Psychology a concise introduction 5th edition pdf download free*

Your Web browser is not enabled for JavaScript. Some features of WorldCat will not be available. Create lists, bibliographies and reviews: or. Search WorldCat Find items in libraries near you. Advanced Search Find a Library. The reduction of anxiety in the experimental group participants in our aerobic exercise experiment may be partially or completely due to a placebo effect.

This is why researchers add a control group called the placebo group to control for the possible placebo effect. A placebo group is a group of participants who believe they are receiving treatment, but they are not.

They get a placebo—an inactive pill or a treatment that has no known effects. For example, the participants in a placebo group in the aerobic exercise exoperational definition A description of the operations or procedures that periment would be told that they are getting an antianxiety a researcher uses to manipulate or drug, but they would only get a placebo in this case, a pill measure a variable.

The complete design for the placebo effect Improvement due to aerobic exercise experiment including the experimental, the expectation of improving because of receiving treatment. For the experimenter to conclude that there is a placebo effect, the placebo group A control group of participants who believe they are reduction of anxiety in the placebo group would have to be receiving treatment, but who are only significantly greater than the reduction for the control group.

For the experimenter to conclude that the reduction of anxiplacebo An inactive pill or a treatety in the experimental group is due to aerobic exercise and ment that has no known effects. The placebo group controls for the placebo effect, and the control group provides a baseline level of anxiety reduction for participants who do not participate in the aerobic exercise program or receive a placebo. The level of anxiety reduction for each group is determined by comparing the measurements of the dependent variable level of anxiety before and after manipulating the independent variable aerobic exercise.

We use what are called inferential statistical analyses—statistical analyses that allow researchers to draw conclusions about the results of their studies. Such analyses tell the researcher the probability that the results of the study are due to random variation chance.

Obviously, the experimenter would want this probability to be low. Thus, a significant finding is one that is probably not due to chance. A statistiresearchers to draw conclusions about the results of a study by detercally significant finding with little practical value sometimes mining the probability the results are occurs when very large samples are used in a study.

With such due to random variation chance. Belmont and Marolla analyzed intelligence test data for almost , year-old Dutch males. There was a clear birth-order effect: First borns scored significantly higher than second borns, second borns higher than third borns, and so on.

However, the score difference between these groups was very small only a point or two and thus not of much practical value. So remember, statistically significant findings do not always have practical significance. The aerobic exercise experiment would also need to include another control measure, the double-blind procedure.

In the double-blind procedure, neither the experimenters nor the participants know which participants are in the experimental and control groups. It is not unusual for participants to be blind to which group they have been assigned.

This is especially critical for the placebo group participants. If they were told that they were getting a placebo, there would be no expectation for getting better and no placebo effect. But why should the experimenters not know the group assignments of the participants? This is to control for the effects of experimenter expectation Rosenthal, , If the experimenters knew which condition the participants were in, they might unintentionally treat them differently and thereby affect their behavior.

In addition, the experimenters might interpret and record the behavior of the participants differently if they needed to make judgments about their behavior their anxiety level in the example study. The key for the participant assignments to groups is kept by a third party and then given to the experimenters once the study has been conducted. In most experiments, the researcher examines multiple values of the independent variable.

With respect to the aerobic exercise variable, the experimenter might examine the effects of different amounts or different types of aerobic exercise. Such manipulations would provide more detailed information about the effects of aerobic exercise on anxiety. An experimenter might also manipulate more than one independent variable. For example, an experimenter might manipulate diet as well as aerobic exercise.

Different diets high-protein vs. If aerobic exercise reduces anxiety, then it might also that combines the results of a large number of studies on one experireduce depression.

As an experimenter increases the number of mental question into one analysis to values of an independent variable, the number of independent arrive at an overall conclusion variables, or the number of dependent variables, the possible gain in knowledge about the relationship between the variables increases.

Thus, most experiments are more complex in design than our simple example with an experimental group and two control groups. In addition, many experimental studies including replications are necessary to address just one experimental question.

Researchers now, however, have a statistical technique called meta-analysis that combines the results for a large number of studies on one experimental question into one analysis to arrive at an overall conclusion.

Because a meta-analysis involves the results of numerous experimental studies, its conclusion is considered much stronger evidence than the results of an individual study in answering an experimental question.

