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| Five Sources of Knowledge |
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Experience
Authority
Deductive Reasoning
Inductive Reasoning
The Scientific Approach |
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| A generic term for a variety of research approaches that study phenomena in their natural settings, without predetermined hypothesis. |
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| INquiry employing operational definitions to generate numeric data to answer predetermined hypotheses or questions. |
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| A thinkning process in which you proceed from generic to specific statments using prescribed rules of logic. |
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| Beginning with general premises or already known facts and deriving specific logical conclusions. |
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The investigator makes observations on particular events in a class (or category) and then, on the basis of the observed events, makes inferences about the whole class.
It is the reverse of the deductive method. |
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| Requires that the investigator examine every example of a phenomenon. |
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| A system in which you observe a sample of a group and infer from the sample what is characterstic of the entire group. |
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| Research that aims to solve an immediate practical problem. |
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Generally described as a process in which investigators move inductively from their observations to hypothesis to the logical implications of the hypothesis.
A way of seeking knowledge that involves both inductive and deductive reasoning to develop hypotheses that are then subjected to rigorous and objective testing. |
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| Research that aims to obtain empirical data that can be used to formulate, expand, or evaluate theory rather than to solve a practical problem. |
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"A set of interrelated constructs (concepts), definitions, and propositions that present a systematic view of phenomena by specifying relations among variables, with the purpose of explaining and predicting the phenomena" (Kerlinger, 1986, 9).
A theory organizes the findings from many separate observations and investigations into a framework that provides explanations of phenomena. |
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| The principle states that all things being equal, the simplest explanation of a phenomena is preferred over a more complicated explanation. |
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A statement describing relationships among variables that is tentatively assumed to be true.
It identifies observations to be made to investigate a question. |
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| the belief that all natural phenomena have antecedent factors. |
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| an abstraction at a higher level thatn a concept used to explain, interpret, and summarize observations and to form a part of the conceptual content of a theory. |
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| a definition that specifies the procedure or operations to be followed in producing or measuring a concept. |
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| a characteristic that takes on the same value for all indviduals in a study; contrast with variable |
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| a representation of a construct that takes on a range of values. for ex - height, reading test score, gender, etc. |
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| a variable, the measure of which can take an infinite number of points within a range. |
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| Types of Continuous Variables |
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Continuous variables can be classified into three categories:
Interval - Scale Variables:
Interval scale data has order and equal intervals. Interval scale variables are measured on a linear scale, and can take on positive or negative values. It is assumed that the intervals keep the same importance throughout the scale. They allow us not only to rank order the items that are measured but also to quantify and compare the magnitudes of differences between them. We can say that the temperature of 40°C is higher than 30°C, and an increase from 20°C to 40°C is twice as much as the increase from 30°C to 40°C. Counts are interval scale measurements, such as counts of publications or citations, years of education, etc.
Continuous Ordinal Variables:
They occur when the measurements are continuous, but one is not certain whether they are on a linear scale, the only trustworthy information being the rank order of the observations. For example, if a scale is transformed by an exponential, logarithmic or any other nonlinear monotonic transformation, it loses its interval - scale property. Here, it would be expedient to replace the observations by their ranks.
Ratio - Scale Variables:
These are continuous positive measurements on a nonlinear scale. A typical example is the growth of bacterial population (say, with a growth function AeBt.). In this model, equal time intervals multiply the population by the same ratio. (Hence, the name ratio - scale).
Ratio data are also interval data, but they are not measured on a linear scale. . With interval data, one can perform logical operations, add, and subtract, but one cannot multiply or divide. For instance, if a liquid is at 40 degrees and we add 10 degrees, it will be 50 degrees. However, a liquid at 40 degrees does not have twice the temperature of a liquid at 20 degrees because 0 degrees does not represent "no temperature" -- to multiply or divide in this way we would have to use the Kelvin temperature scale, with a true zero point (0 degrees Kelvin = -273.15 degrees Celsius). In social sciences, the issue of "true zero" rarely arises, but one should be aware of the statistical issues involved. |
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Nominal Variables
Nominal variables allow for only qualitative classification. That is, they can be measured only in terms of whether the individual items belong to certain distinct categories, but we cannot quantify or even rank order the categories: Nominal data has no order, and the assignment of numbers to categories is purely arbitrary
Examples:
Gender:
1. Male
2. Female
Marital Status:
1. Unmarried
2. Married
3. Divorcee
4. Widower |
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Ordinal Variables
A discrete ordinal variable is a nominal variable, but its different states are ordered in a meaningful sequence. Ordinal data has order, but the intervals between scale points may be uneven. Because of lack of equal distances, arithmetic operations are impossible, but logical operations can be performed on the ordinal data. A typical example of an ordinal variable is the socio-economic status of families. We know 'upper middle' is higher than 'middle' but we cannot say 'how much higher'. Ordinal variables are quite useful for subjective assessment of 'quality; importance or relevance'. Ordinal scale data are very frequently used in social and behavioral research. Almost all opinion surveys today request answers on three-, five-, or seven- point scales. Such data are not appropriate for analysis by classical techniques, because the numbers are comparable only in terms of relative magnitude, not actual magnitude. |
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Dummy Variables from Quantitative Variables
A quantitative variable can be transformed into a categorical variable, called a dummy variable by recoding the values. Consider the following example: the quantitative variable Age can be classified into five intervals. The values of the associated categorical variable, called dummy variables, are 1, 2,3,4,5.
Example: Age Ranges |
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