Developmental Research Matrix

Respond to the following in a minimum of 175 words:

An experimenter is examining the relationship between age and self-disclosure. A large sample of participants that are 25 to 35 years old and participants that are 65 to 75 years old are compared, and significant differences are found with younger participants disclosing much more than older people. The researcher reports an effect size of .34. What does this mean?

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PART2-SEE ATTACHMENT….Week Six Homework Exercise.

PART3-SEE ATTACHMENT…Developmental Research Matrix. I ONLY HAVE TO ANSWER ONE QUESTION THIS QUESTION “Describe the research method. What will you do? What instruments will you use to measure sexual attitudes”?


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Contrast the three ways of describing results: comparing group percentages, correlating scores, and comparing group means.
Describe a frequency distribution, including the various ways to display a frequency distribution.
Describe the measures of central tendency and variability.
Define a correlation coefficient.
Define effect size.
Describe the use of a regression equation and a multiple correlation to predict behavior.
Discuss how a partial correlation addresses the third-variable problem.
Summarize the purpose of structural equation models.
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STATISTICS HELP US UNDERSTAND DATA COLLECTED IN RESEARCH INVESTIGATIONS IN TWO WAYS: FIRST, STATISTICS ARE USED TO DESCRIBE THE DATA. Second, statistics are used to make inferences and draw conclusions, on the basis of sample data, about a population. We examine descriptive statistics and correlation in this chapter; inferential statistics are discussed in Chapter 13. This chapter will focus on the underlying logic and general procedures for making statistical decisions. Specific calculations for a variety of statistics are provided in Appendix C.

Before looking at any statistics, we need to review the concept of scales of measurement. Whenever a variable is studied, the researcher must create an operational definition of the variable and devise two or more levels of the variable. Recall from Chapter 5 that the levels of the variable can be described using one of four scales of measurement: nominal, ordinal, interval, and ratio. The scale used determines the types of statistics that are appropriate when the results of a study are analyzed. Also recall that the meaning of a particular score on a variable depends on which type of scale was used when the variable was measured or manipulated.

The levels of nominal scale variables have no numerical, quantitative properties. The levels are simply different categories or groups. Most independent variables in experiments are nominal, for example, as in an experiment that compares behavioral and cognitive therapies for depression. Variables such as gender, eye color, hand dominance, college major, and marital status are nominal scale variables; left-handed and right-handed people differ from each other, but not in a quantitative way.

Variables with ordinal scale levels exhibit minimal quantitative distinctions. We can rank order the levels of the variable being studied from lowest to highest. The clearest example of an ordinal scale is one that asks people to make rank-ordered judgments. For example, you might ask people to rank the most important problems facing your state today. If education is ranked first, health care second, and crime third, you know the order but you do not know how strongly people feel about each problem: Education and health care may be very close together in seriousness with crime a distant third. With an ordinal scale, the intervals between each of the items are probably not equal.

Interval scale and ratio scale variables have much more detailed quantitative properties. With an interval scale variable, the intervals between the levels are equal in size. The difference between 1 and 2 on the scale, for example, is the same as the difference between 2 and 3. Interval scales generally have five or more quantitative levels. You might ask people to rate their mood on a 7-point scale ranging from a “very negative” to a “very positive” mood. There is no absolute zero point that indicates an “absence” of mood.

Page 244In the behavioral sciences, it is often difficult to know precisely whether an ordinal or an interval scale is being used. However, it is often useful to assume that the variable is being measured on an interval scale because interval scales allow for more sophisticated statistical treatments than do ordinal scales. Of course, if the measure is a rank ordering (for example, a rank ordering of students in a class on the basis of popularity), an ordinal scale clearly is being used.

Ratio scale variables have both equal intervals and an absolute zero point that indicates the absence of the variable being measured. Time, weight, length, and other physical measures are the best examples of ratio scales. Interval and ratio scale variables are conceptually different; however, the statistical procedures used to analyze data with such variables are identical. An important implication of interval and ratio scales is that data can be summarized using the mean, or arithmetic average. It is possible to provide a number that reflects the mean amount of a variable—for example, the “average mood of people who won a contest was 5.1” or the “mean weight of the men completing the weight loss program was 187.7 pounds.”