The various research methods that have been discussed are summarized in Table 1. Their purposes and data-gathering procedures are described. Table 1. There are three descriptive methods—observation, case studies, and surveys. Observational studies can be conducted in the laboratory or in a natural setting naturalistic observation. Sometimes participant observation is used. In participant observation, the observer becomes a part of the group being observed.

The main goal of all observation is to obtain a detailed and accurate description of behavior. A case study is an in-depth study of one individual.

Hypotheses generated from case studies in a clinical setting have often led to important experimental findings. Surveys attempt to describe the behavior, attitudes, or beliefs of particular populations groups of people.

It is essential in conducting surveys to ensure that a representative sample of the population is obtained for the study. Random sampling in which each person in the population has an equal opportunity to be in the sample is used for this purpose. Descriptive methods only allow description, but correlational studies allow the researcher to make predictions about the relationships between variables.

In a correlational study, two variables are measured and these measurements are compared to see if they are related. A statistic, the correlation coefficient, tells us both the type of the relationship positive or negative and the strength of the relationship.

Zero and values near zero indicate no relationship. As the absolute value approaches 1. Correlational data may also be depicted in scatterplots. A positive correlation is indicated by data points that extend from the bottom left of the plot to the top right. Scattered data points going from the top left to the bottom right indicate a negative correlation. The strength is reflected in the amount of scatter—the more the scatter, the lower the strength. A correlation of 1. To draw cause-effect conclusions, the researcher must conduct well-controlled experiments.

In a simple experiment, the researcher manipulates the independent variable the hypothesized cause and measures its effect upon the dependent variable the variable hypothesized to be affected. These variables are operationally defined so that other researchers understand exactly how they were manipulated or measured.

In more complex experiments, more than one independent variable is manipulated or more than one dependent variable is measured. The experiment is conducted in a controlled environment in which possible third variables are held constant; the individual characteristics of participants are controlled through random assignment of participants to groups or conditions.

Other controls used in experiments include using a control group that is not exposed to the experimental manipulation, a placebo group, which receives a placebo to control for the placebo effect, and the double-blind procedure to control for the effects of experimenter and participant expectation. The researcher uses inferential statistics to interpret the results of an experiment.

These statistics determine the probability that the results are due to chance. For the results to be statistically significant, this probability has to be very low,. Statistically significant results, however, may or may not have practical significance or value in our everyday world. Because most experimental questions lead to many studies including replications, meta-analysis, a statistical technique that combines the results of a large number of experiments on one experimental question into one analysis, can be used to arrive at an overall conclusion.

Autism rates were higher in counties with higher precipitation levels. Try to identify some possible third variables that might be responsible for this correlation. To do this, we need to use statistics. There are two types of statistics—descriptive and inferential. We described inferential statistics when we discussed how to interpret the results of experimental studies. The correlation coefstudy in a concise fashion.

For experimental findings, we need two types of departicipants frequency receiving scriptive statistics to summarize our data— measures of each score for a variable. In addition, a researcher often constructs a frequency distribution for the data. A frequency distribution depicts, in a table or a graph, the number of participants receiving each score for a variable. The bell curve, or normal distribution, is the most famous frequency distribution. We begin with the two types of descriptive statistics necessary to describe a data set: measures of central tendency and measures of variability.

Descriptive Statistics In an experiment, the data set consists of the measured scores on the dependent variable for the sample of participants. A listing of this set of scores, or any set of numbers, is referred to as a distribution of scores, or a distribution of numbers. To describe such distributions in a concise summary manner, we use two types of descriptive statistics: measures of central tendency and measures of variability. The first is one that you are already familiar with—the mean or average.

The mean is the numerical average for a distribution of scores. To compute the mean, you merely add up all of the scores and divide by the number of scores. A second measure of central tendency is the median—the score positioned in the middle of the distribution of scores when all of the scores are listed from the lowest to the highest.

If there is an odd number of scores, the median is the middle score. If there is an even number of scores, the median is the halfway point between the two center scores. The final measure of central tendency, the mode, is the most frequently occurring score in a distribution of scores.

Sometimes there are two or more scores that occur most frequently. In these cases, the distribution has multiple modes. That gives us a distribution of five test scores: 70, 80, 80, 85, and The sum of all five scores is Now divide by 5, and you get the mean, If there had been an even number of scores, the median would be the halfway point between the center two scores.

