chapter introduction to psychology...introduction to psychology 11and behaviour can all be described...

Curriculum statement topicIntroduction to psychology

Chapter contentsQuantitative and qualitative research ...... 000

Investigation designs ............................... 000

Comparing qualitative and quantitative

investigations ................................... 000

Reliability and validity in research ........... 000

Sample and population ........................... 000

Objective and subjective data .................. 000

Methods of assessing psychological

responses ......................................... 000

Making sense of research findings ........... 000

Ethical considerations in psychological

research ........................................... 000

Introduction to psychology2

Chapter

10 Psychology for South Australia: Stage 2

Figure 2.1 Qualitative data can be collected as verbal statements through focus groups

in which participants talk about their opinions and experiences.

In chapter 1, you learnt that psychology uses scientific methods to study behaviour, feelings and thoughts. Psychologists can choose from a range of research methods and investigation designs to systematically collect data in order to test a hypothesis and answer questions about behaviour. Research methods are the tools, or techniques, psychologists use to obtain accurate and reliable information about thoughts, feelings and behaviour. These research methods are used as a part of an investigation design, which is the plan used by a researcher. Each investigation design has its specific purposes, advantages and disadvan-tages. The choice of investigation design depends on what is most appropriate for the specific topic of research interest.

QUANTITATIVE AND QUALITATIVE RESEARCHSometimes psychologists refer to research investi-gation designs as being either qualitative or quantitative. For example, they may refer to an investigation design as quantitative observational or qualitative. This is basically a way of categorising dif-ferent research methods or the specific design of the research. Essentially, qualitative research designs involve the collection of qualitative (non-numer-ical) data and quantitative research designs involve the collection of quantitative (numerical) data. All of the investigation designs used in psychology involve the collection of information called data. The data may be considered as the ‘evidence’ which will form the results of the study and be the basis of the conclusions that will be made.

Qualitative dataQualitative data are infor-mation about the ‘quali-ties’ or characteristics of what is being studied. They may be descriptions, words, meanings, pictures, texts, and so on. These data can describe any aspect of a person’s thoughts, feelings or behaviour; more spe-cifically, what something is like, or how something

is experienced. They may be collected as images, or as written or verbal statements made by partici-pants, or as descriptions of behaviour observed and recorded by the researcher. In one study, a psycholo-gist collected and analysed pictures drawn by school-children to obtain information about how they felt when bullied. Sometimes psychologists audio or video record participants to collect data in research. Psychologists studying self-esteem in young children may collect qualitative data by asking children open-ended questions related to their self-esteem. Like-wise, a researcher interested in learning about the factors that enable some people to cope better than others with personal trauma may collect qualitative data through focus groups in which participants talk about how they felt in a specific traumatic situation and how they dealt with their feelings.

Quantitative dataQuantitative data are numerical information about the ‘quantity’ or amount of what is being studied; that is, how much of something there is. They may be heights or weights of prematurely born infants, percentages of participants who respond with ‘Yes’ or ‘No’, or the mean (average) reaction time of participants when a light is flashed onto a screen in an experiment, and so on. Thoughts, feelings

Introduction to psychology 11

and behaviour can all be described in quantitative terms. For example, in a survey, a question might ask participants to use a five-point scale to rate their feelings on issues such as compulsory school uniform or the persuasiveness of a particular adver-tisement. Information about individuals’ scores on a range of psychological tests such as intelligence tests, personality tests and aptitude and interest tests are also provided as quantitative data. In addition, data collected during experiments are typically col-lected in a numerical form and are therefore usually quantitative.

Although qualitative data are typically expressed in the form of words, they can be converted into a quantitative form. For example, participants’ responses to open-ended interview questions about their thoughts and feelings when anxious could be summarised as numbers based on the frequency (‘how often’) with which certain feelings, such as nausea, are reported.

The majority of studies referred to in this text used quantitative data. This reflects the preference for quantitative data in most psychological research. The use of numerical data makes it easier and faster to summarise and interpret the information col-lected. This is why quantitative data are often pre-ferred to qualitative data. However, this does not mean that qualitative data are less important or less useful than quantitative data.

INVESTIGATION DESIGNSData may be collected using one or more investi-gation designs. In the SSABSA Stage 2 Psychology course, there are three key investigation designs: experimental, quantitative observational and quali-tative. The design(s) that a researcher chooses to use will be determined by the question or hypoth-esis they are investigating. For example, a researcher could study the effect of using particular study tech-niques on exam performance by using an experi-mental, quantitative observational or qualitative design.

Figure 2.2 Quantitative data are collected when a person

takes an intelligence or aptitude test.

LEARNING ACTIVITY 2.1Review questions

1. Define the terms qualitative data and quantitative data.

2. What is a key difference between these two types of data? Explain with reference to an example.

3. Indicate whether the data collected in each of the following research studies are qualitative data, quantitative data or both. Explain your answers.(a) A videotaped recording of a focus group

discussion by year 12 students about the effect of paid work on study time and year 12 results.

(b) Records of reaction times for people who have and have not slept a minimum of eight hours.

(c) An account by business executives of how stress affects them, both physically and socially.

(d) Heart rate and blood pressure readings from business executives to assess their level of stress.

(e) Amount of time it takes an elderly person to complete a cognitive ability task.

(f) A mother’s description of changes in her child’s behaviour after the child walked unassisted for the first time.

(g) Pictures drawn by refugee children about their experiences in a detention centre.

(h) Ratings on a five-point attitude rating scale. Participants are asked to rate their feelings about the statement, ‘I support compulsory school uniforms for students’.


Experimental investigation designOne of the most scientifically rigorous and con-trolled investigation designs used in psychology is the experimental design. An experimental design is used to test whether one variable, or factor, influ-ences or causes a change in another variable. For example, whether talking on a hand-held mobile phone while driving (one variable) influences or causes a change in driver reaction time (second variable). Essentially, an experiment enables the researcher to investigate and find out the causes of things. For example, if talking on a hand-held mobile phone while driving actually causes drivers to react more slowly and therefore increases the likelihood of an accident.

Experimental designs can be conducted under strictly controlled conditions in a laboratory setting or outside the laboratory in a field setting. In a field setting, conditions may be less strictly controlled, but a field setting has the advantage of enabling observations of a participant’s behaviour in a ‘real-world’ environment where their behaviour occurs more naturally.

Figure 2.3 A researcher could use an experimental,

quantitative observational or a qualitative design to

investigate how study techniques impact on exam

performance.

Figure 2.3 A researcher could use an experimental,

quantitative observational or a qualitative design to

investigate how study techniques impact on exam

performance.

Characteristics of an experimental designThere are different ways of designing an experiment, and some experiments have more simple or complex designs than others. All experiments, however, have a number of common features; the manipulation of the independent variable by the researcher, random assignment and control groups. We consider the key distinguishing characteristics of the psychological experiment and why the experiment can be used to investigate causes of behaviour.

Independent and dependent variablesA variable influences or causes a change in another variable. In research, a variable is something that can vary (change) in amount or kind over time. If research involved testing whether a particular anger management technique reduced the incidence of road rage in people who had previously been con-victed of road rage, the two variables being tested would be the anger management technique and the incidence of road rage.

Psychologists distinguish between two types of vari-ables called independent variables and dependent variables.

Independent variableIn an experiment, one variable is manipulated or changed by the experimenter to observe whether it affects another variable and what those effects are. The variable that is manipulated or changed is called the independent variable (IV). It is called an independent variable because the experimenter can

Figure 2.4 An example of a dependent variable could be

the amount of road rage behaviour displayed as a result of

using or not using an anger management technique.


independently vary it in some way. The IV is said to be the cause of any changes that may result in the other variable of research interest. For example, in the road rage experiment, the IV would be the anger management technique. The experimenter would have control over which group of participants would learn the anger management technique and which participants would not, in order to test the effect(s) of the technique on the incidence or extent of road rage-related behaviour; that is, the dependent variable.

Dependent variable

The variable that is used to observe and measure the effects of the IV is called the dependent vari-

able (DV). The DV is often the response(s) given by a participant(s) in an experiment and it usually has a numerical value. It is called the dependent variable because whether or not it changes and the way in which it changes ‘depend’ on the influence of the independent variable. In terms of a cause–effect relationship, the DV is the effect(s) caused by manipulation of or exposure to the IV. In the road rage example, the DV is the measured change in the amount of road rage behaviour displayed by partici-pants as a result of using or not using the anger man-agement technique, the IV.

LEARNING ACTIVITY 2.2Identifying independent and dependent variables

1. Identify the independent and dependent vari-ables in the following hypotheses.(a) People will behave differently in a crowd

from the way they behave when alone.(b) Drinking water while driving a car increases

driver alertness.(c) Listening to music while studying for a test

decreases performance on the test.(d) Eating food high in sugar increases hyper-

activity in children.(e) The colour of a room affects students’ ability

to focus on school work.(f) High levels of stress increase incidences of

insomnia.(g) Brainwave activity changes when awake com-

pared to when asleep.(h) Children who eat breakfast perform better

on cognitive tests at school.

LEARNING ACTIVITY 2.2Identifying independent and dependent variables

1. Identify the independent and dependent vari-ables in the following hypotheses.(a) People will behave differently in a crowd

from the way they behave when alone.(b) Drinking water while driving a car increases

driver alertness.(c) Listening to music while studying for a test

decreases performance on the test.(d) Eating food high in sugar increases hyper-

activity in children.(e) The colour of a room affects students’ ability

to focus on school work.(f) High levels of stress increase incidences of

insomnia.(g) Brainwave activity changes when awake com-

pared to when asleep.(h) Children who eat breakfast perform better

on cognitive tests at school.

Experimental and control groupsIn a simple experiment, the participants are often divided into two groups. One group of partici-pants, called the experimental group, is exposed to the experimental condition, where the IV is present. A second group of participants, called the control

group, is exposed to the control condition, where the IV is absent. For example, in an experiment to investigate the effectiveness of a new study tech-nique on exam performance, the IV is the use of the study technique and the DV is performance on the exam. The experimental group will learn, and then use, the study technique and the control group will not use the study technique. The control group provides a standard of comparison against which the experimenter can compare the performance of the experimental group in order to determine whether the independent variable has affected the dependent variable. If the exam performance of the experimental group is significantly better than the exam performance of the control group, the experi-menter may conclude that the IV (use of the study technique) affected the DV (exam performance of participants).

Some experiments do not have both an experi-mental and a control group. Instead, they have one group who are exposed to both the control condition and the experimental condition. For example, to study the influence of drinking caffeinated drinks on people’s concentration while driving, a group of participants could have their driving abilities tested in a driving simulator (control condition) having consumed no caffeinated drinks. The same group could later be tested again in the simulator (experi-mental condition) after having consumed caffeine. The test results of the same participants under the two different conditions would then be compared.

The experimental group and the control group need to be as similar as possible in all personal char-acteristics that can affect the DV, and to be treated the same except for the time when the experimental group is exposed to the independent variable. This is necessary so that if a change occurs in the experi-mental group and does not occur in the control group, the researcher can be more confident in concluding that it is likely the independent variable caused the change.

It is to be expected that people have different abil-ities, personality traits and other characteristics that might affect the outcome of the experiment. One way of minimising differences in the composition of the control and experimental groups is to ran-domly assign or allocate participants to the groups or conditions.


Random assignmentIn random assignment (also called random allo-cation), participants selected for the experiment are just as likely to be in the experimental group as the control group. In other words, every person to be used as a participant in the experiment has an equal chance of being selected in any of the groups to be used. This can be achieved by simply flipping a coin, drawing names out of a hat, or using some other kind of lottery method in which chance alone will determine in which group each participant will be. With a sufficiently large number of participants, it is reasonable to assume that each group will contain about equal numbers of most participant variables that can affect the results. For example, in the experiment investigating the new study technique, if the experimental group is ‘more intelligent’ overall than the control group and the experimental group performs significantly better on the exam, it will be difficult for the researcher to isolate the effect of the study technique on exam performance. Was it a participant’s higher intelligence that enabled better exam performance, rather than use of the study technique, or was it a combination of both?

