Psychology questions

1. Discuss and critique research questions of each team member, from the Research Multimedia Activity assignment. Use The Characteristics of High Quality Research on page 3 of Exploring Research in your discussion

High-quality research is characterized by many different attributes, many of which tend to be related to one another and also tend to overlap. High-quality research • is based on the work of others, • can be replicated, • is generalizable to other settings, • is based on some logical rationale and tied to theory, • is doable, • generates new questions or is cyclical in nature, • is incremental, and • is an apolitical activity that should be undertaken for the betterment of society.

1. How effective is strong words or moderate words for reliability and validity on response of attitude scales. ( use information below as reference)

Strong words or moderate words: A comparison of the reliability and validity of responses on attitude scales

Frey, B. B., & Edwards, L. M. (2011). Strong words or moderate words: A comparison of the reliability and validity of responses on attitude scales. Psychology, 2(1), 49-52. doi:http://dx.doi.org/10.4236/psych.2011.21008

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A common assumption in attitude measurement is that items should be composed of strongly worded statements. The presumed benefit of strongly worded statements is that they produce more reliable and valid scores than statements with moderate or weak wording. This study tested this assumption using commonly accepted criteria for reliability and validity. Two forms of attitude scales were created—a strongly worded form and a moderately worded form—measuring two attitude objects—attitude towards animal experimentation and attitude towards going to the movies. Different formats were randomly administered to samples of graduate students. There was no superiority found for strongly worded statements over moderately worded statements. The only statistically significant difference was found between one pair of validity coefficients ( r = 0.69; r = 0.15; Z = 2.60, p ≤ 0.01) and that was in the direction opposite from expected, favoring moderately worded items over strongly worded items (total scores correlated with a general behavioral item). (PsycINFO Database Record (c) 2016 APA, all rights reserved) (Source: journal abstract)

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1007458

Subject

Attitude Measurement (major);
Psychometrics (major);
Test Reliability (major);
Test Validity (major)

1. VIDEO What are some of the ways to organize Quantitative Data. What would be the best way graph to use or continuous data when organizing quantitative Data. http://fod.infobase.com/p_ViewVideo.aspx?xtid=36200#

Reference

Organizing quantitative data [Video file]. (2005). Retrieved April 29, 2017, from https://fod.infobase.com/PortalPlaylists.aspx?wID=18566&xtid=36200

1. Test Yourself You’d be surprised how many important scientific breakthroughs were the result of informal talk (aka “bull”) sessions between people interested in the same or similar topics. Just sitting around and talking about ideas is one of the great pleasures when it comes to learning and scientific discovery. Be a bit creative and list five ideas you have or questions you find particularly interesting about any topic. Don’t worry at this point how you would answer the question but take a few intellectual risks and see what you come up with.

My topic would be age and drug addiction it’s effect on life. ( questions should pertain to this topic)

3A 4. What’s the difference between Contrast qualitative and quantitative methods. Give examples

