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. Age which there are some unusually low at retirement is an example of a left-skewed distribution. Another example would be scores on a relatively easy exam. Because unusually high or low scores distort a mean, such distortion occurs for the means of skewed distributions.
The mean for a right-skewed distribution is distorted toward the tail created by the few high scores and therefore is greater than the median. The mean for the left-skewed distribution is distorted toward the tail created by the few low scores and therefore is less than the median. When you have a skewed distribution, you should use the median because atypical scores in the distribution do not distort the median.
This means that you need to know the type of frequency distribution for the scores before deciding which measure of central tendency—mean or median—is more appropriate. Beware, sometimes the inappropriate measure of central tendency for skewed distributions the mean is used to mislead you Huff, If the distribution of increase in family incomes across all levels of socioeconomic status was skewed, then the median would be the appropriate statistic to use.
The distribution was indeed skewed, severely left-skewed. The growth in family income not only eluded the lower and middle classes, but by and large, it passed up the upper-middle class as well. The huge increases in income went mainly to those with already huge incomes. Thus, in this case the median was the appropriate statistic to use.
Skewed distributions are also important to understand because various aspects of everyday life, such as medical trends mortality rates for various diseases , are often skewed. Stephen Jay Gould, a noted Harvard scientist, died of cancer in However, Gould realized that his expected chances depended upon the type of frequency distribution for the deaths from this disease. Because the statistic is reported as a median rather than a mean, the distribution is skewed.
Now, if you were Gould, which type of skewed distribution would you want—right or left? Look at its shape in Figure 1. If it is 8 months from the origin to the median, then it is less than 4 months from the median to the end of the distribution.
You would want a severely right-skewed distribution with a long tail to the right, going on for years. This is what Gould found the distribution to be when he examined the medical literature on the disease. The distribution had a tail that stretched out to the right for many years beyond the median, and Gould was fortunate to be out in this long tail, living for 20 more years after getting the diagnosis. Such thinking provided him and the many readers of his article on the subject with a better understanding of a very difficult medical situation.
Thinking like a scientist allows all of us to gain a better understanding of ourselves, others, and the world we all inhabit.
Such thinking, along with the accompanying research, has enabled psychological scientists to gain a much better understanding of human behavior and mental processing. We describe the basic findings of this research in the remainder of the book.
You will benefit not only from learning about these findings, but also from thinking more like a scientist in your daily life. Section Summary To understand research findings, psychologists use statistics—a branch of mathematics that provides procedures for the description and analysis of data. In this section, we were concerned with descriptive statistics. There are three such measures: mean, median, and mode. The mean is merely the arithmetical average.
The median is the middle score when the distribution is arranged in ascending or descending order. The mode is the most frequently occurring score. Of these three measures, the mean is used most often. However, if unusually high or low scores in the distribution distort the mean, then the median should be used. In addition to describing the typical score, we need to determine the variability of the scores. We could use the range—the difference between the highest and lowest scores—but unusually low or high scores distort it.
The measure of variability most often used is the standard deviation, the average extent that the scores vary from the mean of the distribution. The standard deviation is especially relevant to the normal bell-shaped frequency distribution.
Sixty-eight percent of the scores in a normal distribution fall within 1 standard deviation of the mean, 95 percent within 2 standard deviations, and over 99 percent within 3 standard deviations.
These percentages hold true regardless of the value of the standard deviation. They also enable us to compute the percentile rank for a specific score in a normal distribution. The percentile rank for a score is the percentage of the scores below it in the distribution of scores.
All distributions are not symmetric like the normal distribution. Two important nonsymmetric distributions are the right-skewed and left-skewed distributions. In a right-skewed distribution, there are some unusually high scores; in a left-skewed distribution, there are some unusually low scores. In both cases, the mean is distorted, therefore the median should be used. They are listed in the order in which they appear in the chapter. For those you do not know, return to the relevant section of the chapter to learn them.
When you think that you know all of the terms, complete the matching exercise based on these key terms. The answers to this exercise follow the answers to the Concept Checks at the end of the chapter. An explanation of a correlation between two variables in terms of another variable that could possibly be responsible for the observed relationship between the two variables. The score positioned in the middle of a distribution of scores when all of the scores are arranged from lowest to highest.
An asymmetric frequency distribution in which there are some unusually high scores that distort the mean to be greater than the median. Improvement due to the expectation of improving because of receiving treatment. A control measure in an experiment in which neither the experimenters nor the participants know which participants are in the experimental and control groups. An inverse relationship between two variables. A control measure in an experiment in which participants are randomly assigned to groups in order to equalize participant characteristics across the various groups in the experiment.