The purpose of random assignment of partici-pants is to obtain groups that are as alike as possible in terms of participant characteristics before intro-ducing the IV so that the effects of the IV can be estimated. With random assignment of participants to the experimental and control groups, researchers can conclude that if two groups think, feel or behave differently at the end of the experiment, it very prob-ably has something to do with the effect of the inde-pendent variable. Consequently, random assignment is an important means of experimental control.

Random assignment is different from random sam-pling. Random assignment is used to place partici-pants in groups, whereas random sampling is one of the methods that can be used to select participants for an experiment.

Measure effect

on DV

Control group

(IV not present)

Participants

Experimental group

(IV present)

Is there a difference?

Random assignment

Measure effect

on DV

Figure 2.5 Flow chart of an experimental design

Measure effect

on DV

Control group

(IV not present)

Participants

Experimental group

(IV present)

Is there a difference?

Random assignment

Measure effect

on DV

Figure 2.5 Flow chart of an experimental design

LEARNING ACTIVITY 2.3Class activity

This classroom activity enables you to test whether random assignment actually produces groups that are very alike in participant characteristics.

You will need to determine easily observed and measurable characteristics of class members; for example, sex, hair colour, eye colour, short socks versus long socks and left-handed versus right-handed. Each class member should describe them-selves in relation to each characteristic, on a separate card or sheet of paper.

The descriptions for each characteristic are then collected, mixed up and distributed into two ‘groups’, using a random assignment procedure. The means (averages) of the different character-istics for each group should then be calculated and a profile produced for each group in terms of the characteristics so that the ‘equivalence’ of the groups can be compared.1. How close were the means for each characteristic?2. How ‘equivalent’ were the groups?3. Would you expect equivalence to increase as

group size increases? Explain your answer.


1. Summarise the key characteristics of an experi-mental design.

2. Explain the main difference between indepen-dent and dependent variables in an experiment.

3. Choose a research topic of interest that could be investigated using an experiment. Write a hypothesis for this experiment, ensuring the hypothesis refers to the independent and depen-dent variables for the experiment.

4. What is an experimental group? What is a control group?

5. What is the main procedure that distinguishes the experimental and control groups?

6. Why is a control group used in an experiment?7. What is random assignment and why is it often

used in an experiment?


Factors that influence the outcome of an experimentIn an experiment to test whether sleep deprivation causes headaches, the IV is the amount of sleep obtained and the DV is the frequency of head-aches reported. The results of this experiment are described in table 2.1.

Table 2.1 Frequency of headaches reported by

participants and amount of sleep obtained

Hours of sleep

Frequency of headaches reported

Never Sometimes Often

8 or more 40 18 2

7 38 20 6

6 or less 15 35 7

The results suggest that the frequency of head-aches is likely to increase if people experience six or less hours of sleep. However, what would happen if participants who had eight or more hours of sleep also took sleeping pills which reduced the likelihood of headaches occurring, or participants had dif-ferent definitions of what constitutes a headache, or participants who had six or less hours sleep also were experiencing considerable stress in their lives?

There are many variables that might influence the DV in an experiment. Experimenters try to predict what these might be when planning an experiment and develop their research design to control or mini-mise the influence of as many of these unwanted var-iables as possible. However, some of these unwanted variables are difficult to control, and sometimes the experimenter is not able to predict variables that might affect the DV.

Many of the factors that influence the outcome of an experiment, apart from the IV, are called extran-eous variables. The different kinds of extraneous variables are often classified as participant variables, situational variables and experimenter effects.

Extraneous variablesAn extraneous variable is a variable other than the IV that can cause a change in the DV in an experi-ment. When extraneous variables are present in an experiment, they can make it difficult to conclude with confidence that changes which have occurred in the DV have resulted because of the IV and not some other variable.

In the sleep study described above, extraneous variables that may have resulted in headaches devel-oping or not developing could include the amount of stress in the person’s life, illness (for example, a

cold), eye strain, or the use of particular medication. Thus, in the group who had six or less hours of sleep, the greater likelihood of them experiencing a head-ache may not have been a result of insufficient sleep if one or more relevant extraneous variables were present. In order to conclude that the frequency of headaches will increase as a result of reduction in the amount of sleep obtained, all relevant extran-eous variables must be controlled or eliminated.

Sometimes potential extraneous variables can be identified prior to the research, at other times they become apparent as the experiment progresses and, in some instances, the experimenter is totally unaware of their influence. Extraneous variables may include: participant variables (individual dif-ferences in personal characteristics among research participants such as intelligence, motivation, mood and so on); situational variables (such as the impact of the experimental situation on the participant’s responses); and experimenter effects (ways in which the presence or expectations of the experimenter may influence the behaviour of the participants).

Participant variablesThe individual characteristics that participants involved in research bring with them to the experi-ment are called participant (or subject) variables. Participant variables include biological sex, intelli-gence, personality characteristics, motivation, emo-tional state, cultural background and so on. Each of these variables, and many other specific participant variables, can affect the way participants respond in an experiment. Thus experimenters try to take the relevant participant variables into account when they design their experiment. For example, a psychologist might test the notion that ignoring

LEARNING ACTIVITY 2.5Predicting extraneous variables

For each of the following research topics, identify the (a) IV, (b) DV and (c) two potential extraneous variables.1. The effect of anxiety on the ability to make new

friends.2. The effects of sleep deprivation on exam

performance.3. The effects of stress on aggressive behaviour.4. The number of car accidents that occur when

drivers listen to very loud music.


attention-seeking children who are misbehaving will reduce the incidence of attention-seeking behaviour. However, a reduction in the frequency of attention-seeking behaviour after a month of ignoring this type of behaviour may not have occurred only as a result of ignoring the misbehaviour. Factors relating to the children or their respective personal experiences may have impacted on their changed behaviour. For example, if a child’s family situation becomes more or less unsettled, their behaviour may change, irrespective of the psychologist’s behaviour. A child’s health, mood or self-esteem may also have an impact on the child’s behaviour.

Researchers attempt to control the impact of par-ticipant variables by ensuring, as far as possible, that participants in different groups of the experiment are as similar as possible in important personality characteristics and abilities that may influence the results of the experiment. The use of random assign-ment helps researchers to achieve this.

Placebo effectOne of the more subtle and therefore less obvious participant variables is known as the placebo effect. Generally, the placebo effect refers to an improve-ment in health or wellbeing due to an individual’s belief that the treatment given to them will be effec-tive. The placebo effect is seen in medicine when a patient recovers from an illness or pain when they have been given a substance or a treatment with no actual medicinal or therapeutic value. The mere sug-gestion that a treatment will be used or is being used is often enough to make the person feel better. For example, some people begin to feel better if they are put on a waiting list for treatment, as compared to how they might feel if not on the waiting list.

In psychology, the placebo effect occurs when-ever a participant’s response is influenced by their expectation of what to do or how to think or feel, rather than by the specific procedure which is used to produce that response (Zimbardo, 1992). For example, in an experiment to determine the effects of nicotine on driving performance, an experimental group and a control group could be used. Participants in the experimental group could smoke cigarettes just before a driving test and participants in the control group would not. However, the act of lighting up a cigarette and inhaling smoke just before the test, might set off certain expectations in members of the experi-mental group; for example, feeling relaxed, nervous, confident or alert.

These expectations, in turn, might affect driving performance. Therefore, it would be better to have the control group do everything that the experi-mental group does, except use nicotine. So, instead

of the control group simply not smoking, the experi-menter might give them a placebo, or fake treat-ment. In this experiment, a placebo would be to give the participants in the control group fake cigarettes to smoke which do not contain nicotine, but which burn, taste and smell like the real thing. The control group participants will not know their cigarettes are fake and will have no way of distinguishing them from real ones. After the driving test is taken, and, if the results show that control group participants have significantly fewer accidents than the experi-mental group, the researcher can be more confident in concluding that nicotine increases the probability of a car accident (Wade & Tavris, 1990). There are many different types of placebos. Placebos which are used in drug research often take the form of pills or injections.

The placebo effect shows that when human participants take part in an experiment, they are not simply passive participants whose behaviour is controlled solely by the independent variable being tested by the experimenter. The participants usually know they are being observed, and it is pos-sible that this knowledge may affect their behaviour in some way. In some cases, participants actually try to outsmart the experimenter by acting in a way that is opposite to what they think is expected (Carlson, 1987). Most participants, however, are usually cooperative and motivated to do whatever is asked of them. But in their efforts to please, they may act in an experimental situation in ways that they would not act in real life. In other words, experimental participants sometimes do what they think the experimenter wants them to do. When experimental participants are reacting to their per-ception of the experimenter’s wants, they are not responding truly to the independent variable(s) of the experiment (Gerow, 1992).

Because participant expectations can influence the results of an experiment, it is important that participants do not know whether they are in an experimental or control group. To enable this, the experimenter can use a single-blind procedure so that participants are not aware of (are ‘blind’ to) the condition of the experiment to which they have been assigned.

Situational variablesImportant differences in the individual character-istics of research participants are not the only factors that can have an unwanted effect on the dependent variable and therefore the results of an experiment. All participants of different groups of the experi-ment must be tested under the same conditions and in the same situation in order for the experimenter to more confidently conclude that any change in the


DV is the result of the IV. Thus, experimenters need to control situational variables; that is, variables asso-ciated with the experimental situation itself that may affect the outcome of an experiment in an unwanted way. Situational variables include factors such as per-sistent background noise, time of the day, different apparatus or testing conditions, air temperature and so on, depending on what is being tested (that is, studied) in the experiment.

An effective way of avoiding situational variables is to consider them when planning the experiment and ensure they are eliminated, minimised or occur in both the experimental and control con-ditions if they can’t be adequately controlled. For example, if background noise is likely to affect the results of an experiment in an unwanted way, then the effects of it could be removed by conducting the experiment in a soundproof room. This would remove any unwanted effect it may have on the DV. If background noise cannot be entirely eliminated because of the situation in which the experiment must be conducted, then the experimenter would attempt to ensure that background noise occurred at about the same level in both the experimental and control conditions.

There are potentially many extraneous situational variables that can affect experiments and it is diffi-cult for an experimenter to predict and control all of them. Hence, experimenters tend to focus on controlling those situational variables that are likely to have a significant effect on the DV. For example,

in an experiment to determine the softest noise a person can hear, it would be very important to control background noise. However, in an experi-ment to test the effect of caffeine on performance of some physical task, background noise may not be so critical.

An experimenter may also minimise the effects of situational variables by balancing or equalising their effects for all groups of participants involved in the research. For example, if an experimenter testing the effectiveness of a particular reading program on children’s reading skills used two different rooms to test the children, a way of controlling the possible effects of being in the different rooms could be to test half the participants in each group (that is, some using the reading program and some not using the reading program) in each room.

Another procedure for controlling situational var-iables is to test participants in random order, rather than testing all participants in one condition first, then all participants in the other condition. In this way, any variable that may change over time such as the temperature, time of day, or the functioning of the apparatus will affect the conditions approxi-mately equally.

Experimenter effectsAnother extraneous variable that may affect the outcome of an experiment relates to the experi-menters themselves. Personal characteristics of the experimenter and their behaviour during the experiment may unintentionally affect the way in which the research participants respond. These are called experimenter effects. Factors such as an experimenter being tired, in a bad mood or unwell are examples of experimenter effects. These factors

LEARNING ACTIVITY 2.6Identifying situational variables

For each of the following topics, describe one situ-ational variable that may affect the outcome of the experiment in an unwanted way.(a) Whether students who have breakfast can con-

centrate in class more easily.(b) Whether the level of anxiety experienced

affects the ability to perform a complex motor task in gymnastics.

(c) Whether the time since a person has slept affects their ability to concentrate while driving.

LEARNING ACTIVITY 2.6Identifying situational variables

For each of the following topics, describe one situ-ational variable that may affect the outcome of the experiment in an unwanted way.(a) Whether students who have breakfast can con-

centrate in class more easily.(b) Whether the level of anxiety experienced

affects the ability to perform a complex motor task in gymnastics.

(c) Whether the time since a person has slept affects their ability to concentrate while driving.

Figure 2.5a Persistent background noise, such as

roadwork, can affect the results of an experiment if not

properly controlled.


may affect the way in which the experimenter relates to the participants, which, in turn, may result in the participants behaving in a manner different from how they would otherwise behave, thus altering the outcome of the experiment. Furthermore, experimenters may sometimes unknowingly treat participants in the experimental and control groups differently and thus may unintentionally influence the outcome of the experiment.

