumptions (hypotheses) against the evidence of the "real world". As such, it makes provision for three assumptions. i. A universal objective communication reality is available to be studied. ii. people are capable of devising methods of studying communication phenomena. iii. Hypotheses explaining these phenomena are therefore capable of proving and disproving. Among the quantitative communication research problems, a researcher can investigate include the following: Example 1 How balanced is the Nigerian press reportage of the Niger Delta Crisis? This is a communication problem that can be investigated quantitatively. Balance is a variable that has measurable values. Example 2 Prominence of Development News in Nigerian Newspapers. This communication problem can be quantitatively investigated. Prominence is a variable that can be measured. Quantitative research techniques or methods include *experimentation, survey and content analysis.* **Qualitative Research** On the other hand, European investigations were influenced more by historical, cultural and critical interests and were largely shaped by Marxism. This led to the qualitative research approach which rejects the idea of an objective and value-free research. In a nutshell, it is the analysis of visual and verbal data that effect everyday experience (Wimmer & Dominick, 2012). The method emphasizes how people in everyday natural settings create meaning and interpret the events of their world. The method rejects the idea of the permanent character ascribed to knowledge by the quantitative approach. Qualitative research is generally characterized by inductive approaches to knowledge building aimed at generating meaning. Researchers use this approach to explore, investigate, learn about social phenomenon, to unpack the meaning people ascribe to activities, situations and events and to build a depth of understanding about some dimension of social life. Underlying qualitative research is the importance of people's subjective experiences and meaning-making processes (Leavy, 2017). The qualitative method became popular in mass communication research during the 1970s and 1980s and gained added visibility in the 1990s. Some problems that researchers can investigate qualitatively include the following: Example 1: Preventing, managing, and resolving conflicts between oil-producing companies and host communities in Nigeria through the mass media. This is a communication problem that can be investigated qualitatively. This is because; the research problem is located in human activity and the social structure. Example 2 Caregiver's assessment of media messages on polio national immunization in Nigeria. This can also be treated as a qualitative research problem. The media is a social instrument used by both the operators and the audience. Over the years, tension has grown between these two traditions, although considerable influence has flowed both ways as scientific procedures have developed an interest in Europe and critical perspectives have been taken seriously in some parts of America. In Africa, the qualitative method is gradually bracing up to the quantitative method, (Little, 2001). Qualitative research methods include *in-depth interviews, focus group discussion (FGD), observational research, case study, and historical and ethnographic research***.** In these methods, interest is directed toward context-bound conclusions that could potentially point the way to new policies and decisions, rather than towards "scientific" generalizations. **Advantages and Disadvantages of Qualitative and Quantitative Research** Qualitative and quantitative research approaches have certain advantages and disadvantages. **Advantages of qualitative research** 1. Qualitative research allows researchers to view behaviour in a natural setting without the artificiality that sometimes surrounds quantitative research. 2. Qualitative research can increase a researcher's depth of understanding of the phenomenon under investigation. This is especially true when the phenomenon has not been previously investigated. 3. Qualitative research is relatively easier to conduct. 4. Qualitative research methods are flexible and allow the researcher to pursue new areas of interest. For instance, a questionnaire is unlikely to provide data about questions that were not asked but a researcher conducting in-depth interview might discover facets of a subject that were not considered before the study began. **Disadvantages** 1. Sample sizes are usually too small to allow the researcher to generalize the data beyond the sample selected from the particular study. 2. Reliability of the data can also be a problem since single observers are describing unique events. 3. Because a researcher doing qualitative research is most of the time closely involved with the respondents, it is possible for him to lose objectivity when collecting data. (Wimmer & Dominick, 2011). 4. If qualitative research is not properly planned, the outcome may produce nothing of value. **Advantages of Quantitative research** 1. First, the use of numeric data (figures) allows greater precision in reporting results. 2. Secondly, it permits the use of powerful statistical tools thereby making its results more credible. **Disadvantage**s This include the setting i.e., where the research is performed is somewhat artificial and it is more rigorous to conduct than qualitative research. In survey research, respondents purposely deceive researchers by deliberately telling lies. In experimentation, the respondents may just give the researcher the type of response he wants and this may affect the outcome of the research. **Mixed Method Research**: This is an approach that stems from the realization that both quantitative and qualitative techniques are important in understanding any phenomenon. It bridges the gap between quantitative and qualitative research. MMR is also known as triangulation, as used in mass media research. Wimmer and Dominick (2011) define it within this context as the use of both qualitative and quantitative methods to fully understand the nature of a research problem. Leavy (2107), explained it as involving the collection, analysis, and integration of both quantitative and qualitative data in a single project. Here, the research projects are integrated or synergistic, with the quantitative aspect of the work, influencing the qualitative phase or vice versa. MMR may result in a comprehensive understanding of the phenomenon under investigation because of the integration of qualitative and quantitative data. It is generally appropriate when the purpose is to describe, explain or evaluate. **References** Leavy, P. (2017). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based and Community-Based participatory Research Approaches. New York: The Guilford Press Little, W. Stephen (2017). Theories of Human Communication. USA: Wardsworth. Onwubere, C. H., Wilson, D & Esiri, M. (2018). Communication Research. Abuja: NOUN Wimmer, R. D. and Dominick (2011) Mass Media Research: An Introduction Boston: Wadsworth