social research and what constitutes a relationship between variables
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social research and what constitutes a relationship between variables
For this discussion, please start by reviewing the two documents linked in your Supplemental reading 2 file – these are an overview of what we call variables in social research and what constitutes a relationship between variables. For this post, please choose one of the demographic variables explored in your text: age, race/ethnicity, sex/gender or SES and discuss its relationship with health. In your discussion, please make judicious use of the information from both the main reading and the supplemental resources, and make sure to use your own examples to illustrate your points. this is the articles that you going to use to answer the question. Variables You won’t be able to do very much in research unless you know how to talk about variables. A variable is any entity that can take on different values. OK, so what does that mean? Anything that can vary can be considered a variable. For instance, age can be considered a variable because age can take different values for different people or for the same person at different times. Similarly, country can be considered a variable because a person’s country can be assigned a value. Variables aren’t always ‘quantitative’ or numerical. The variable city consists of text values like New York or Sydney. We can, if it is useful, assign quantitative values instead of (or in place of) the text values, but we don’t have to assign numbers in order for something to be a variable. It’s also important to realize that variables aren’t only things that we measure in the traditional sense. For instance, in much social research and in program evaluation, we consider the treatment or program to be made up of one or more variables (i.e., the cause’ can be considered a variable). An educational program can have varying amounts of ‘time on task’, ‘classroom settings’, ‘student-teacher ratios’, and so on. So even the program can be considered a variable (which can be made up of a number of sub-variables). An attribute is a specific value on a variable. For instance, the variable Student grade has two attributes: pass and fail. Or, the variable agreement might be defined as having five attributes: 1 = strongly disagree 2 = disagree 3 = neutral 4 = agree 5 = strongly agree Another important distinction having to do with the term ‘variable’ is the distinction between an independent and dependent variable. This distinction is particularly relevant when you are investigating cause-effect relationships. It took me the longest time to learn this distinction. (Of course, I’m someone who gets confused about the signs for ‘arrivals’ and ‘departures’ at airports – do I go to arrivals because I’m arriving at the airport or does the person I’m picking up go to arrivals because they’re arriving on the plane!). I originally thought that an independent variable was one that would be free to vary or respond to some program or treatment, and that a dependent variable must be one that depends on my efforts (that is, it’s the treatment). But this is entirely backwards! In fact the independent variable is what you (or nature) manipulates – a treatment or program or cause. The dependent variable is what is affected by the independent variable – your effects or outcomes. For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones. Finally, there are two traits of variables that should always be achieved. Each variable should be exhaustive, it should include all possible answerable responses. For instance, if the variable is “religion” and the only options are “Protestant”, “Jewish”, and “Muslim”, there are quite a few religions I can think of that haven’t been included. The list does not exhaust all possibilities. On the other hand, if you exhaust all the possibilities with some variables – religion being one of them – you would simply have too many responses. The way to deal with this is to explicitly list the most common attributes and then use a general category like “Other” to account for all remaining ones. In addition to being exhaustive, the attributes of a variable should be mutually exclusive, no respondent should be able to have two attributes simultaneously. While this might seem obvious, it is often rather tricky in practice. For instance, you might be tempted to represent the variable “Employment Status” with the two attributes “employed” and “unemployed.” But these attributes are not necessarily mutually exclusive – a person who is looking for a second job while employed would be able to check both attributes! But don’t we often use questions on surveys that ask the respondent to “check all that apply” and then list a series of categories? Yes, we do, but technically speaking, each of the categories in a question like that is its own variable and is treated dichotomously as either “checked” or “unchecked”, attributes that are mutually exclusive. The Nature of a Relationship While all relationships tell about the correspondence between two variables, there is a special type of relationship that holds that the two variables are not only in correspondence, but that one causes the other. This is the key distinction between a simple correlational relationship and a causal relationship. A correlational relationship simply says that two things perform in a synchronized manner. For instance, there has often been talk of a relationship between ability in math and proficiency in music. In general people who are good in one may have a greater tendency to be good in the other; those who are poor in one may also tend to be poor in the other. If this relationship is true, then we can say that the two variables are correlated. But knowing that two variables are correlated does not tell us whether one causes the other. We know, for instance, that there is a correlation between the number of roads built in Europe and the number of children born in the United States. Does that mean that if we want fewer children in the U.S., we should stop building so many roads in Europe? Or, does it mean that if we don’t have enough roads in Europe, we should encourage U.S. citizens to have more babies? Of course not. (At least, I hope not). While there is a relationship between the number of roads built and the number of babies, we don’t believe that the relationship is a causal one. This leads to consideration of what is often termed the third variable problem. In this example, it may be that there is a third variable that is causing both the building of roads and the birthrate, that is causing the correlation we observe. For instance, perhaps the general world economy is responsible for both. When the economy is good more roads are built in Europe and more children are born in the U.S. The key lesson here is that you have to be careful when you interpret correlations. If you observe a correlation between the number of hours students use the computer to study and their grade point averages (with high computer users getting higher grades), you cannot assume that the relationship is causal: that computer use improves grades. In this case, the third variable might be socioeconomic status – richer students who have greater resources at their disposal tend to both use computers and do better in their grades. It’s the resources that drives both use and grades, not computer use that causes the change in the grade point average. Patterns of Relationships We have several terms to describe the major different types of patterns one might find in a relationship. First, there is the case of no relationship at all. If you know the values on one variable, you don’t know anything about the values on the other. For instance, I suspect that there is no relationship between the length of the lifeline on your hand and your grade point average. If I know your GPA, I don’t have any idea how long your lifeline is. Then, we have the positive relationship. In a positive relationship, high values on one variable are associated with high values on the other and low values on one are associated with low values on the other. In this example, we assume an idealized positive relationship between years of education and the salary one might expect to be making. On the other hand, a negative relationship implies that high values on one variable are associated with low values on the other. This is also sometimes termed an inverse relationship. Here, we show an idealized negative relationship between a measure of self-esteem and a measure of paranoia in psychiatric patients. These are the simplest types of relationships we might typically estimate in research. But the pattern of a relationship can be more complex than this. For instance, the figure on the left shows a relationship that changes over the range of both variables, a curvilinear relationship. In this example, the horizontal axis represents dosage of a drug for an illness and the vertical axis represents a severity of illness measure. As dosage rises, severity of illness goes down. But at some point, the patient begins to experience negative side effects associated with too high a dosage, and the severity of illness begins to increase again.
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