To prevent experimenter biases from affecting experimental results, researchers often design experiments so that the person collecting the data and the participants are unaware of the condi-tions of the experiment being tested; nor are they aware of the results that are expected from the dif-ferent experimental conditions. Only the person in charge of the research, who has no personal contact with the research participants, is aware of this information.

When this occurs, the experiment uses a double-blind procedure, in which both the person col-lecting the data and the participants are unaware of (are ‘blind’ to) the conditions to which the participants have been assigned. The double-blind procedure has obvious value in experiments where knowledge of the conditions might affect the judgement of the experimenter as well as the participants.

LEARNING ACTIVITY 2.7Class discussion – experimenter effect

Consider the following experiment, which was con-ducted by Rosenthal and Jacobson (1968), and then answer the questions that follow.

At the beginning of the school year, some teachers were told that the children in their class had ‘aca-demic promise’. Other teachers were not given any information about the academic potential of their class.

At the end of the year, children whose teachers were told they had ‘academic promise’ showed sig-nificantly greater gains in IQ scores than children whose teachers were given no information. The children had in fact been randomly allocated to each of the classes.1. Identify the IV and DV in this experiment.2. What experimenter effect may have occurred

in the experiment and influenced the results obtained?

LEARNING ACTIVITY 2.7Class discussion – experimenter effect

Consider the following experiment, which was con-ducted by Rosenthal and Jacobson (1968), and then answer the questions that follow.

At the beginning of the school year, some teachers were told that the children in their class had ‘aca-demic promise’. Other teachers were not given any information about the academic potential of their class.

At the end of the year, children whose teachers were told they had ‘academic promise’ showed sig-nificantly greater gains in IQ scores than children whose teachers were given no information. The children had in fact been randomly allocated to each of the classes.1. Identify the IV and DV in this experiment.2. What experimenter effect may have occurred

in the experiment and influenced the results obtained?

Advantages and disadvantages of the experimental designIn an experiment, the researcher attempts to control the conditions in which a behaviour of interest or other event occurs. As well as controlling the IV, the researcher also attempts to eliminate the influ-ence of unwanted extraneous variables so that any change to the DV can be attributed to the effect of the IV and not some other variable. Elimination of all extraneous variables is not always possible, but in an experiment, control over extraneous variables is usually greater than in other research methods. This is one advantage of an experiment, when compared to other research methods.

A second advantage is that an experiment involves introducing or manipulating the IV in controlled conditions in order to observe the effect on the DV. This makes it possible to determine whether there is a cause and effect relationship between the IV and DV; that is, whether the change to the DV is caused by or dependent on the presence of the IV. A third advantage is that, because of the strict conditions and control, the experimenter can set up the experiment again and repeat it to test or ‘check’ their results and confirm any effect of the IV on the DV. Alternatively, the experimenter can report the conditions of an experiment in such a precise way that other researchers can replicate the experiment and test the results. Replication is very important because when a study is repeated and similar results are obtained, there can be greater confidence in the


1. What is an extraneous variable?2. Why do researchers try to minimise or control

the potential effects of extraneous variables?3. What are participant variables?4. How can participant variables influence the

results of an experiment?5. Describe one way of controlling participant vari-

ables when designing an experiment.6. What are situational variables?7. Describe one way of controlling situational vari-

ables when designing an experiment.8. What is the placebo effect?9. Give an example of how the placebo effect can

influence the results of an experiment.10. What are experimenter effects?


1. What is an extraneous variable?2. Why do researchers try to minimise or control

the potential effects of extraneous variables?3. What are participant variables?4. How can participant variables influence the

results of an experiment?5. Describe one way of controlling participant vari-

ables when designing an experiment.6. What are situational variables?7. Describe one way of controlling situational vari-

ables when designing an experiment.8. What is the placebo effect?9. Give an example of how the placebo effect can

influence the results of an experiment.10. What are experimenter effects?


results obtained. Furthermore, replication enables researchers to test if the results obtained for an experiment can be obtained for other groups and/or in other situations.

Despite its precision and the use of controlled con-ditions, there are several disadvantages to using an experimental design. Although a field experiment occurs in a real-life setting and therefore has a rela-tionship to the real world, the laboratory setting is often artificial and removed from real life. Bringing someone into a laboratory can change their behav-iour to the point where it is too difficult to gener-alise, or ‘apply’, the observed behaviour outside the laboratory, therefore the results don’t necessarily apply to the ‘real world’. Furthermore, some things cannot be measured in a laboratory. The labora-tory would not be the best setting for testing vari-ables such as grief, hate or love. It may be difficult for participants to express these emotions naturally or very realistically in a laboratory setting. In this sense, experiments in a laboratory setting can lack external validity. External validity is whether the results obtained in the laboratory would be valid in the ‘real world’.

A key disadvantage of using an experimental design is that it could be unethical or impossible to randomly assign participants to groups. For example,

if investigating the effect of parents separating on their children’s behaviour at school, it would be unethical and impossible to randomly assign partici-pants to a control and an experimental group and then force parents of children in the experimental group to separate. In situations like this, researchers would be likely to choose a quantitative observa-tional design rather than an experimental one.

Quantitative observational investigation designIf a researcher chooses to collect quantitative data by observing pre-existing criteria and variables, the study would be called a quantitative observational investigation or described as having a quantitative observational design. For example, a pre-existing vari-able could be whether the parents of children are divorced. These designs are some times referred to as being quasi-experimental designs. They have many of the same features as an experimental design but don’t involve the same degree of researcher control over the variables. There are many reasons why a researcher may choose to use a quantitative obser-vational design rather than an experimental design. A quantitative observational design is used when it is not ethical to set up an experiment, when it is too costly, or when it is impossible for other reasons. For example, if researching the effect of exposing young children to violent video games, it would be unethical

LEARNING ACTIVITY 2.9Analyse research

A group of researchers is investigating the hypoth-esis that increasing the amount of time since a par-ticipant has slept will decrease their reaction time. The participants will spend a 24-hour period in a laboratory where the researchers can test their reac-tion time one hour after waking up and then every hour after that, for 24 hours.1. State the hypothesis for this investigation.2. What are the dependent and independent

variables?3. Is the raw data qualitative or quantitative?4. How would the researchers establish a control

condition?5. Describe the advantages and disadvantages of using

an experimental design for this investigation.

LEARNING ACTIVITY 2.9Analyse research

A group of researchers is investigating the hypoth-esis that increasing the amount of time since a par-ticipant has slept will decrease their reaction time. The participants will spend a 24-hour period in a laboratory where the researchers can test their reac-tion time one hour after waking up and then every hour after that, for 24 hours.1. State the hypothesis for this investigation.2. What are the dependent and independent

variables?3. Is the raw data qualitative or quantitative?4. How would the researchers establish a control

condition?5. Describe the advantages and disadvantages of using

an experimental design for this investigation.

Figure 2.6 A disadvantage of experiments in the

laboratory is that the results may lack external validity and

hence not be valid in the ‘real world’.

FIG 2.6 B TO COME


to deliberately expose a group of children to these games. By using a quantitative observational design, a researcher can investigate the topic by having a group of children who never watch violent video games and a group of children who watch violent video games. This research can’t be considered as an experimental design as the children have not been randomly assigned to the groups. This also means that the researcher can’t infer causation, as the pos-sible effects of other variables cannot be minimised as they can in an experimental situation.

Quantitative observational designs have some similarities to experiments in that they still have a dependent and independent variable and can still have a control group. They can also both be con-ducted under either controlled or ‘field’ conditions. However there is no random allocation to groups, as groups already exist. Randomly assigning children to groups and then deliberately exposing one group to violent video games would be unethical.

Correlational investigations are an example of quantitative observational designs. Correlational research allows us to assess the degree to which two variables are related. However, this doesn’t allow a researcher to imply that one variable will cause a particular effect on another.

Advantages and disadvantages of a quantitative observational designQuantitative observational designs allow vari-ables to be investigated that would be unethical, impossible or too costly using an experimental

FIG. 2.7 TO COME

Figure 2.7 It would be unethical to use an experimental

design to determine the effect of violent video games on

young children.

FIG. 2.7 TO COME

Figure 2.7 It would be unethical to use an experimental

design to determine the effect of violent video games on

young children.

design. Some kinds of human behaviour can only be studied using naturalistic observation in a field setting because it would be unethical (inappro-priate) or impractical to study them in a laboratory situation. For example, it would be unethical to severely deprive children in their early life in order to observe the effect of deprivation on behaviour in the future (Zimbardo, 1992). This means that the observed behaviour is likely to be more true to life and hence can provide greater external validity. A disadvantage of a quantitative observa-tional design is that you can’t infer such a strong cause and effect link as you can with an experi-mental design, because there is a greater chance of other variables affecting the results. This is due to the lack of random assignment to groups.


1. Describe the main characteristics of a quantita-tive observational design?

2. What are key differences between an experi-mental and a quantitative observational design?

3. What are the key similarities between an experi-mental and a quantitative observational design?


1. Describe the main characteristics of a quantita-tive observational design?

2. What are key differences between an experi-mental and a quantitative observational design?

3. What are the key similarities between an experi-mental and a quantitative observational design?

LEARNING ACTIVITY 2.11Group discussion

For each of the following two scenarios, outline the strengths and weaknesses of using an experimental and a quantitative observational design to do the investigations.

Scenario 1

A researcher wants to investigate what makes two people attracted to each other.

Scenario 2

A psychologist wants to determine whether students are more likely to participate in class discussions when group members are the same sex, compared to when they are in mixed-sex groups.

LEARNING ACTIVITY 2.11Group discussion

For each of the following two scenarios, outline the strengths and weaknesses of using an experimental and a quantitative observational design to do the investigations.

Scenario 1

A researcher wants to investigate what makes two people attracted to each other.

Scenario 2

A psychologist wants to determine whether students are more likely to participate in class discussions when group members are the same sex, compared to when they are in mixed-sex groups.


Qualitative investigation designQualitative investigations are essentially different to both experimental and quantitative observational designs in that they collect only qualitative data. Two common examples of qualitative investigations are those that use focus groups and those that use the Delphi technique.

Focus groupsSometimes, achieving a deep understanding of a behaviour of interest, as experienced in real life, is more valuable than precise numerical data achieved through quantitative research. A qualitative research method that enables such an in-depth understanding involves the use of a focus group. The term focus group is used to refer to a group interview tech-nique that obtains data through discussion between research participants in a group setting. An impor-tant feature of the focus group method is the use of group interaction. Participants are encouraged to talk to one another, ask questions, exchange per-sonal experiences and points of view and comment on each others’ experiences and opinions (Kitzinger, 2000). This is different from a conventional group interview in which the researcher asks each person to respond to a question in turn. Focus groups can be used to obtain information on all types of behav-iour and experiences. For example, people’s experi-ences of services provided by psychologists, the impact of advertising campaigns, bullying in schools, dating, the experience of being a victim of a violent crime, the experience of having a brain injury, the

attitudes and needs of staff in an organisation, and so on. A focus group may also be used to generate a hypothesis or refine a questionnaire or rating scale for another research study.

A key idea behind the focus group method is that interacting with others in a group situation can help people to explore and clarify their own views in ways that would be far less possible in a one-to-one inter-view or a conventional group interview. To promote group discussion, the researcher (called a ‘facili-tator’) uses free response (open-ended) questions and encourages research participants to discuss issues of importance to them in relation to the research topic, and even to generate questions for discussion with the rest of the group (Kitzinger, 2000).

A focus group study can consist of anything from a few to over 50 groups, depending on the aims of the research and the resources available. Even just a few groups can generate a large amount of data. For this reason, many studies using focus groups rely on a small number of groups. The preferred group size is from four to eight people. Sessions may last for around one or two hours or extend into a whole afternoon or a series of meetings. Sessions are relaxed, in a comfortable setting, with participants usually sitting in a circle to help establish an atmos-phere that encourages open discussion. Some studies combine the focus group method with other data collection techniques; for example, focus group dis-cussion is useful when seeking to explain or explore survey results or to analyse observed behaviour that participants engaged in (Kitzinger, 2000).