The percentage of scores below a specific score in a distribution of scores. A research perspective whose major explanatory focus is how the brain, nervous system, and other physiological mechanisms produce our behavior and mental processes. A description of the operations or procedures that a researcher uses to manipulate or measure a variable. A visual depiction of correlational data in which each data point represents the scores on the two variables for each participant.
The entire group of people that a researcher is studying. The difference between the highest and lowest scores in a distribution of scores. Statistical analyses that allow researchers to draw conclusions about the results of a study by determining the probability the results are due to random variation chance.
Practice Test Questions The following are practice multiple-choice test questions on some of the chapter content. The answers are given after the Key Terms Exercise answers at the end of the chapter. If you guessed on a question or incorrectly answered a question, restudy the relevant section of the chapter. Which of the following major research perspectives focuses on conditioning by external environmental events as the major cause of our behavior?
Which of the following would be the best procedure for obtaining a representative sample of the students at your school? Which of the following research methods allow s the researcher to draw cause— effect conclusions? Height and weight are correlated; elevation and temperature are correlated. Manipulate is to measure as. This indicates that: a. In an experiment, the pants receive an inactive treatment but are told that the treatment will help them. The most frequently occurring score in a , and the distribution of scores is the average score is the.
In a left-skewed distribution, the mean is than the median; in a right-skewed distribution, the mean is than the median.
Which of the following types of scatterplots depicts a weak, negative correlation? For example, biological explanations will involve actual parts of the brain or chemicals in the brain. Cognitive explanations, however, will involve mental processes such as perception and memory without specifying the parts of the brain or chemicals involved in these processes.
Thus, the biological and cognitive perspectives propose explanations at two different levels of internal factors, the actual physiological mechanisms and the mental processes resulting from these mechanisms, respectively. The behavioral perspective emphasizes conditioning of our behavior by external environmental events while the sociocultural perspective emphasizes the impact of other people and our culture on our behavior and mental processing.
Thus, these two perspectives emphasize different types of external causes. In addition, the behavioral perspective emphasizes the conditioning of observable behavior while the sociocultural perspective focuses just as much on mental processing as observable behavior and on other types of learning in addition to conditioning.
To generalize to a population, you need to include a representative sample of the population in the study. However, the results of a case study do allow the researcher to develop hypotheses about cause-effect relationships that can be tested in experimental research to see if they apply to the population.
Random assignment is a control measure for assigning the members of a sample to groups or conditions in an experiment. Random sampling allows the researcher to generalize the results from the sample to the population; random assignment controls for individual characteristics across the groups in an experiment. Random assignment is used only in experiments, but random sampling is used in experiments and some other research methods such as correlational studies and surveys.
In addition, because they are strong correlations, there would not be much scatter. Thus, the direction of the scatter would be different in the two scatterplots. According to the authors of the study, such variables would include increased television and video viewing, decreased vitamin D levels because of less exposure to sunlight, and increased exposure to household chemicals.
In addition, there may be chemicals in the atmosphere that are transported to the surface by the precipitation. All of these variables could serve as third variables and possibly account for the correlation.
Thus, they cannot be told that they received a placebo. Second, the experimenter must be blind in order to control for the effects of experimenter expectation for example, unintentionally judging the behavior of participants in the experimental and placebo groups differently because of knowing their group assignments. Measures of variability tell us how much the scores vary from one another, the variability between scores. The range is the difference between the highest and lowest scores, and the standard deviation is the average extent that the scores vary from the mean for the set of scores.
As the scores diverge from the mean, they become symmetrically less frequent, giving the distribution the shape of a bell. The opposite is true for the left-skewed distribution. The mean is less than the median because the unusually low scores in the distribution distort it. Answers to Key Terms Exercise 1. It is responsible for our perception, consciousness, memory, language, intelligence, and personality—everything that makes us human.
This would seem to be a daunting job for an organ that only weighs about three pounds. The brain, however, has been estimated to consist of about billion nerve cells, called neurons Thompson, Each neuron may receive information from thousands of other neurons; therefore, the number of possible communication connections between these billions of neurons is in the trillions! In this chapter on neuroscience the scientific study of the brain and nervous system , we will first examine neurons, the building blocks of the nervous system.
We will look at how neurons transmit and integrate information, and how drugs and poisons interrupt these processes and change our behavior and mental processes. We will also consider how some diseases and disorders are related to transmission problems. We will also consider emotions and the role of the autonomic nervous system, a division of the peripheral nervous system, in explaining how our emotional experiences are generated. Next, the major parts of the brain vast collections of neurons and their functions will be detailed.