119 Ch 5 5.What is realiabity and why is it important in research. Give an example

Reliability and Validity: Why They Are Very, Very Important You can have the sexiest-looking car on the road, but if the tires are out of balance, you can forget good handling and a comfortable ride. The tires, or where “the rubber meets the road,” are crucial. Respected levels of reliability and validity are the hallmarks of good measurement practices. In the same way, you can have the most imaginative research question with a well-defined, clearly articulated hypothesis, but if the tools you use to measure the behavior you want to study are faulty, you can forget your plans for success. The reliability (or the consistency) and validity (or the does-what-it-should qualities) of a measurement instrument are essential because the absence of these qualities could explain why you act incorrectly in accepting or rejecting your research hypothesis. For example, you are studying the effect of a particular training program and you are using a test of questionable reliability and validity. Let’s assume for the moment that the treatment truly works well and could be the reason for making significant differences in the groups you are comparing. Because the instrument you are using to assess skills is not consistently sensitive enough to pick up changes in the behavior you are examining, you can forget seeing any differences in your results, no matter how effective the treatment (and how sound your hypothesis). With that in mind, remember: Assessment tools must be reliable and valid; otherwise, the research hypothesis you reject may be correct but you will never know it! Reliability and validity are your first lines of defense against spurious and incorrect conclusions. If the instrument fails, then everything else down the line fails as well. Now we can go on to a more detailed discussion of reliability and validity, what they are, and how they work. A Conceptual Definition of Reliability Here we go again with another set of synonyms. How about dependable, consistent, stable, trustworthy, predictable, and faithful? Get the picture? Something that is reliable will perform in the future as it has in the past. Reliability occurs when a test measures the same thing more than once and results in the same outcomes. You can use any of the synonyms for reliability listed earlier as a starting definition, but it is important to first understand the theory behind reliability. So, let’s begin at the beginning. Reliability consists of both an observed score and a true score component. When we talk of reliability, we talk of scores. Performance for any one person on any variable consists of one score composed of three clearly defined components, as shown in Figure 5.1. Figure 5.1 The components of reliability. The observed score is the score you actually record or observe. It is the number of correct words on a test, the number of memorized syllables, the time it takes to read four paragraphs of prose, or the speed with which a response is given. It can be the dependent variable in your study or any other variable being measured. Any observed score consists of the two other components: true score and error score (see Figure 5.1). The true score is a perfect reflection of the true value of that variable, given no other internal or external influences. In other words, for any person there is only one true score on a particular variable. After repeated measurements, there may be several values for a particular measurement (due to error in the measurement process which we will get to in a minute), but there is only one true one. However, one can never ascertain what that true value is. Why? First, because most variables, such as memory, intelligence, and aggression, cannot be directly measured and, second, because the process of measurement is imperfect. Try as we might, we can never design a test that reflects the true score on any variable or characteristic. Yet, the measurement process and the theory of reliability always assume a true score is there. For example, on a variable such as intelligence, each person has a true score that accurately (and theoretically) reflects that person’s level of intelligence. Suppose that, by some magic, your true intelligence score is 110. If you are then given a test of intelligence and your observed score comes out to be 113, then the test overestimates your IQ. But because the true score is a theoretical concept, there is no way to know that.

Ch 5 6. What are the different types of reliablity?

Different types of reliability used for different purposes. However, no matter what type of assessment device you use, reliability is an essential quality that must be established before you test your hypothesis.

4 Type of Reliability

Test–retest- A measure of stability

Parallel-forms- A measure of equivalence

Inter-rater- A measure of agreement

Internal consistency- A measure of how consistently each item measures the same underlying construct

What It Is How You Do It What the Reliability Coefficient Looks Like Test–retest A measure of stability Administer the same test/measure at two different times to the same group of participants Parallel-forms A measure of equivalence Administer two different forms of the same test to the same group of participants Inter-rater A measure of agreement Have two raters rate behaviors and then determine the amount of agreement between them Percentage of agreements Internal consistency A measure of how consistently each item measures the same underlying construct Correlate performance on each item with overall performance across participants • Cronbach’s alpha • Kuder-Richardson

Ch 5 7. What Makes Up Error Scores? Lists some examples of major sources of error which can affect test scores from one testing situation to the next.

The error score is all of those factors that cause the true score and the observed score to differ. For example, Mike might get 85 of 100 words correct on a spelling test. Does this mean that Mike is an “85% correct speller” on all days on all tests of spelling? Not quite. It means that on this day, for this test, Mike got 85 of 100 words correct. Perhaps tomorrow, on a different set of 100 words, Mike would get 87 or 90 or even 100 correct. Perhaps, if his true spelling ability could be measured, it would be 88. Why are there differences between his true score (88) and his observed score (85)? In a word, error. Whose or what error? You’ll find out about that in a moment. Perhaps Mike did not study as much as he should have, or perhaps he did not feel well. Perhaps he could not hear the teacher’s reading of each word. Perhaps the directions telling him where he was supposed to write the words on the test form were unclear. Perhaps his pencil broke. Perhaps, perhaps, perhaps . . . . All of these factors are sources of error. Repeated scores on almost any variable are nearly always different from one another because the trait being assessed changes from moment to moment, and the way in which the trait is assessed also changes (albeit ever so slightly) and is not perfect (which no measurement device is). What Makes Up Error Scores? Let’s go beyond the catchall of error scores. You can see in Figure 5.1 that error scores are made up of two elements that help to explain why true and observed scores differ. Both trait and method errors contribute to the unreliability of tests. The first component of error scores is called method error, which is the difference between true and observed scores resulting from the testing situation. For example, you are about to take an exam in your introductory psychology class. You have studied well, attended reviews, and feel confident that you know the material. When you sit down to take the test, however, there are matching items (which one in Column A goes with Column B?) and crossword puzzle–like items, and you were expecting multiple choice. In addition, the directions as to how to do the matching are unclear. Instead of reaching your full potential on the test (or achieving as close to your true score as possible), you score lower. The error between the two results from the method error—unclear instructions and so on. The second component is trait error. Here, the reason for the difference between the true and observed scores is characteristic of the person taking the test. For example, if you forgot your glasses and cannot read the problems, or if you did not study, or if you just do not understand the material, then the source of the difference between the true score (what you really know if nothing else interferes) and the score you get on the test (the observed score) is a result of trait errors.