Figure 2.8 Group interaction is an important aspect of a focus group.


Advantages and disadvantages of focus groupsThe main advantage of focus groups is the richness of the qualitative data that can be generated about a behaviour of interest. Focus groups are particu-larly suited to gathering information about attitudes and experiences. The groups are generally easy to organise and sessions are relatively inexpensive to conduct. Focus groups are also useful for collecting information from people who have difficulty reading or writing, or have other communication difficulties, although communication difficulties may be a disad-vantage in some studies or situations. However, the ‘safety in numbers’ factor may also be an advantage within a group situation. It can encourage the par-ticipation of those who may normally be uncomfort-able or anxious about revealing information about themselves to an interviewer in a one-to-one inter-view situation. Co-participants in the focus group can also provide support through their expression of feelings that are common to their group, but which they may consider to be very different or abnormal from those of people not in the group. This is par-ticularly important when researching very sensitive or ‘taboo’ experiences such as the death of a loved one or sexual violence. However, this can also pose a disadvantage as the person may not feel comfortable expressing a particular response in front of others. In addition, the presence of other research partici-pants does not enable the confidentiality of more conventional research settings.

Delphi techniqueSometimes it is useful to obtain information on a research question or problem from experts or a group of individuals, each of whom has individual exper-tise relevant to the question or topic of interest. For example, a researcher might be interested in the psy-chological services that may be required for people with a specific disability or mental health problem, the factors that may be effective in preventing young, newly licensed drivers from ‘drag racing’ or doing ‘burnouts’ on public roads, or the type of media advertising campaign that should be devised to help reduce ‘binge drinking’ among teenagers. In the latter case, experts could include psychologists and psychiatrists who specialise in working with teenagers, student welfare teachers, youth workers, police, para-medics, drug and alcohol specialists, media and adver-tising specialists, teenagers and parents of teenagers. ‘Recruiting’ these experts and obtaining their input would provide valuable quantitative and/or qualita-tive data to better understand the issues associated with the problem. Experts may also provide advice or additional information that may not have occurred

to the researcher. For example, whether or not a ‘fear campaign’ is appropriate to get the message through to teenagers, and the types of media commonly used by teenagers, including the most popular magazines, preferred radio stations and usual television viewing times. Consider, however, the potential problems in accessing the experts, let alone organising meetings with them. Experts are often busy people with a heavy load of work commitments. Some of the experts whose advice you wish to seek may also live interstate or even overseas. Imagine the potential difficulties and costs associated with getting them together in the one place at the same time for a discussion or a focus group meeting. Furthermore, if you managed to get them together, consider the likely diversity of opin-ions and the associated difficulties in getting them to reach consensus (agreement) on the best solution to the question or problem of interest.

One research method that uses experts without bringing them together in a face-to-face meeting and helps ensure consensus is reached on the question or problem of research interest is called the Delphi tech-nique (or process). The Delphi technique uses a series of self-administered questionnaires and feedback to obtain the opinion of experts in a field of interest. The technique typically involves the following steps:

Step 1: Recruitment of group members. Relevant indi-viduals are invited to provide opinions on a specific topic, based on their knowledge and experience.

Step 2: Construction and distribution of a question-naire. The opinions obtained in Step 1 are grouped together under a limited number of headings and statements, then distributed to all participants as a self-administered questionnaire usually requiring participants to rank their agreement with each state-ment in the questionnaire. In addition to scoring their agreement with statements, participants may be asked to rate how confident or certain they are about their opinions.

Step 3: First circulation and administration of the ques-tionnaire. Participants complete the questionnaire and return it to the researchers.

Step 4: Collation and categorisation of results. The rank-ings are summarised by the researchers and included in a repeat version of the questionnaire. The redis-tributed questionnaire includes a summary of the responses of all the experts consulted in the first cir-culation. This gives participants the opportunity to revise their responses (if they wish) after considering the responses of other experts, taking into account alternatives or views they may not have considered.

Step 5: Second circulation and administration of the ques-tionnaire. Participants complete the questionnaire again, re-ranking their agreement with each state-ment. Participants have the opportunity to change their responses in the light of the group’s response.


Step 6: Collation of results. The re-rankings are sum-marised and assessed for the degree of consensus. If an acceptable degree of consensus is obtained, the process may end; if not, Steps 4 and 5 are repeated until consensus is reached.

Step 7: Summary of findings. The researchers summa-rise their findings. The final results are provided to all participants.

Advantages and disadvantages of the Delphi techniqueThe Delphi technique has practical advantages of enabling a small or large group of experts to be contacted cheaply, through the post or by e-mail, using a self-administered questionnaire (in hard copy or online), with few geographical limitations in accessing participants. It seeks to maximise the benefits from having informed people consider a problem while minimising the disadvantages associ-ated with group decision making in face-to-face meetings. For example, group decision making in face-to-face meetings can be dominated by one indi-vidual or by ‘subgroups’ with the same interests. The use of successive rounds of questions that include feedback on overall group responses helps ensure participants consider alternative views and options, and consensus can often be achieved on this basis. However, the Delphi technique has also been criti-cised on the basis that it ‘forces consensus’ and the agreed upon opinion(s) may have been weakened by not allowing participants to discuss issues.

The Delphi technique can be applied to problems or issues in your school, college or local community; for example, determining and prioritising a range of different problems or issues, planning a social event, guest speaker, seminar, conference or forum, devel-oping an improved service(s) or evaluating a plan to do or change something.


1. Summarise the differences between a focus group and the Delphi technique.

2. A psychologist wants to investigate the influence of watching television on children’s behaviour. For the above research:(a) What are the advantages and disadvantages

of using a focus group?(b) What are the advantages and disadvantages

of using the Delphi technique?

COMPARING QUALITATIVE AND QUANTITATIVE INVESTIGATIONSBoth qualitative and quantitative research methods have advantages and disadvantages. Although qual-itative research fits less comfortably with traditional scientific ‘rules’ and procedures than does quanti-tative research, it should not be viewed as ‘less scien-tific’ than or ‘not as useful’ as quantitative research. Researchers who undertake qualitative research typically become a part of the research study, known to or interacting with participants as they collect the data. This can provide a richer and deeper under-standing and description of participants and their responses. However, factors that can influence the results of the research, such as the personal biases of the researcher or participants responding in ways they believe they should respond, are more difficult to control.

Quantitative research usually involves data collec-tion under conditions that are strictly controlled to prevent or minimise the influence of such factors. For example, in an experiment, whether it is con-ducted in a laboratory or field (‘real life’) setting, the researcher will use specific procedures to ensure that their involvement and the real purpose of the experiment are unknown to the participants. A typical procedure in a laboratory experiment is to use a research assistant who is unaware of the purpose of the research or the specific experimental ‘treatment’ group in which participants have been placed. In a field experiment, the researcher may observe par-ticipants from a concealed spot to ensure that their presence or knowledge of their presence does not influence participants’ responses in an unwanted way.The decision of whether to use a qualitative or quantitative research method or design depends on what it is that the researcher is trying to find out. More specifically, the decision depends on whether the research question is best answered through qualitative research, quantitative research, or both. Generally, if the research question is about mean-ings and experiences, or if it is about something that is relatively complex, that is best studied ‘as a whole’, then qualitative research that will produce qualita-tive data tends to be used.

For example, a psychologist may be interested in finding out about the experiences of life and the complex set of psychological and sociological interactions in a detention centre for refugees and illegal immigrants. In this case, they may decide to use a qualitative research design. Assuming permis-sion could be obtained, they might observe the daily lives of people living in the detention centre, ideally


living with and living like them. Gathering data in this way would provide a detailed description of life within the detention centre. The emphasis would not be on categorising the behaviour of the children and adults so that it could be described in numerical form.

Alternatively, if the research question has a more restricted focus and is about people’s behaviour, then quantitative research that will generate quan-titative data tends to be used. For example, a psy-chologist may be interested in the specific question of whether an applicant described as a ‘girl’ is more or less likely to be offered a job than an applicant described as a ‘woman’. Because the topic being researched is very specific, the data could be col-lected and reported in a quantitative form; that is, the proportion (‘number’) of participants who judged each candidate (one of whom was described as a girl, the other as a woman) as suitable for par-ticular types of jobs. However, the researcher could also decide to collect qualitative data; for example, to obtain a better understanding of what it feels like for adult women to be referred to as ‘girls’ in dif-ferent settings, or to examine the issue of how this kind of language is used in day-to-day office environ-ments (Banyard & Grayson, 2000).

Figure 2.9 A study of the daily life of a refugee in a

detention centre would probably use a qualitative research

design.

Figure 2.9 A study of the daily life of a refugee in a

detention centre would probably use a qualitative research

design.

RELIABILITY AND VALIDITY IN RESEARCHAn important goal of research is to obtain results that are both reliable and valid. This will mean that the results are consistent and accurate.

Reliability refers to the consistency and stability of the results obtained from a research study. For example, if you measured your height with a ruler and then decided to double-check it, you should expect to get the same result. Similarly, if you con-ducted an experiment on a group of participants and repeated it again with a similar group of participants under the same conditions, you should expect the results to be very similar on each occasion the exper-iment is conducted. Because conducting an experi-ment is a more complicated process than measuring your height, it is not expected that the results will be identical each time the experiment is conducted. However, if the results are to be considered reliable, then they should be very alike each time the experi-ment is repeated.

Of course, a researcher always sets out to obtain reliable results. However, when their study is repeated, it may be found that the results are not reliable. This is more likely to occur if the study is not repeated in exactly the same way in which it was first conducted; for example, if there are differences in important personal characteristics of participants or if the conditions under which the study was first conducted are significantly different in some way.

Validity means that the research study has pro-duced results that accurately measure the behav-iour or event that it claims to have measured. For example, if you measured your height with a cloth tape measure that had been left outside in the open weather for a long time and had become inaccurate through stretching, the result would not be a valid measure of your true height. The inaccurate ruler, however, is reliable as it will give you the same result each time it is used. In other words, a measure can be reliable even though it is not valid, but a measure cannot be valid unless it is reliable.


1. Explain the main difference between qualitative and quantitative research.

2. Briefly describe one advantage and one disadvan-tage of qualitative and quantitative investigations.


Another type of validity relates to the conclu-sions the researcher makes about a study. In this case, the results are valid if the conclusion(s) drawn by the researcher is (are) correct. This means that the conclusion is specifically based on those variables that the study was investigating and the data obtained from the study. For example, if a researcher concludes that a new drug they tested in an experiment reduces headaches, or that participants in a taste-preference study pre-ferred Coca-Cola over Pepsi, the research is valid only if the new drug really works or if the partici-pants really did prefer Coca-Cola (Stangor, 1998). Early in the chapter it was said that researchers who do experiments need to be aware that some-times experiments in a laboratory lack external validity. This means that the results, while accu-rate in the laboratory, may not accurately measure what would happen in the environment outside the laboratory.

As with seeking reliability, researchers always attempt to conduct valid research; that is, they attempt to draw accurate conclusions from their data. Yet often, despite a researcher’s best inten-tions, their research is invalid or not as valid as it may have been. This can occur for a number of dif-ferent reasons. Sometimes a researcher may draw a conclusion from their data that cannot actually be drawn; that is, the data do not actually justify, support or ‘back up’ the conclusion. Another reason that research and its results may be invalid is because one or more extraneous variables have not been adequately controlled and have therefore influenced the results in an important way. For example, in an experiment, an uncontrolled extra-neous variable may affect the results in addition to the IV. When this happens, the researcher may find it difficult to separate the effects of these two vari-ables and cannot be certain whether it was the IV being tested or an unwanted extraneous variable that caused the change in the DV.


1. What can a researcher do to ensure validity and reliability?

2. Describe factors that decrease the validity of results.

3. Describe factors that decrease the reliability of results.

SAMPLE AND POPULATIONPsychologists often study people (or animals) when conducting research. The participants being studied in the research are called the sample. A sample is usually a subsection, or smaller group, of research participants selected from a larger group (population) of research interest. For example, if a researcher is interested in finding out whether children who attended a child-care centre during their preschool years are better at solving conflicts than preschool children who didn’t attend a child-care centre, it would be impractical to test every preschool child who attended a child-care centre and every preschool child who did not. Researchers therefore select a sample with whom they conduct their research. If the sample is selected in a scien-tific way, the results obtained for the sample can then be generalised, or extended to apply, to the larger group of research interest.

