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In chapter 3 of The Practice of Research in Criminology and Criminal Justice there are four areas of focus. They are concepts, measurement operations, evaluation of measures, and levels of measurement. We are going to address conceptualization by using substance abuse and related ideas as examples. For measurement, let us review first how measures of substance abuse have been created, utilizing procedures as available data, questions, observations, and less direct and prominent measures. We will also explain how to assess the validity and reliability of these measures. Finally, the level of measurement reflected in unrelated measures is our last topic. Hopefully, at the end of this you will have a fine comprehension of measurement.
A concept is a mental image tat summarizes a set of similar observations, feelings, or ideas. "Concepts such as substance-free housing require an explicit definition before they are used in research because we cannot be certain that all readers will share the same definition. It is even more important to define concepts such as poverty or social control or strain, we cannot be certain that others know exactly what we mean." The meaning of concepts is often disputed among experts and illuminating the meaning of such concepts does not simply benefit those unfamiliar with them. In order to do ample work of conceptualization, we need more than just a definition, for our concepts. We will probably have to distinguish inner aspects of the concept. Conceptualization is defined as "The process of specifying what we mean by a term. In deductive research, conceptualization helps to translate portions of an abstract theory into testable hypotheses involving specific variables.
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There are four levels of measurement: nominal, ordinal, interval, and ratio. Levels of measurement is defined as; "The complexity of the mathematical means that can be used to express the relationship between a variable's values. The nominal level of measurement, which is qualitative, has no mathematical interpretation; the quantitative levels of measurement are progressively more complex mathematically." The first of the levels is ordinal. At this level, the numbers assigned to cases specify only the order of the cases, permitting greater-than and less-than distinctions; absolute mathematical distinctions cannot be made between categories. An interval-level measure is created by a scale that has fixed measurement units but no absolute, or fixed, zero point. The numbers can be added and subtracted, but ratios are not meaningful, the values must be mutually exclusive and exhaustive. The numbers indicating the values of a variable at the ratio level of measurement represent fixed measuring units and an absolute zero point. Ratio numbers can be added and subtracted, and because the numbers begin at an absolute zero point, they can be multiplied and divides. An important thing to remember is that researchers choose levels of measurement in the process of operationalizing the variables; the level of measurement is not inherent in the variable itself (78-82).
Always remember that measurement validity is a necessary foundation for social research. Without careful conceptualization or conscientious efforts to operationalize, gathering data for key concepts is a wasted effort. If you cannot tell how key concepts were operationalized when you read a research report, don't trust the findings. Planning ahead is the key to achieving valid measurement in your own research.
Ch 4 Sampling
Chapter 4 deals with sampling, we will review the rationale for using sampling in social research and consider two alternatives to sampling. We also will look at the topic of specific sampling methods and when they are most appropriate, using a variety of examples from the criminological research literature. We also briefly introduce the concept of a sampling distribution and explain how it helps in estimating our degree of confidence in statistical generalizations.
Sampling is the fundamental starting point in criminological research. Probability sampling methods allow a researcher to use the laws of chance, or probability, to draw samples from population parameters that can be estimated with a high degree of confidence. A sample of just 1000 or 1500 individuals can be used to reliably estimate the characteristics of the population of a nation comprising millions of individuals. Well-designed samples require careful planning, some advance knowledge about the population to be sampled, and adherence to systematic selection procedures so that the selection procedures are not biased. And even after the sample data are collected, the researcher's ability to generalize from the sample findings to the population is not completely certain. Systematic random sampling is "a variant of simple random sampling and is a little less time consuming. When you systematically select a random sample, the first element is selected randomly from a list or from sequential files, and then every nth element is systematically selected thereafter." The best the researcher can do is to perform additional calculations that state the degree of confidence that can be placed in the sample statistic (101-115).
The alternatives to random, or probability-based, sampling methods are almost always much less palatable, even though they typically are much cheaper. Without a method of selecting cases likely to represent the population in which the researcher is interested, research findings will have to be carefully qualified. Unrepresentative samples may help researchers understand which aspects of a social phenomenon are important, but questions about the generalizability of this understanding are left unanswered. Social scientists often seek to generalize their conclusions from the population that they studied to some larger target population. The validity of generalizations of this type is necessarily uncertain, for having a representative sample of a particular population does not at all ensure that what we find will hold true in other populations. Nonetheless. The accumulation of finds from studies based on local or otherwise unrepresentative populations can provide important information about broader populations (116-119).