psychology questions due thursday
"Ethical Standards Summary." EXPLORING RESEARCH EIGHTH EDITION Neil J. Salkind University of Kansas
Salkind, Neil, N. J. (2012). Exploring research (8th ed.th ed.). New Jersey: Pearson Education, Inc.
1.What makes Bad or Good Evaluating Research Questions and Hypotheses
2.Pose a variety of good and bad research questions. 3 ea
3.Discuss what makes questions good or bad and how the bad ones can be improved.
4. Discuss what makes good or bad hypotheses.
5. Define the words below as relate to psychology and Discuss how different people in may have had different ideas about these definitions--how one variable can be defined in different ways.
Intelligence
Socioeconomic status
Personality
Depression
Happiness
Health
6. What is research.?
7. What is experimental and non experimental research give example
8. When trying to decide which scientific method to use when exploring a question, what is the best rule of thumb to go by?
9. What is the The Relationship Between Independent and Dependent Variables
10. Why is the null hypothesis always a statement of equality? Why can the research hypothesis take on many different forms? Ch 2
11. What are some potential advantages to reading peer-reviewed journal articles instead of relying on information obtained through other online sources such as Wikipedia? What are some potential advantages to using Wikipedia? Ch 3
12. Why good sampling may be the most important step in any research project. How big is big enough? ch4
13. Why measurement is an important part of the research process • What the process of measurement includes ch 5
Research is, among other things, an intensive activity that is based on the work of others and generates new ideas to pursue and questions to answer. Research is a process through which new knowledge is discovered. A theory, such as a theory of motivation, or development, or learning, for example, helps us to organize this new information into a coherent body, a set of related ideas that explain events that have occurred and predict events that may happen. Second, while we’re talking about other studies, research is an activity that can be replicated. If someone conducts a research study that examines the relationship between problem-solving ability and musical talent, then the methods and procedures (and results) of the experiment should be replicable with other groups for two reasons.
A hypothesis results when the questions are transformed into statements that express the relationships between variables such as an “if . . . then” statement. For example, if the question is, “What effects does using Facebook have on the development of friendships?” then the hypothesis could be, adolescents who use Facebook as their primary means of maintaining social contact have fewer close friends. Several characteristics make some hypotheses better than others. Whereas a null hypothesis is a statement of no relationship between variables, a research hypothesis is a definite statement of the relationship between two variables. For example, for each of the null hypotheses stated earlier, there is a corresponding research hypothesis. Notice that I said “a” and not “the” corresponding research hypothesis, because there can certainly be more than one research hypothesis for any one null hypothesis. Here are some research hypotheses that correspond with the null hypotheses mentioned earlier. Research hypotheses are statements of inequality. • The average score of ninth graders is different from the average score of twelfth graders on the ABC memory test. • There is a relationship between personality type and job success. • Voting patterns are a function of political party. • The brand of ice cream preferred is related to the buyer’s age, gender, and income. Each of these four research hypotheses has one thing in common: They are all statements of inequality. Unlike the null hypothesis, these research hypotheses posit a relationship between variables, not an equality. The nature of this inequality can take two different forms: directional and nondirectional. If the research hypothesis posits no direction to the inequality (such as “different from”), then the research hypothesis is a nondirectional research hypothesis. If the research hypothesis posits a direction to the inequality (such as “more than” or “less than”), then the research hypothesis is a directional research hypothesis.
A good hypothesis provides a transition from a problem statement or question into a form that is more amenable to testing using the research methods we are discussing. The following sections describe the two types of hypotheses—the null hypothesis and the research hypothesis—and how they are used, as well as what makes a good hypothesis.
Questions can be as broad as inquiring about the effects of social media on peer groups, or as specific as the relationship between the content of social media transactions and acceptance by peers. Whatever their content or depth of inquiry, questions are the first step in any scientific endeavor. Identifying the Important Factors Once the question has been asked, the next step is to identify the factors that have to be examined to answer the question. Such factors might range from the simplest, such as an adolescent’s age or socioeconomic status, to more complicated measures, such as the daily number of face-to-face interactions. For example, the following list of factors have been investigated over the past 10 years by various researchers who have been interested in the effects of social media: • age and gender of the adolescent, • ethnicity, • level of family education
True experimental research examines direct cause-and-effect relationships. True Experimental Quasi-experimental
Nonexperimental research examines the relationship between variables, without any attention to cause-and-effect relationships,
The important distinction between nonexperimental methods and the others you will learn about later is that nonexperimental research methods do not set out, nor can they test, any causal relationships between variables. For example, if you wanted to survey the social media–using behavior of adolescents, you could do so by having them maintain a diary in which they record what tools they use and for how long.