8. What are some helpful things you can do to help you figure out if your first idea for a research study is the best one?

Ch 11 9. What is the importance and role of experimental designs

One tool that can assist in understanding the search for these differences is the true experimental research method. Unlike any of the other methods discussed thus far, the experimental method tests for the presence of a distinct cause and effect. This means that once this method is used, the judgment can be made that A does cause B to happen or that A does not cause B to happen. Other methods, such as historical and descriptive models, do not offer that luxury. Although they can be used to uncover relationships between variables, there is no way that a causal relationship can be established.

Experimental Designs There is a variety of types of experimental designs. In this section, you will find a description of the set made famous by Donald Campbell and Julian Stanley in their 1963 monograph “Experimental and Quasi-Experimental Design for Research on Teaching,” which helped revolutionize the way in which research projects are planned and conducted. Quasi-experimental designs are also known as causal-comparative designs. Campbell and Stanley identified three general categories of research designs: pre-experimental, true experimental, and quasi-experimental. (Quasi-experimental designs are also referred to as causal-comparative designs.) This chapter will discuss the pre-experimental and true experimental designs; Chapter 12 covers quasi-experimental design. The most significant difference among these types of experimental designs is the degree to which they impose control on the variables being studied. The pre-experimental method has the least amount of control, the true experimental method has the most, and the quasi-experimental method is somewhere in the middle. The more control a design allows, the easier it is to attribute a cause-and-effect sequence of events. Another way in which these three designs differ from one another is the degree of randomness that enters into the design. You already know that the word “random” implies an equal and independent chance of being selected, but that definition and concept can be applied beyond the selection of a sample of subjects from a population to the concept’s importance in experimental design. The point at which random assignment enters the process distinguishes different types of experimental designs from one another. Actually, different steps need to be taken to ensure the quality of true randomness in the best of all experimental designs. The first step is one you know most about, the random selection of subjects from a population to form a sample. This is the first procedure you would undertake in an experiment. Now you have a sample. Second, you want to assign subjects randomly to different groups. You want to make sure, for example, that subjects assigned to group 1 had an equal chance of being assigned to group 2. Finally (if you followed steps 1 and 2), you have two groups you can assume are equivalent to each other. Now you need to decide which of the two groups will receive the treatment or, if you have five groups, which treatment each group will receive. In the same way that you used a table of random numbers in previous examples, you assign (at random) different treatments to the groups. By following these steps, you can ensure that: 1. The subjects are randomly selected from a population and randomly assigned to groups. 2. Which group receives which treatment is decided randomly as well.

1. How effective is strong words or moderate words for reliability and validity on response of attitude scales. ( use information below as reference)

2. What are some of the ways to organize Quantitative Data. What would be the best way graph to use or continuous data when organizing quantitative Data.

3. Test Yourself You’d be surprised how many important scientific breakthroughs were the result of informal talk (aka “bull”) sessions between people interested in the same or similar topics. Just sitting around and talking about ideas is one of the great pleasures when it comes to learning and scientific discovery. Be a bit creative and list five ideas you have or questions you find particularly interesting about any topic. Don’t worry at this point how you would answer the question but take a few intellectual risks and see what you come up with. See attachment for topic

4. What’s the difference between Contrast qualitative and quantitative methods. Give examples

5. What is realiabity and why is it important in research. Give an example

6. What are the different types of reliablity?

7. What Makes Up Error Scores? Lists some examples of major sources of error which can affect test scores from one testing situation to the next.

8. What is the importance and role of experimental designs

9. What are some helpful things you can do to help you figure out if your first idea for a research study is the best one? Please Provide references