To determine television ratings in Adelaide, a sample of households is selected from the total population of households in Adelaide with televi-sions. The programs viewed by the people in the sample are recorded over a set period of time. The results for the sample are then applied to the whole population from which the sample was drawn; that is, Adelaide television viewers. For example, ratings for news programs might indicate that 40 per cent of the sample (and therefore of the population) watched the channel 9 news, 24 per cent watched channel 7 news, 18 per cent watched channel 10 news, 12 per cent watched ABC news and 6 per cent watched SBS news.

In scientific research, the population does not necessarily refer to all people (or animals) in the world, in a country, or even in a particular city or area. The term population refers to the entire group of research interest from which a sample is drawn. A population of interest may be all pre-school children, all P-plate drivers, all blonde-haired females, all SACE Psychology students, all female SACE Psychology students, all Catholic school-educated boys, or all male chimpanzees born in captivity.

A population in a study doesn’t always refer to living things. A population being studied could also be measurable things such as all commu-nity health centres in the South Coast region, all admissions at a hospital, all SACE exam results in English in 2006, all days of school missed by year 9 students, all brands of sneakers, all calls to the 000 telephone number, or any other specific source of data.


Unrepresentative samplesAn unrepresentative sample is a sample which doesn’t represent the population due to its size or that is biased in terms of factors such as gender, age, socio-economic status or cultural groups. The problem for a researcher is that an investigation that uses an unrepresentative sample can have a lack of internal validity, which means that it doesn’t test the hypothesis. Using small or unrepresentative samples means that a sample doesn’t represent the popu-lation, therefore it may be inaccurate to generalise results to the wider population. Very often a small sample doesn’t represent all key interest groups within a population.

Historically, much psychological research used American, white, middle- and upper-class college students. These samples were unrepresentative in terms of ethnic groups, age and socio-economic status. This limitation may have led to distorted results (Weiten, 2004).

OBJECTIVE AND SUBJECTIVE DATAObjective data Objective data follow the notion of ‘seeing is believing’. They are based on measurements of a participant’s response that can be directly observed and verifi ed by the researcher. Essentially, this means that objective data involve responses that are physical, real or can be demonstrated, and therefore can be observed to occur and can be verifi ed, or confi rmed, as having occurred by an independent observer. Data collected through an experiment in which observations and measurements are planned, precise and systematic are considered to be objec-tive data. Participants’ responses in an experiment can be directly observed as being present or not present, and there is little room for interpretation by the researcher. Consequently, objective data are considered to be free from any bias on the part of the researcher.

Objectivity involves taking steps to prevent per-sonal factors from infl uencing any aspect of the research or its report. Objectivity requires that observations are made and recorded free of bias, prejudice or other personal factors which may distort the data obtained (Zimbardo, 1992). For example, an objective description of an event should simply describe the people, their clothing, their postures and positions in relation to one another, their facial expressions, their movements, their voices and words, and the environment. The

LEARNINGACTIVITY 2.15Identifying samples and populations

1. For three of the following research samples, ident ify two different populations from which the samples may have been drawn.(a) 20 year 5 girls and 20 year 5 boys (b) 35 teenagers in part-time work for more

than 6 hours a week(c) 50 females with red hair(d) 25 adults with insomnia

2. For two of the following research topics, ident ify a sample that might be used to conduct the research and a population from which the sample could be drawn.(a) Is insomnia inherited?(b) Does exercise reduce stress?(c) Are children who regularly play violent

computer games more likely to behave aggressively?

(d) What is the effect of caffeine consumption upon memory?

(e) How can people with a fear of spiders be assisted to overcome their fear?

Figure 2.10 The sample in this example is a subset of the

population of people working for an accounting fi rm.

Population

Sample


description might offer suggestions or possible explanations about the motives or emotions of the people but it should be made clear that these are not objective observations, but personal interpre-tations. This is often a difficult task. As is the case for any person, researchers have many expecta-tions and preconceived ideas about how and why people think, feel and behave the way they do. When information about an event is limited, the observer tends to draw inferences, logical judge-ments or conclusions, in order to develop a more complete picture and put together an organised and sensible description. Often these inferences are inaccurate or biased due to faulty memory and prejudices (‘pre-judgements’) (Wallace et al., 1990).

Subjective dataIt is not always appropriate or even possible to collect objective data. Sometimes researchers want information about thoughts and feelings or behav-iours that cannot be directly observed for study purposes; for example, sexual activity or criminal acts. Consequently, they may need to ask partici-pants about how they think, feel or behave. In these cases, researchers rely on self-reports. Self-reports are verbal answers, either written or spoken, to questions asked by the researcher. Questionnaires, surveys, interviews and focus groups are all exam-ples of self-reports. Subjective data are based on self-reports provided by participants. Unlike objec-tive data, the data are determined by the research participants and the researcher cannot directly verify and therefore be certain that the data are accurate. Consequently, when researchers use sub-jective data, they assume that the participants are honest, can accurately recall what they are asked to describe, and are able to give detailed accounts of their experiences. Although subjective data may be more detailed or ‘complex’ than data available from more scientifically rigorous methods under controlled conditions, subjective data are often biased and tend to be difficult to interpret accu-rately when compared with objective data (which are usually quantitative).

The term subjectivity is used to refer to personal factors of the researcher that can influence some aspect of the research or the report on the research. For example, when observers let their own motives, expectations and previous experience interfere with their interpretation of a participant’s responses or the results obtained from their research. In such cases, the observer’s interpretation will be biased and the term observer bias is often used to describe this interpretation.


1. (a) Define the term objective data.(b) Why is it important for data to be objective?

2. (a) Define the term subjective data.(b) What is the main limitation of subjective

data?(c) Why might a researcher choose to collect

subjective data?

METHODS OF ASSESSING PSYCHOLOGICAL RESPONSESObjective quantitative measuresFollowing previous definitions, objective quantitative measures collect numerical data using objective methods. Examples of objective quantitative measures include physiological measures (such as heart rate), behaviour counts and scores on standardised tests. An advantage of these measures is that they are less likely to be affected by participant bias and subjectivity. It is harder for participants to manipulate the data.

Physiological measuresWe are able to make conclusions about some psycho-logical states by using physiological measures. These include heart rate, blood pressure, brainwave activity and skin conductivity, which changes as we perspire and can be measured using a Galvanic Skin Response (GSR) machine. For example, increasing our level of stress or fear is associated with an increase in heart rate and perspiration. If we wanted to assess whether a particular adver-tisement arouses fear or stress, we might measure a person’s heart rate or use a Galvanic Skin Response machine.

Figure 2.11

A Galvanic Skin Response

machine is an objective quantitative measure that

determines the amount of moisture on the skin.


Behaviour countsUsing behaviour counts is an observational method for which a researcher views a group of people or animals to record instances of particular behaviours. A researcher determines criteria for the behaviour(s) they will count, by establishing what will classify as the behaviour(s).

The researcher will then observe the behaviour(s), usually in a way that ensures the participant isn’t aware they are being observed. If someone is aware that they are being watched, it is common for them to act differently from the way they would act if they were unaware. Therefore, so that the researcher can get accurate information, they try to minimise the bias by ‘hiding’ so they can’t be seen. Kindergarten children might be observed from behind a one-way mirror. Alternatively, a camera may be set up to video the participants. A researcher then views this later to count behaviour(s). Alternatively, researchers may act as if they are a participant so that they can blend into a situation and therefore make close observations.

Scores on standardised testsScores on standardised tests are used to measure the individual differences that exist among people in abilities, aptitudes, interests and aspects of per-sonality (Weiten, 2004). Common examples are intelligence tests, which aim to measure intellectual potential, and aptitude tests, which are often used in job selection and assess specific types of mental abilities such as numerical ability and mechanical reasoning.

The tests are classed as standardised as they are administered and scored in the same way each time. Subjects have the same instructions, questions and time limits so that individual differences between people can be compared.

Subjective quantitative measuresThese measures also gather numerical data but this time they are based on subjective measures. This means the researcher cannot be as confident that participants have provided accurate data free from bias. Examples include responses on checklists, questionnaires and rating scales.

Questionnaires Questionnaires are written sets of questions. They are used in research as part of the survey method of gathering data. A survey is used to collect data from a large number of people. In a survey, the researcher gives a common set of questions to a large number of research participants, either face

to face, by mail, by telephone or over the Internet. Unlike the census, which tries to survey the entire population of Australia, a sample survey collects information from a carefully selected group of people whom the researcher believes has charac-teristics that are representative of the entire pop-ulation from which the sample is drawn. A survey provides quantitative data on the topic of research interest.

Rating scalesRating scales are another kind of self-report. They typically use a series of fixed-response questions or statements about different aspects of a research topic (see chapter 3 p. 000).

Qualitative assessment of dataThe Delphi technique and focus groups are methods of collecting qualitative data. While they provide detailed information about thoughts and feelings, it is time consuming to analyse as it needs to be read, so that a researcher can become familiar with it and then identify core themes. These data can also be subjective as the researcher infers meaning as they interpret the data. Content anal-ysis of responses in focus groups is a common, yet time consuming, method of collating and analysing focus group data.

8dciZci�VcVanh^h�d[�gZhedchZh��^c�[dXjh�\gdjeh

1. Organise the data — become familiar with the

data and their organisation.

2. Identify core themes — look at each group of

comments, identify groups of comments that are

similar and hence can be grouped together.

3. Code themes — look at each collection of similar

comments, develop an identifying name that rep-

resents the group.

4. Keep track of themes — keep track of key quotes

that relate to the core themes. You may wish

to consider which theme is discussed with the

greatest frequency. Note the context in which a

comment is made. Is it in response to another’s

comment?

5. Analysis — look for things such as when partici-

pants agree, contradictions, descriptions, different

ways that people say the same thing.

HdjgXZ: Adapted from SSABSA Support Materials,

www.ssabsa.sa.edu.au/support/science/pscy/pscy-

tl-focus.doc, last updated 19 March 2004.

Accessed 8 February 2006.

BOX 2.1

8dciZci�VcVanh^h�d[�gZhedchZh��^c�[dXjh�\gdjeh

1. Organise the data — become familiar with the

data and their organisation.

2. Identify core themes — look at each group of

comments, identify groups of comments that are

similar and hence can be grouped together.

3. Code themes — look at each collection of similar

comments, develop an identifying name that rep-

resents the group.

4. Keep track of themes — keep track of key quotes

that relate to the core themes. You may wish

to consider which theme is discussed with the

greatest frequency. Note the context in which a

comment is made. Is it in response to another’s

comment?

5. Analysis — look for things such as when partici-

pants agree, contradictions, descriptions, different

ways that people say the same thing.

HdjgXZ: Adapted from SSABSA Support Materials,

www.ssabsa.sa.edu.au/support/science/pscy/pscy-

tl-focus.doc, last updated 19 March 2004.

Accessed 8 February 2006.

BOX 2.1


MAKING SENSE OF RESEARCH FINDINGSWhen research has been conducted and results obtained, researchers generally do three things with the results. First, the results are summarised and described so they can be interpreted. The results are then inter-preted so they can be understood. Finally, the results are explained; that is, reasons are suggested about why the particular results were obtained and what they mean. Researchers use statistics to summarise and interpret the results obtained from their research, particularly quantitative research. Statistics are essentially math-ematical procedures. Descriptive statistics are used for summarising and describing the results.

Descriptive statisticsA researcher interested in whether memory declines with age might give some previously unseen infor-mation to twenty 10-year-olds, twenty 25-year-olds, twenty 40-year-olds, twenty 55-year-olds, twenty 70-year-olds and twenty 85-year-olds. The research participants would be required to learn the infor-mation, then complete a memory test so that their memory could be assessed. In all, there would be 120 bits of data (that is, test scores) about the memory of participants in different age groups. How can the researcher make sense of all these different bits of information so that meaningful conclusions about memory and age can be reached?