Scales of measurement have important implications for the way that the results of research investigations are described and analyzed. Most research focuses on the study of relationships between variables. Depending on the way that the variables are studied, there are three basic ways of describing the results: (1) comparing group percentages, (2) correlating scores of individuals on two variables, and (3) comparing group means.

Comparing Group Percentages
Suppose you want to know whether males and females differ in their interest in travel. In your study, you ask males and females whether they like or dislike travel. To describe your results, you will need to calculate the percentage of females who like to travel and compare this with the percentage of males who like to travel. Suppose you tested 50 females and 50 males and found that 40 of the females and 30 of the males indicated that they like to travel. In describing your findings, you would report that 80% of the females like to travel in comparison with 60% of the males. Thus, a relationship between the gender and travel variables appears to exist. Note that we are focusing on percentages because the travel variable is nominal: Liking and disliking are simply two different categories.

After describing your data, the next step would be to perform a statistical analysis to determine whether there is a statistically significant difference between the males and females. Statistical significance is discussed in Chapter 13; statistical analysis procedures are described in Appendix C.

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Correlating Individual Scores
A second type of analysis is needed when you do not have distinct groups of subjects. Instead, individuals are measured on two variables, and each variable has a range of numerical values. For example, we will consider an analysis of data on the relationship between location in a classroom and grades in the class: Do people who sit near the front receive higher grades?

Comparing Group Means
Much research is designed to compare the mean responses of participants in two or more groups. For example, in an experiment designed to study the effect of exposure to an aggressive adult, children in one group might observe an adult “model” behaving aggressively while children in a control group do not. Each child then plays alone for 10 minutes in a room containing a number of toys, while observers record the number of times the child behaves aggressively during play. Aggression is a ratio scale variable because there are equal intervals and a true zero on the scale.

In this case, you would be interested in comparing the mean number of aggressive acts by children in the two conditions to determine whether the children who observed the model were more aggressive than the children in the control condition. Hypothetical data from such an experiment in which there were 10 children in each condition are shown in Table 12.1; the scores in the table represent the number of aggressive acts by each child. In this case, the mean aggression score in the model group is 5.20 and the mean score in the no-model condition is 3.10.

TABLE 12.1 Scores on aggression measure in a hypothetical experiment on modeling and aggression


Page 246For all types of data, it is important to understand your results by carefully describing the data collected. We begin by constructing frequency distributions.

When analyzing results, researchers start by constructing a frequency distribution of the data. A frequency distribution indicates the number of individuals who receive each possible score on a variable. Frequency distributions of exam scores are familiar to most college students—they tell how many students received a given score on the exam. Along with the number of individuals associated with each response or score, it is useful to examine the percentage associated with this number.

Graphing Frequency Distributions
It is often useful to graphically depict frequency distributions. Let’s examine several types of graphs: pie chart, bar graph, and frequency polygon.

Pie charts Pie charts divide a whole circle, or “pie,” into “slices” that represent relative percentages. Figure 12.1 shows a pie chart depicting a frequency distribution in which 70% of people like to travel and 30% dislike travel. Because there are two pieces of information to graph, there are two slices in this pie. Pie charts are particularly useful when representing nominal scale information. In the figure, the number of people who chose each response has been converted to a percentage—the simple number could have been displayed instead, of course. Pie charts are most commonly used to depict simple descriptions of categories for a single variable. They are useful in applied research reports and articles written for the general public. Articles in scientific journals require more complex information displays.

Bar graphs Bar graphs use a separate and distinct bar for each piece of information. Figure 12.2 represents the same information about travel using a bar graph. In this graph, the x or horizontal axis shows the two possible responses. The y or vertical axis shows the number who chose each response, and so the height of each bar represents the number of people who responded to the “like” and “dislike” options.