We will focus mainly on the cerebral cortex, the seat of higher mental functioning in humans. Last, we will consider what consciousness is and what brain activity during sleep a natural break from consciousness tells us about the five stages of sleep and the nature of dreaming. Humans are biological organisms. To understand our behavior and mental processes, we need to understand their biological underpinnings, starting with the cellular level, the neuron.
How we feel, learn, remember, and think all stem from neuronal activity. So, how a neuron works and how neurons communicate are crucial pieces of information in solving the puzzle of human behavior and mental processing. We have a fairly good understanding of how information is transmitted, but we do not have as good an understanding of exactly how these vast communication networks of neurons oversee what we do and make us what we are.
These more complex questions are the remaining key pieces of the puzzle to be solved. In this section, we will cover the part of the story that is best understood—how the building blocks of the nervous system, the neurons, work.
The Structure of a Neuron The brain and the nervous system are composed of two types of cells—neurons and glial cells. Neurons are responsible for information transmission throughout the nervous system.
They receive, send, and integrate information within the brain and the rest of the nervous system. The number of neurons we have is impressive, but we have about 10 times more glial cells to support the work of billions of neurons. Glial cells are only about one-tenth as large as neurons, so they take about the same amount of space as neurons Kalat, Recent research is questioning the idea that glial cells merely provide a support system for neurons Fields, , ; Koop, Glial cells also appear to influence the formation of synapses and to aid in determining which neuronal connections get stronger or weaker over time, which is essential to learning and to storing memories.
Whereas neuroscientists are excited by all of these possibilities and the prospect of doing research on these cells that have been largely ignored until recently, neurons are still viewed as the most important cells for communication within the human nervous system and thus will be the focus of our discussion.
Neurons all have the same basic parts and structure, and they all operate the same way. The three tion within the nervous system. The dendrites receive information from other neurons and pass it along to the cell body.
The cell body decides whether the information should be passed on to other neurons. If it decides it should, then it does so by means of an electrical impulse that travels down the axon—the longer, thin fiber coming out of the cell body. The pictured neuron has a myelinated axon. Please note that there are periodic gaps where there is no myelin. The impulse jumps from one gap to the next down the axon. When the impulse reaches the axon terminals, it triggers chemical communication with other neurons.
Dendrites are the fibers that project out of the cell body like the branches of a tree. Their main function is to receive information from other neurons. The dendrites pass this information on to the cell body, which contains the nucleus of the cell and the other biological machinery that keeps the cell alive. The cell body also decides whether or not to pass the information from the dendrites on to other neurons.
If the cell body decides to pass along the information, it does so by way of the axon—the long, singular fiber leaving the cell body. The main function of the axon is to conduct inforis to receive information from other mation from the cell body to the axon terminals in order to neurons. How Neurons Communicate The first point to note in learning about how neurons communicate with each other and sometimes with muscles and glands is that the process is partly electrical and partly chemical.
Communication between neurons, however, is chemical. They are separated by a microscopic gap that chemical molecules travel across to carry their message. The electrical impulse. The electrical part of the story begins with the messages received by the dendrites from other neurons. These inputs are either excitatory telling the neuron to generate an electrical impulse or inhibitory telling the neuron not to generate an electrical impulse. The cell body decides whether or not to generate an impulse by continually calculating this input.
If the excitatory input outweighs the inhibitory input by a sufficient amount, then the cell body will generate an impulse.
The impulse travels from the cell body down the axon to the axon terminals. This impulse is an all-or-nothing event, which means that there is either an impulse or there is not; and if there is an impulse, it always travels down the axon at the same speed regardless of the intensity of the stimulus input. So, how are the varying intensities of stimuli for example, a gentle pat on the cheek versus a slap encoded?
The answer is straightforward. The intensity of the stimulus determines how many neurons generate impulses and the number of impulses that are generated each second by the neurons.
Stronger stimuli a slap rather than a pat lead to more neurons generating impulses and generating those impulses more often. The impulses in different neurons travel down the axon at varying rates up to around miles per hour Dowling, This may seem fast, but it is much slower than the speed of electricity or computer processing. Concise Psychology Dictionary Download. Recommend Documents. A Mathematical Introduction to Logic. Psychology A Concise Introduction explores the broad territory of the introductory psychology course while answering the growing need for a shorter, less expensive book.
At less than half the price of a standard textbook, it offers an affordable alternative. A built in Study Guide, written by the author, offers a practical suite of learning aids that foster review and self assessment without I recommend that you check the cost To get a cheap price or good deal. Griggs Jump to. Sections of this page. Psychology A concise introduction. All rights reserved. Please sign in to WorldCat Don't have an account?
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