For example, if there had been only four scores in our sample distribution 70, 80, 85, and 85 , the median would be the halfway point between 80 and 85, For the distribution of five scores, there are two numbers that occur twice, so there are two modes—80 and This kind of distribution is referred to as a bimodal distribution a distribution with two modes.

Remember that a distribution can have one or more than one mode. Of the three measures of central tendency, the mean is the one that is most commonly used. This is mainly because it is used to analyze the data in many inferential statistical tests.

The mean can be distorted, however, by a small set of unusually high or low scores. In this case, the median, which is not distorted by such scores, should be used. The median distribution of scores. The mean is distorted because it middle of a distribution of scores when all of the scores are arranged has to average in the value of any unusual scores. Measures of variability. In addition to knowing the typical score for a distribution, you need to determine the variability between the scores.

There are two measures of variability—the range and the standard deviation. The range is the simpler to compute. However, like the mean, unusually high or low scores distort the range.

For example, if the 70 in the distribution had been a 20, the range would change to be 85 minus 20, or The measure of variability used most often is the standard deviation. In general terms, the standard deviation is the average extent that the scores vary from the mean of the distribution.

If the scores do not vary much from the mean, the standard deviation will be small. If they vary a lot from the mean, the standard deviation will be larger.

However, if the scores had been 20, 40, 80, , and , the mean would still be 80; but the scores vary more from the mean, therefore the standard deviation would be much larger. The standard deviation and the various other descriptive statistics that we have discussed are summarized in Table 1. Review this table to make sure you understand each statistic. The standard deviation is especially relevant to the normal distribution, or bell curve. We will see in Chapter 6, on thinking and intelligence, that intelligence test scores are actually determined with respect to standard deviation units in the normal distribution.

Next we will consider the normal distribution and the two types of skewed frequency distributions. It tells us how often each score occurred. These frequencies can be presented in a table or visually in a figure.

For many human traits such as height, weight, and intelligence , the frequency distribution takes on the shape of a bell curve. In fact, if a large number of people are measured on almost anything, the frequency distribution will visually approximate a bell-shaped curve.

Statisticians call this bell-shaped frequency distribution, shown in Figure 1. Normal distributions. There are two main aspects of a normal distribution. First, the mean, the median, and the mode are all equal because the normal distribution is symmetric about its center.

You do not have to worry about which measure of central tendency to use because all of them are equal. The same number of scores fall below the center point as above it. Second, the percentage of scores falling within a certain number of standard deviations of the mean is set. About 68 percent of the scores fall within 1 standard deviation of the mean; about 95 percent within 2 standard deviations of the mean; and over 99 percent within 3 standard deviations of the mean.

These percentages are what give the normal distribution its bell shape. Figure 1. About 68 percent of the scores mean but different standard deviations. Both have bell shapes, fall within 1 standard deviation of the mean, about 95 percent within but the distribution with the smaller standard deviation A is 2 standard deviations of the mean, taller.

As the size of the standard deviation increases, the bell and over 99 percent within 3 standard shape becomes shorter and wider like B. In addition, about 68 percent of the scores fall within 1 standard deviation of the mean, about 95 percent within 2 standard deviations of the mean, and over 99 percent within 3 standard deviations of the mean.

Normal distribution A has a smaller standard deviation than normal distribution B. As the standard deviation for a normal distribution gets smaller, its bell shape gets narrower and taller. B Mean The percentages of scores and the number of standard deviations from the mean always have the same relationship in a normal distribution.

This allows you to compute percentile ranks for scores. A percentile rank is the percentage of scores below a specific score in a distribution of scores.

For example, the percentile rank of a score that is 1 standard deviation above the mean is roughly 84 percent. Remember, a normal distribution is symmetric about the mean so that 50 percent of the scores are above the mean and 50 percent are below the mean. What is the percentile rank for a score that is 1 standard deviation below the mean?

Remember that it is the percentage of the scores below that score. Look at Figure 1. What percentage of the scores is less than a score that is 1 standard deviation below the mean? The answer is about 16 percent.

You can never have a percentile rank of percent because you cannot outscore yourself, but you can have a percentile rank of 0 percent if you have the lowest score in the distribution. The scores on intelligence tests and the SAT are based on normal distributions, therefore percentile ranks can be calculated for these scores. We will return to the normal distribution when we discuss intelligence test scores in Chapter 6. Skewed distributions. In addition to the normal distri- bution, two other types of frequency distributions are important.