A researcher would use descriptive statistics to organise, summarise and describe the data so that they can be interpreted. It is difficult to draw con-clusions about whether memory declines with age by looking at 120 individual scores. Thus, in order to compare the memory scores of the six different age groups to determine whether there has been a decline in memory with age, a number that summa-rises the data for each age group could be calculated. The researcher could calculate the mean (average) score on the memory test for each age group. The mean scores could be used to describe the ‘average’ performance on the memory test for each age group and would enable the researcher to compare the dif-ferent age groups.

The mean score is just one type of descriptive sta-tistic and is called a measure of central tendency. Other types of descriptive statistics include calcula-tions of how spread out (how variable) from the mean score the individual scores are for a particular group. Tables and graphs such as frequency distributions and bar graphs can also be prepared to describe the results. Generally, the specific type of descriptive sta-tistic used depends on the kind of research and type of data collected. Some descriptive statistics are more

suited to particular research and data than others. Overall, using descriptive statistics allows results to be generalised to the wider population rather than just to the sample of participants.

Measures of central tendencyData are often summarised by determining a single numerical score that can describe the data for the whole group(s). This score, called a measure of central tendency, describes the ‘central’ or ‘average’ value of a set of scores. When a measure of central tendency is calculated, it often provides a ‘typical’ score for a set of scores.

Suppose you collected data for an empirical investi-gation that involved comparing males and females on a test of recall. The research participants are in five year 8 classes, each of which has 25 students. Data for each of the 125 students — 65 girls and 60 boys — are obtained. To help determine which group performed best, a measure of central tendency could be calcu-lated. This would provide a single score for girls and a single score for boys. Scores could then be compared to estimate which group of participants, boys or girls, performed best on the memory task.

The most commonly used measures of central ten-dency are the mean and median.

MeanThe mean is the arithmetical average of all the indi-vidual scores (or measures) in a set of scores. It is calculated by adding all the scores together and dividing the total by the number of scores. For example, 10 rats were put into a maze. The length of time (in seconds) it took each rat to reach the end of the maze is listed below:

26, 17, 21, 18, 12, 17, 18, 24, 25, 17.

The mean for the group is calculated by adding the scores together (195), then dividing the total by the number of scores (10). The mean is 19.5 seconds.

In this example, the mean provides the most exact measure of central tendency. However, in other sets of data, the mean may not always provide the most accurate measure of central tendency of a set of scores, especially if the scores cluster at the extreme ends of the range of possible scores. For example, if a set of scores consisted of 140, 140, 140, 140, 180, 180, 180, 180, the mean would be 160. Suppose that these data referred to height (in centimetres) of a netball team. A manufacturer of netball skirts would be surprised when the players attended for a fitting of their skirts, having been informed that the mean height is 160 centimetres. Thus, when a mean is pro-vided for a set of data, it doesn’t necessarily follow that any of the individual scores will be the same value as the mean or even approximate it.


Often the mean is calculated to several decimal places. In many instances, this does not create a problem; however, sometimes the mean score may become meaningless in real life. For example, if the mean number of children per family in Australia is 1.75, it is difficult to imagine what 0.75 of a child means.

When scores in a set of data cluster closely around a central score, the mean is a fairly accurate indi-cator of the ‘typical’ score; that is, it is representative of the scores. If, however, the scores are very widely spread, unevenly distributed or cluster around extreme values, then the mean can be misleading and other measures of central tendency should be used instead. Another measure of central tendency that can be considered is the median.

Median

Another way of obtaining a score that may repre-sent the central point in a set of scores is to arrange the scores in order of size (for example, highest to lowest) and select the score that falls in the middle as being typical of the whole set of scores. This middle score is called the median.

The median is the middle score (or midpoint) of a set of scores. More specifically, the median is the point that divides the set of scores into two equal halves when the scores are arranged from highest to lowest (or lowest to highest). For example, the time taken (in seconds) for each rat to complete the maze in rank order (from highest to lowest) is:

26, 24, 21, 18, 18, 17, 17, 17, 15, 13, 12.

In this example the median is 17. When there is an even number of scores, the median is the average of the two middle scores. For example, if the two middle scores are 20 and 21, the median would be 20.5.

LEARNING ACTIVITY 2.17Calculating mean scores

Calculate the mean of the following sets of data.1. IQ scores

120, 115, 97, 82, 93, 100, 111, 112, 132, 111

2. Scores on a sleepiness scale

2, 15, 23, 11, 5, 17, 20, 15, 16, 7

3. Birth weight of babies in kg

2.8, 3.3, 2.5, 3.0, 2.9, 4.4, 3.5, 2.7, 3.2, 4.4.

LEARNING ACTIVITY 2.17Calculating mean scores

Calculate the mean of the following sets of data.1. IQ scores

120, 115, 97, 82, 93, 100, 111, 112, 132, 111


2, 15, 23, 11, 5, 17, 20, 15, 16, 7


2.8, 3.3, 2.5, 3.0, 2.9, 4.4, 3.5, 2.7, 3.2, 4.4.

The median is a particularly useful descriptive sta-tistic if there are limited data, but if there is a large amount of data, determining the median is time consuming and often impractical.

The median is also a useful statistic when many extreme scores occur in the set of scores because the median is not affected by extreme scores. For example, the test results shown in table 2.2 were obtained when a psychology teacher tested her class on research methods in psychology.

Table 2.2 Test results

Rank Per cent

1 98

2 91

3 91

4 60

5 59

6 57

7 57

8 57

9 56

10 54

Total 680

Mean 68

Median 58

The calculation of the mean score on the test does not provide an accurate impression of the average score on the test, because the inclusion of three very high scores inflates the mean figure. In situations such as this, the median is a more accurate reflec-tion of the ‘typical’ score on the test as it is closer to the majority of scores in the set of data.

LEARNING ACTIVITY 2.18 Calculating median scores

Calculate the median of the following sets of data.1. IQ scores

120, 115, 97, 82, 93, 100, 111, 112, 132, 111


2, 15, 23, 11, 5, 17, 20, 15, 16, 7


2.8, 3.3, 2.5, 3.0, 2.9, 4.4, 3.5, 2.7, 3.2, 4.4.

LEARNING ACTIVITY 2.18 Calculating median scores

Calculate the median of the following sets of data.1. IQ scores

120, 115, 97, 82, 93, 100, 111, 112, 132, 111


2, 15, 23, 11, 5, 17, 20, 15, 16, 7


2.8, 3.3, 2.5, 3.0, 2.9, 4.4, 3.5, 2.7, 3.2, 4.4.



1. What information is provided by a measure of central tendency?

2. When would a researcher use a measure of central tendency to describe data in preference to a summary of the data in a table or graph?

3. How is the mean calculated?4. When is the mean most useful as a descriptive

statistic?5. Give an example that indicates when the mean

is not a particularly useful descriptive statistic. Briefly explain your choice of example.

6. What is the median?7. When is the median a particularly useful descrip-

tive statistic?8. When would the median probably not be as

useful as the mean?

Graphical representation of dataThe saying ‘A picture is worth a thousand words’ has also been applied to numbers — a graph is said by some researchers to be ‘worth a thousand numbers’. Pictures that present numerical data are called graphics. The most commonly used picture or graphic is a graph. A graph is a pictorial representa-tion of data.

Graphing or plotting data typically involves the use of two lines (axes) drawn at right angles to one another. The horizontal line is the X axis and the vertical line is the Y axis. The point where the axes intersect is called the origin (0). Generally, the frequency (for example, the number of cases or amount of something) is plotted on the Y axis. The unit of measurement (for example, time, weight) is plotted along the X axis. Graphs show trends in the data collected; for example, how often a response is made, how aspects of behaviour change over time or changes in the experiences of a research participant.

There are various types of graphs that express data in different ways. The kind of graph used depends mainly on the type of data collected. Among the more commonly used kinds of graphs in psychology are the line graph, bar graph, histogram and fre-quency polygon.

Line graphA line graph is a pictorial representation that indicates the relationship between two factors, or

two variables in an experiment; for example, the amount of progesterone or testosterone in the blood and a person’s age, or the amount of sleep a person has had and how tired they report feeling. The horizontal, or X, axis usually has time, age, test scores or the number of trials plotted on it, with the numerical value of the data increasing along the axis from left to right. A line graph that describes the relationship between amount of sleep obtained and level of tiredness reported would list the amount of sleep in hours on the X axis in inter-vals; for example, beginning at zero, then one, two, three, four hours and so on. One important feature of a line graph is that the variable plotted on the X axis is continuous; that is, there is a series of pro-gressively increasing values that can be listed.

The vertical, or Y, axis usually has the frequency of the response, number of errors, percentage of correct responses or some measure of perform-ance plotted along it. A line graph that described the data from the experiment on the amount of sleep obtained and level of tiredness reported would record the level of tiredness reported along the Y axis in intervals; for example, beginning at zero (not tired at all), then one, two, three, four and five, where a rating of five might be identified as exhaustion (figure 2.12).

Various points on a line graph represent the score on one axis that corresponds with a value on the other axis. The intersecting point can represent a corresponding IV/DV score on the two variables by one research participant, or the mean score of a group of participants.

1 2 3 4 5 6 7 8 9 10

6

5

4

3

2

1

0

Amount of sleep obtained

(X axis)

Leve

l of

tire

dness

(Y a

xis

)

Figure 2.12 A line graph that shows the relationship

between the amount of sleep obtained by a group of

research participants (X axis) and the level of tiredness they

reported (Y axis)

1 2 3 4 5 6 7 8 9 10

6

5

4

3

2

1

0

Amount of sleep obtained

(X axis)

Leve

l of

tire

dness

(Y a

xis

)

Figure 2.12 A line graph that shows the relationship

between the amount of sleep obtained by a group of

research participants (X axis) and the level of tiredness they

reported (Y axis)


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Figure 2.13 Line graphs can be used for plotting various

types of data. Note that frequency of responses (as the DV)

is always shown on the Y axis.

Frequency distribution

A frequency distribution is a way of organising data to show how often (frequently) a value or measure (for example, a score) occurs in a set of data. Fre-quency distributions can be represented in a table or a graph. For example, table 2.3 is a frequency distribution. It shows all the possible values of what has been measured (organised into groups or inter-

vals) and the number of times each value occurs in the set of data (the number of individuals in each interval). In a frequency distribution, the scores are

often arranged either from highest to lowest score or lowest to highest score, so that data are presented in an orderly, logical way.

When there is a large number of scores, it is often useful to organise the scores into intervals, then total the number of scores for each interval. The interval can be any size within the range of scores, but the size of each interval should be consistent across all scores. Intervals of five or 10 units are typically used. If an interval of five is used (as in table 2.3), then the difference between one interval and the next is five; that is, 5–9, 10–14, 15–19 and so on.

If a school psychologist was interested in whether giving detention for lateness prevented students from arriving late to school, as a starting point they might gather data on the number of times students at a particular secondary college arrived late to school during semester one. The data could be arranged in a frequency distribution as shown in table 2.3.

Number of times students arrived late:15, 3, 12, 23, 17, 2, 1, 9, 0, 15, 7, 29, 21, 15, 16.

Table 2.3 Frequency distribution of times late to school

during semester one for a sample of year 9 students

Times late Number of individuals

30+ 0

25–29 1

20–24 2

15–19 5

10–14 1

5–9 2

Below 5 4

Bar graphOne kind of frequency distribution is a bar graph. A bar graph shows how frequently a particular category of data occurs by representing the data using a series of discrete (separate) bars or rectan-gles adjacent to, but not touching, one another (as shown in figure 2.14). The horizontal (X) axis shows the types of categories and the vertical (Y) axis indi-cates the frequency with which (how often) each category occurs. Bar graphs are commonly used to express data that can be described as discrete (not continuous) categories such as female or male. One important feature of a bar graph is that each of the categories plotted on the X axis is distinct and that there is no continuation between one category and the next; for example, there would be separate bars for data about female participants’ responses and male participants’ responses. Each bar is the same


width and has a small space between it and the next bar. In addition, the bars can start from either the X axis (that is, vertically) or from the Y axis (that is, horizontally).