Independent and dependent variables are the two kinds of variables that you will deal with most often throughout Exploring Research. However, there are other variables that are important for you to know about as well, because an understanding of what they are and how they fit into the research process is essential for you to be an intelligent consumer and to have a good foundation as a beginning producer of research. A control variable is a variable that has a potential influence on the dependent variable; consequently, the influence must be removed or controlled. For example, if you are interested in examining the relationship between reading speed and reading comprehension, you may want to control for differences in intelligence, because intelligence is related both to reading speed and to reading comprehension. Intelligence must be held constant for you to get a good idea of the nature of the relationship between the variables of interest. An extraneous variable is a variable that has an unpredictable impact upon the dependent variable. For example, if you are interested in examining the effects of television watching on achievement, you might find that the type of television programs watched is an extraneous variable that might affect achievement. Such programs as Discovery, Nova, and Sesame Street might have a positive impact on achievement, whereas other programs might have a negative impact. A moderator variable is a variable that is related to the variables of interest (such as the dependent and independent variable), masking the true relationship between the independent and dependent variable. For example, if you are examining the relationship between crime rate and ice cream consumption, you need to include temperature because it moderates that relationship. Otherwise, your conclusions will be inaccurate.
Finally, there’s the hugely popular and successful Wikipedia (at http://www.wikipedia.org/), an encyclopedia that is almost solely based on the contributions of folks like you and me. At this writing, Wikipedia contains over 3,000,000 articles on absolutely everything you can think of. This may be the perfect online place to start your investigations. Trustworthy? To a great extent, yes. Wikipedia is monitored by content experts, and a recent study found that the venerable Encyclopedia Britannica had more factual errors than did Wikipedia. And, of course, the great thing about any wiki (and it is a general term for anything built on the contributions of many people and open for editing by anyone as well) is that the facts, if incorrect initially, will surely be changed or modified. The Wikipedia site also contains other wikis, including Wiktionary (a dictionary), Wikinews, Wikiquotes, and more. Just exploring the encyclopedia and these ancillaries is fun. Finally, one especially useful source that you should not overlook is The Statistical Abstract of the United States, published yearly by the U. S. Department of Commerce (http://www.census.gov/statab/www). This national database about the United States includes valuable, easily accessible information on demographics and much more.
Measurement Importance< One way to measure height is simply to place people in categories such as A and B, without any reference to their actual size in inches, meters, or feet. Here, the level of measurement is called nominal because people are assigned to groups based on the category to which they belong. A second strategy would be to place people in groups that are labeled along some dimension, such as Tall and Short. People are still placed in groups, but at least there is some distinction beyond a simple categorical label. In other words, the labels Tall and Short have some meaning in the context they are used, whereas Category A and Category B tell us only that the groups are different, but the nature of the difference is not known. In the second strategy, the level of measurement is called ordinal. A third strategy is one in which Rachael is found to be 5 inches taller than Gregory. Now we know that there is a difference between the two measurements and we also know the precise extent of that difference (5 inches). Here, the level of measurement is called interval. Finally, the height of an object or a person could even be measured on a scale that can have a true zero. Although there can be problems in the social and behavioral sciences with this ratio level of measurement, it has its advantages, as you shall read later in this chapter. This level of measurement is called ratio. Keep in mind three things about this whole idea of level of measurement: 1. In any research project, an outcome variable belongs to one of these four levels of measurement. The key, of course, is how the variable is measured. 2. The qualities of one level of measurement (such as nominal) are also characteristic of the next level up. In other words, variables measured at the ordinal level also contain the qualities of variables measured at the nominal level. Likewise, variables measured at the interval level contain the qualities of variables measured at both the nominal and ordinal levels. For example, if you know that Lew is 60 inches tall and Linda is 54 inches tall (interval or possibly ratio level of measurement), then Lew is taller than Linda (ordinal level of measurement) and Lew and Linda differ in height (nominal level of measurement). 3. The more precise (and higher) the level of measurement, the more accurate the measurement process will be and the closer you will come to measuring the true outcome of interest.
One way to measure height is simply to place people in categories such as A and B, without any reference to their actual size in inches, meters, or feet. Here, the level of measurement is called nominal because people are assigned to groups based on the category to which they belong. A second strategy would be to place people in groups that are labeled along some dimension, such as Tall and Short. People are still placed in groups, but at least there is some distinction beyond a simple categorical label. In other words, the labels Tall and Short have some meaning in the context they are used, whereas Category A and Category B tell us only that the groups are different, but the nature of the difference is not known. In the second strategy, the level of measurement is called ordinal. A third strategy is one in which Rachael is found to be 5 inches taller than Gregory. Now we know that there is a difference between the two measurements and we also know the precise extent of that difference (5 inches). Here, the level of measurement is called interval. Finally, the height of an object or a person could even be measured on a scale that can have a true zero. Although there can be problems in the social and behavioral sciences with this ratio level of measurement, it has its advantages, as you shall read later in this chapter. This level of measurement is called ratio. Keep in mind three things about this whole idea of level of measurement: 1. In any research project, an outcome variable belongs to one of these four levels of measurement. The key, of course, is how the variable is measured. 2. The qualities of one level of measurement (such as nominal) are also characteristic of the next level up. In other words, variables measured at the ordinal level also contain the qualities of variables measured at the nominal level. Likewise, variables measured at the interval level contain the qualities of variables measured at both the nominal and ordinal levels. For example, if you know that Lew is 60 inches tall and Linda is 54 inches tall (interval or possibly ratio level of measurement), then Lew is taller than Linda (ordinal level of measurement) and Lew and Linda differ in height (nominal level of measurement). 3. The more precise (and higher) the level of measurement, the more accurate the measurement process will be and the closer you will come to measuring the true outcome of interest.