They are called skewed distributions, which are frequency distributions that are asymmetric in shape. The two major types of skewed distributions are illustrated in Figure 1. In a rightskewed distribution, the mean is greater than the median because the unusually high scores distort it. The mean is less than the median because the unusually low scores distort it. A left-skewed distribution is a frequency distribution in which there are some unusually low scores [shown in Figure 1.

An easy way to remember the difference is that the tail of the right-skewed distribution goes off to the right, and the tail of the left-skewed distribution goes off to the left. A rightskewed distribution is also called a positively skewed distribution the tail goes toward the positive end of the number line ; a left-skewed distribution a negatively skewed distribution the tail goes toward the negative end of the number line.

As you read these examples, visually think about what the distributions would look like. Remember, the tail of a right-skewed distribution goes to the right the high end of the scale , and the tail of a left-skewed distribution goes to the left the low end of the scale. The incomes of most people tend to be on the lower end of possible incomes, but some people make a lot of money, with very high incomes increasingly rare.

The size of most families is 3 or metric frequency distribution in 4, some are 5 or 6, and greater than 6 is increasingly rare. I recommend that you check the cost To get a cheap price or good deal. Griggs Jump to. Sections of this page. Accessibility Help. Email or Phone Password Forgot account? Psychology is no suspicion instead it is a series of research and studies one has to do.

No information is taken from just mere thinking. Every psychological theory has to have what is called empirical evidence there was no such thing as out of the blue theories. These are things one needed time to research and perfect before presenting them to the world. Psychology is all facts and no fiction. Psychology involves our daily living in all aspects and there is no escaping it at all. So, what do we learn from this book?

You get to learn the definition of psychology, it's goals and the perspectives it has. Know how psychology enables one to analyze others in different aspects of life. Learn how psychology is applied in every field that concerns our lives. Also, learn about hu. This introductory textbook presents a coherent overview of the theory, methodology and potential application of narrative psychological approaches.

It compares narrative psychology with other social constructionist approaches and argues that the experience of self only takes on meaning through specific linguistic, historical and social structures. The author shows how the choice of one narrative over another - for example arising out of dominant narrative structures of power and control - can have serious social and psychological implications for the construction of images of self, responsibility, blame and morality.

Theoretical approaches are introduced and an overview of methods is provided, encouraging individuals to apply these theories to their own autobiographies. Such theories are further illustrated with case-study material drawing on physical illness HIV infection and childhood sexual abuse.

Each of these issues is examined in a way which demonstrates how different contemporary narratives and discourses are used to construct meaning and a sense of coherent identity in the face of traumatic events which break down temporal coherence and order. Taken as a whole, this book represents essential reading for students and researchers interested in narrative psychology.

Were you ever in a place in your life where you questioned if your thoughts were even your own? Amel, Susan M. Koger, Christie M. Lewis Blackburn, Thomas J. NET 2. Lockard PT PhD. Dattoli, Kent Wallner, Michael S. Drench Ph. Coley, Cynthia A. Gabbard, Gleno Gabbard. Pangrazi, Aaron Beighle, Deb Pangrazi. By Michael R. Eades, Mary Dan Eades. Namy, Nancy J. Gabbard, M. Locke, Waneen W. Wyrick Spirduso, Stephen J. Good, Jefferson E.

Adams By Paul Kleinman. Meyer, Linda F. Jowett, Victoria J. Heatherton, Michael S. Ayala, Rafael Bernabe. Weinberg, Peggy A. Othmer - EEG Institute. Lucas II. Friedman, Richard Levak, James T. Webb, Dave Nichols. John E. Bunch II. Michael Furr, Verne R. Garth Davis, Howard Jacobson. Melton, John Petrila, Norman G.

Poythress, Christopher Slobogin. Hendrix, Darrell C. Hayes, Pallavi Damani Kumar. Videbeck PhD RN. Rajegopal, P. McGuin, J. Petsko, Dagmar Ringe. Kenny MD. Marinelli, Lizette Mujica Laughlin. Schultz, Sydney Ellen Schultz. Grahame Holmes, Thomas A. Breggin MD.

Theodoulou, Matthew A. NET 4. Scott, James C. Abbott, Stanley Trosset. Pratt, Kenneth J. Gill, Nora M. Barrett, Melissa M. NET 1.