For example, researchers interested in investi-gating the type of play four- to five-year-old children engage in might record the type and amount of time spent at each type of children’s play at a kinder-garten over a one-week period. The type of play the children engage in could be categorised according to psychologist Mildred Parten’s system for classi-fying play behaviour. She described four main types of play: solitary play, when the child plays alone and independently; parallel play, when the child plays alone and independently alongside, but not with, other children; associative play, when the child plays with other children in a similar activity, but in their own way; and cooperative play, when the child plays with other children at the same activity. A further category could be unoccupied behaviour when the child does not engage in any play at all. Results of such research could be expressed in a bar graph as shown below.

20

15

10

5

0

Am

ount

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hours

)

asso

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play

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play

unoc

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beha

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solit

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play

para

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play

Types of play

Figure 2.14 A bar graph shows how frequently data occur

in different categories.

20

15

10

5

0

Am

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of

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hours

)

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play

coop

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play

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play

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play

Types of play

Figure 2.14 A bar graph shows how frequently data occur

in different categories.

HistogramHistograms are similar in appearance to bar graphs except the bars touch. Histograms show the fre-quency with which a particular score (or range of scores) occurs in a set of data. Like a bar graph, a histogram has the score(s) plotted on the horizontal (X) axis and the frequency (how often each score occurs) plotted on the vertical (Y) axis. Rectangular bars are used to indicate the frequency of a particular score, as shown in figure 2.15. Histograms differ from bar graphs in two main ways — first, in histograms the bars touch; second, the type of information or variables described on the X axis is continuous and usually numerical, such as age, time or the amount of something. Thus, the X axis of a histogram can be plotted as individual numbers or as intervals.

A histogram could be used to describe data obtained in the following research. A researcher interested in finding out sex differences in how quickly information passes from the eye to the brain to the hand conducted an experiment to test reation time; that is, how quickly male and female

LEARNING ACTIVITY 2.20Representing and interpreting data using a bar graph

A researcher obtained data from a group of students on the relaxation techniques they found most effec-tive for minimising anxiety prior to exams. The data are described in table 2.4. Present these data in a bar graph. Interpret the conclusions that can be drawn from the graph.

Table 2.4 Types of relaxation techniques used

Particpant Relaxation technique used

1 Meditation

2 Drinking coffee

3 Drinking coffee

4 Listening to music

5 Exercise

6 Meditation

7 Sleeping


9 Exercise


11 Exercise

12 Sleeping

13 Meditation

14 Drinking coffee

15 Exercise

16 Exercise

17 Meditation

18 Sleeping

19 Sleeping



participants responded to a red light appearing among written text on a computer screen. Par-ticipants were asked to press the space bar on the keyboard as soon as they saw the red light. The time taken from the appearance of the red light to

pressing the space bar was electronically recorded. Data for two groups of participants can be described on the same histogram using a different colour or pattern to identify the responses of different groups, as shown in figure 2.15.

LEARNING ACTIVITY 2.21Representing data using a histogram

The scores in table 2.5 were obtained by research participants on a test of problem-solving abilities. Graph the data in this frequency distribution as a histogram.

Table 2.5 Frequency distribution of scores on test of problem solving

Score on test of problem-solving Frequency

Score on test of problem-solving Frequency

0 0 11 13

1 2 12 11

2 4 13 15

3 0 14 12

4 3 15 17

5 6 16 11

6 3 17 10

7 5 18 15

8 10 19 4

9 12 20 3

10 12

Figure 2.15 A histogram shows the frequency with which a particular score occurs, where the data shown on

the X axis are continuous.

100

80

60

40

20

0

Fre

quency

(num

ber

of

part

icip

ants

)

0.5 1 1.5 2 2.5 3 3.5 4 4.5

female

male

5

Reaction time (seconds)


Frequency polygonA frequency polygon is another method of graphi-cally representing frequency distributions. A fre-quency polygon is used to graph the frequency of data; however, the presentation of the graph is like a line graph. Using a frequency polygon involves plotting the scores on a task (or groups of scores) on the horizontal (X) axis against the frequency of the scores (or groups of scores) on the vertical (Y) axis of a graph. Dots are plotted at the intersection of the X and Y axes to indicate individual scores and a line is drawn to connect the dots and is brought down to the X axis at either side of the polygon.

In a frequency polygon, if groups of scores are plotted on the X axis, the scores are represented on the graph by the value of the mid-point of the range of scores. For example, if the interval of scores range from 0–4, the mid-point is two. The dot to indicate the score of that range would be placed in line with the score of two. Frequency polygons graph only the frequency of particular responses (or scores). Line graphs can be used to demonstrate a relationship between any two vari-ables being studied.

One advantage of the frequency polygon over the histogram is that more than one set of data can be plotted on the same graph, which makes comparison easier. For example, suppose a researcher collected data on the effects of sleep deprivation on problem-solving ability across three different age groups (15–20 years, 35–40 years, 55–60 years). These data can be presented on one graph, as shown in figure 2.17. To identify the results of the different groups on one graph, researchers could use different kinds of lines for each set of data (such as a solid line, a broken line and a dotted line), or different

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coloured lines (such as red, blue and yellow) or different shapes to identify the point of intersection between the X and Y axes (such as triangles, circles and squares).

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LEARNING ACTIVITY 2.22Tabulating and graphing a frequency distribution

A researcher collected data on maze learning by rats. The rats were required to run through a maze to reach a goal box which contained cheese. The researchers measured the animals’ learning by counting the number of trials each rat took before it could run through the maze from start to finish without making an error (that is, running down a passageway that led to a dead-end). The results for 20 rats were: 6, 12, 5, 7, 10, 12, 6, 5, 7, 10, 12, 15, 14, 20, 18, 18, 20, 14, 15 and 12 trials.1. Describe the data as a frequency distribution

table in intervals.2. Represent the data as a histogram.The first 10 scores were from the offspring of a pair of rats that had inferior maze running abilities. The second 10 scores were offspring from a pair of rats that had superior maze running abilities.3. Show these data as a frequency polygon so that

comparisons can be made between the scores for the two groups.

4. What conclusions could be drawn from the fre-quency polygon?

LEARNING ACTIVITY 2.22Tabulating and graphing a frequency distribution

A researcher collected data on maze learning by rats. The rats were required to run through a maze to reach a goal box which contained cheese. The researchers measured the animals’ learning by counting the number of trials each rat took before it could run through the maze from start to finish without making an error (that is, running down a passageway that led to a dead-end). The results for 20 rats were: 6, 12, 5, 7, 10, 12, 6, 5, 7, 10, 12, 15, 14, 20, 18, 18, 20, 14, 15 and 12 trials.1. Describe the data as a frequency distribution

table in intervals.2. Represent the data as a histogram.The first 10 scores were from the offspring of a pair of rats that had inferior maze running abilities. The second 10 scores were offspring from a pair of rats that had superior maze running abilities.3. Show these data as a frequency polygon so that

comparisons can be made between the scores for the two groups.

4. What conclusions could be drawn from the fre-quency polygon?


Commonly occurring frequency distributionsWhen graphed, a frequency distribution often shows a particular pattern. Certain patterns are given par-ticular names; for example, normal distribution and skewed distribution.

Normal distribution

If any characteristic of a very large group of indi-viduals is measured then plotted as a graph, the data will tend to fall in a bell-shaped pattern called a normal distribution. In a normal distribution, most of the data are located around the centre of the distribution, tapering to a few extremely high or extremely low scores either side of the middle, giving the graph a symmetrical or balanced appearance.


1. For what type of data is a line graph most com-monly used?

2. When would a researcher most likely use a bar graph to express their results?

3. How is a bar graph different from a histogram?4. Describe two ways in which a frequency polygon

differs from a histogram.5. Prepare a frequency distribution table and a

graph to describe results that might be obtained from research on one of the following:(a) types of behaviour for which year 8 students

are most often sent out of class(b) whether females drive slower than males.


1. For what type of data is a line graph most com-monly used?

2. When would a researcher most likely use a bar graph to express their results?

3. How is a bar graph different from a histogram?4. Describe two ways in which a frequency polygon

differs from a histogram.5. Prepare a frequency distribution table and a

graph to describe results that might be obtained from research on one of the following:(a) types of behaviour for which year 8 students

are most often sent out of class(b) whether females drive slower than males.

A normal distribution, such as the one shown in figure 2.18, is a ‘theoretical ideal’ and is rarely perfectly achieved in reality. However, most psycho-logical measurements of human characteristics, as well as measurements of other variables, tend to be normally distributed if a very large group of par-ticipants is studied. If we plotted enough data (for example, scores from different individuals on a par-ticular characteristic such as weight, height, intel-ligence or reaction time), a normal distribution, such as figure 2.18, is likely to be apparent. More specifically, suppose that we measured the number of one-hour driving lessons required for a large, rep-resentative sample of 16-year-old learner drivers to obtain their driver’s licence, we might find that a few would be competent drivers after one or two lessons, while a few learners may need 25 or more lessons. However, most would fall around the midpoint of these extremes at about 11–13 lessons.

Skewed distributions

Sometimes the scores in a frequency distribution are unevenly distributed and cluster to the left or the right of the graph. In such cases, the distribution is called a skewed distribution as there is a lack of balance or symmetry in the distribution. For example, if the number of teeth six-month-old children had were plotted, many of the scores would cluster towards the lower end (left) of the graph producing a posi-tively skewed distribution (figure 2.19). The skew of the graph is linked to the direction of its ‘tail’. On a positively skewed distribution, there are many low scores. The ‘tail’ of the graph tapers in a positive direction towards the higher scores. Alternatively, if the number of teeth 16-year-olds had were plotted, many of the scores would cluster at the higher end (right) of the graph producing a negatively skewed distribution (figure 2.20). On a negatively skewed

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learn

er

dri

vers

)

1 5 9 13 17 21 25

Number of one-hour driving lessons

Figure 2.18 A normal distribution


distribution, there are many high scores. The ‘tail’ of the graph tapers in a negative direction, towards the lower scores.

Fre

qu

en

cy

(nu

mb

er

of

six-m

onth

-old

ch

ild

ren)

Number of teeth

HighLow

Figure 2.19 Positive skew — a curve skewed to the left,

indicating that there is a clustering of a relatively large

number of low ‘scores’

Fre

qu

en

cy

(nu

mb

er

of

six-m

onth

-old

ch

ild

ren)

Number of teeth

HighLow

Figure 2.19 Positive skew — a curve skewed to the left,

indicating that there is a clustering of a relatively large

number of low ‘scores’

Fre

quency

(num

ber

of

16

-year-

old

s)

Number of teethHighLow

Figure 2.20 Negative skew — a curve skewed to the

right, indicating that there is a clustering of a relatively large

number of high ‘scores’

Fre

quency

(num

ber

of

16

-year-

old

s)

Number of teethHighLow

Figure 2.20 Negative skew — a curve skewed to the

right, indicating that there is a clustering of a relatively large

number of high ‘scores’

LEARNING ACTIVITY 2.24Identifying frequency distributions

For each of the following research topics, identify the type of frequency distribution you might expect if data from 5000 individuals were collected.1. The amount of homework completed by year 12

students2. Foot size of 13-year-old boys3. Amount of sleep each day of newborn infants4. The amount of sleep each day of 16-year-olds5. Age at which infants first walk unassisted6. Scores obtained by SACE students on an anxiety

scale on the morning of the English exam7. Grades obtained for the SACE Psychology exam

LEARNING ACTIVITY 2.24Identifying frequency distributions

For each of the following research topics, identify the type of frequency distribution you might expect if data from 5000 individuals were collected.1. The amount of homework completed by year 12

students2. Foot size of 13-year-old boys3. Amount of sleep each day of newborn infants4. The amount of sleep each day of 16-year-olds5. Age at which infants first walk unassisted6. Scores obtained by SACE students on an anxiety

scale on the morning of the English exam7. Grades obtained for the SACE Psychology exam


1. What is a normal distribution?2. What are the distinguishing features of a graph

showing a normal distribution?3. List three characteristics, not referred to in the

text, that would probably show a normal distri-bution if data on a large enough group of partici-pants were collected.

4. What is a skewed distribution?5. In what main way is a positively skewed distri-

bution different from a normal distribution?6. Give an example, not referred to in the text, that

would probably show a positively skewed distri-bution if data on a large enough group of partici-pants were collected.

7. In what main way is a negatively skewed distri-bution different from a normal distribution?

8. Give an example, not referred to in the text, that would probably show a negatively skewed distri-bution if data on a large enough group of partici-pants were collected.

VariabilityIf you collected data on the ages of a sample of year 8 students, there would be very little variability. However, if you collected data on the heights or shoe sizes of the same students, there would be much greater variability. Most research data are made up of measures or values (for example, scores) where there is some variability; that is, where there is a spread of scores and not all scores are the same.

Two year 12 psychology teachers discussed the abil-ities of their respective classes. Teacher A explained that on the mid-year exam, the mean of her students’ results was 78 per cent. Teacher B replied that the mean of his students’ results on the same exam was 68 per cent and that his students must therefore be less capable than his colleague’s. ‘But how do you know I’m not just an easy marker? One of my students got 97 per cent. Then again, another student achieved 18 per cent,’ responded teacher A. Teacher B was sur-prised: ‘The lowest mark in my class was 53 per cent, but my highest mark was only 81 per cent,’ he said, ‘so how do we know which class has the better abilities?’

The discussion between the teachers indicates that a mean, on its own, doesn’t provide the complete picture of the data. The mean and other measures of central tendency describe the ‘central’ value of a frequency distribution. In order to more accurately


2. Amount of television watched each week by eight-year-old children

3. The age at which children start school4. The age at which children speak their first word5. Psychologists’ salaries6. The amount of sleep obtained per night by

year 11 students7. The amount of sleep obtained per night by

4-year-old children8. The resting pulse rates of students in the class9. The reaction times of adult participants to a

visual stimulus presented on a computer screen10. The ability to recall a recently learned list of

15 words in their correct order

Measures of variabilitySuppose a researcher is interested in testing the hypothesis that the presence of background noise (such as music) while students are studying produces lower performance on a test of the material being studied than studying the same information without background noise. Two groups of students partici-pate in the research — one group study with the back-ground noise (experimental group) and the other group study without the background noise (control group). Calculating a mean score for each group of participants will assist the researchers in deciding whether their hypothesis is supported. So why do we need to know about the variability of the scores?

Measuring the variability of the scores provides researchers with information about how reliable any difference between two means is (for example, the difference between the experimental and control group). If the sets of scores are highly variable (widely spread) then any difference between the means of the two groups is less reliable and is more likely to have occurred by chance. However, if each set of scores has a low variability (with scores clustered around the mean), any difference between the means of the two groups is more likely to be due to the effects of the independent variable (rather than chance), in this case, whether there was background noise or not.

There are several different ways of measuring vari-ability, including the range and standard deviation.

Range

The simplest measure of variability in a set of scores is provided by the range. The range is a numerical score that describes the difference between the highest and lowest score in a set of scores. The range is obtained by subtracting the smallest or lowest score from the highest score in a set of scores. It gives a very general measure of the variability of a set of scores. Note how the range is calculated for each

represent the data, a second kind of descriptive statistic is often used — a measure of variability. A measure of variability, or dispersion, indicates how widely the scores are spread or scattered around the central point. The two distributions in figure 2.21 are both normal distributions and have the same mean, but they differ in their variability; that is, how far the scores are spread either side of the mean.

In distribution A, the scores are tightly packed around the mean, indicating low variability. For example, if you graphed data collected on the length of newborn infants, it is likely that the graph would be a normal distribution with very low variability. Most scores would cluster around the mean with very little spread of scores either side of the mean.

In distribution B, the scores are more widely spread from the mean, indicating high variability. For example, if you plotted data on the heights of Year 8 students at a school, it is likely that the graph would be a normal distribution with very high varia-bility. The scores are likely to be spread further from the mean, as some students will be quite short and others quite tall.

LEARNING ACTIVITY 2.26Predicting high and low variability

For each of the following examples, indicate whether you believe there is likely to be high or low variability among the data.1. The amount of alcohol consumed to reach a

blood alcohol content of 0.05 per cent

Fre

quency

Score

Mean

HighLowLow

High

Normal distributionwith high variability

Normal distributionwith low variability

A

B

Figure 2.21 Both graphs are normal distributions with the

same mean. Graph A shows low variability, indicated by the

clustering of scores around the mean. Graph B shows high

variability, indicated by a greater spread of scores from the

mean.

Fre

quency

Score

Mean

HighLowLow

High

Normal distributionwith high variability

Normal distributionwith low variability

A

B

Figure 2.21 Both graphs are normal distributions with the

same mean. Graph A shows low variability, indicated by the

clustering of scores around the mean. Graph B shows high

variability, indicated by a greater spread of scores from the

mean.


set of scores below. These scores were obtained from a study that compared the number of social inter-actions of boys and girls.

Boys: 17, 18, 18, 19, 19, 20, 20, 20, 21

Range: 21–17 � 4

Girls: 17, 18, 18, 19, 19, 20, 20, 20, 27

Range: 27–17 � 10

The range can be a useful measure of variability when all the scores are clustered together and vari-ability is low, as in the example of the number of social interactions for boys. However, because the range is based only on the two extreme scores, it may give a wrong impression of the spread of scores in a set of data. For example, the number of social interactions for girls has a range of 10. This gives the impression of an evenly spread distribution of scores across 10 scores when, in fact, the breadth of the range is due largely to one extreme score. That is, most scores are clustered together and it is only one extreme score that makes the range high. Therefore, although the range adds information about differences in perfor-mance (and hence, scores) for a group of partici-pants, because it is based on only two figures, it is not a very representative measure.

Standard deviation

The standard deviation summarises how far, on average, a score differs (that is, deviates) from the mean in the same units of measurement as the orig-inal data; for example, in IQ points, centimetres and so on.

Standard deviation provides information about the variability, or spread, of a set of scores in relation to the mean. If all the scores in a set of scores were the same, there would be no variability and the standard deviation would be zero because none of the scores would be spread out from the mean. A small standard deviation indicates that there is little variability in the set of scores and that most scores are clustered around the mean. In this case, the mean is a representative descriptive statistic (figure 2.22, curve C). The higher the standard deviation, the greater the variability there is among the scores (figure 2.22, curve A).

The standard deviation is a particularly useful descriptive statistic in that it provides a point of comparison between two different sets of scores. For example, suppose a replacement teacher comes to a new school hoping for an easy day’s work. The replacement teacher is offered either of two classes, both of which have a mean IQ score of 100. There appears to be no difference between the two classes. The teacher is then informed that the standard devi-ation of IQs in one class is 2 whereas the standard deviation in the other is 5. Since a higher standard

deviation means more variability, the class with the standard deviation of 5 may take more effort to teach because students vary more in ability.

Fre

qu

en

cy

of

sco

res

Scores

Mean

HighLowLow

HighC

B

A

Figure 2.22 This graph shows three normal distributions,

each with a different standard deviation. The black curve

(A) has the largest standard deviation and the red curve (C)

has the smallest standard deviation.

LEARNING ACTIVITY 2.27Interpreting data using the mean and standard deviation

1. Two classes sat the same Psychology exam. The following descriptive statistics were calculated from the students’ results in each class:

Class A: mean 65 per cent

Class B: mean 65 per cent.

On the basis of the mean scores alone, what might teachers of these classes conclude about the knowledge of students in each Psychology class? Explain your answer.

2. Suppose the teachers then calculated the stan-dard deviations for their respective classes and obtained the following results:

Class A: mean 65 per cent; standard deviation: 1

Class B: mean 65 per cent; standard deviation: 8.

On the basis of this additional information, what conclusions might the teachers now draw about the knowledge of the students in each Psychology class? Explain your answer.

3. How would information about the standard devi-ation be useful to each teacher in terms of future planning for their classes? What adjustments might each teacher make (if any) as a result of this information about standard deviation?

LEARNING ACTIVITY 2.27Interpreting data using the mean and standard deviation

1. Two classes sat the same Psychology exam. The following descriptive statistics were calculated from the students’ results in each class:

Class A: mean 65 per cent

Class B: mean 65 per cent.

On the basis of the mean scores alone, what might teachers of these classes conclude about the knowledge of students in each Psychology class? Explain your answer.

2. Suppose the teachers then calculated the stan-dard deviations for their respective classes and obtained the following results:

Class A: mean 65 per cent; standard deviation: 1

Class B: mean 65 per cent; standard deviation: 8.

On the basis of this additional information, what conclusions might the teachers now draw about the knowledge of the students in each Psychology class? Explain your answer.

3. How would information about the standard devi-ation be useful to each teacher in terms of future planning for their classes? What adjustments might each teacher make (if any) as a result of this information about standard deviation?


ETHICAL CONSIDERATIONS IN PSYCHOLOGICAL RESEARCHIs it appropriate to put participants through stressful conditions in order to study the effects of stress? Is it appropriate to deceive participants and misinform them of what an experiment is about in order to control the potential influence of participant expec-tations on the results? Should participants in psycho-logical research be fully informed of the purpose of the research before they agree to participate? Ques-tions such as these raise ethical issues.

The term ethics refers to standards that guide individuals to identify good, desirable or accept-able conduct (NHMRC, 2001). In addition to the general ethical principles held by our society, most professions have their own standards of conduct and ethical principles that must be followed. For example, it would be considered unethical for a medical doctor to discuss a patient’s condition with anyone apart from the patient or those responsible for the patient. Likewise, it would be unethical for a psychologist to reveal information discussed in a counselling session or the results of a psychological test to anyone apart from the client, or the guardians of the client if the client is a child under a guardian’s care, without the client’s consent.

Ethical considerations also apply to all research situations. For example, the way participants in experiments are to be treated is controlled by ethical guidelines. These guidelines help ensure that the psychological and physical wellbeing of research participants is protected during their


1. What is a measure of variability?2. Why do researchers use measures of variability

when summarising their data?3. How is the range calculated?4. What effect do extreme scores have on the

range?5. What information does the standard deviation

provide about the distribution of scores?6. One set of data (A) produced a standard devi-

ation of 1 and another set of data (B) produced a standard deviation of 2. What conclusions could be made about (A) and (B)?

7. What does a standard deviation of zero mean?8. Construct a graph that shows a set of scores with

a low standard deviation in one colour and a set of scores with a high standard deviation in a dif-ferent colour.

9. The manager of a company has two assistants, Ms Smith and Ms Taylor. Her business is experi-encing financial difficulties and she must dismiss one of her assistants. As a way of determining which employee she should keep, she asks both assistants to complete a series of keyboard speed tests. She counts the number of errors made by each person. She finds that Ms Smith has a mean number of errors of 10, standard deviation of 2, and Ms Taylor has a mean number of 8 errors with a standard deviation of 4. Which assistant should Ms Jones keep? Give reasons for your answer.

<gVe]^c\�hiVcYVgY�YZk^Vi^dcStandard deviation can also be shown on

a graph that illustrates the variability of a

distribution from the mean (figure 2.23).

When standard deviations are represented

on the X axis of an ‘ideal’ normal distribu-

tion curve, the percentage of scores falling

between the mean and any given point on the

horizontal axis is always the same. For

example, 68.26 per cent of the scores will

fall within one standard deviation either

side of the mean; 95.44 per cent of the

scores will fall within two standard devia-

tions either side of the mean. These per-

centages apply consistently for normal

distribution, irrespective of the size of the

standard deviation.N

um

ber

of

score

s or

ind

ivid

uals

Mean

Standard deviations from the mean

13.59% 34.13% 34.13% 13.59%

2.14%

0.13%

2.14%

0.13%

–3 –2 –1 +1 +2 +3

68.26%95.44%99.74%

Figure 2.23 When standard deviations are represented on the X axis

of an ‘ideal’ normal distribution, certain fixed percentages of scores fall

between the mean at any given point. Most scores fall in the middle

range within one standard deviation either side of the mean.

BOX 2.2


involvement in psychological research and fol-lowing the research.

The Australian Psychological Society (APS) has a code of ethics that psychologists must follow in all aspects of their professional work. The ethical guide-lines for psychological research cover all aspects of the research, particularly the responsibilities of the researcher, the rights of research participants and the use of animals in research. In universities, where most psychological research is planned and con-ducted, there are ethics committees or review panels which examine research proposals to ensure ethical guidelines will be followed.

Responsibilities of the researcherWhen planning research, the researcher must take into consideration the ethical issues involved. The researcher is responsible for ensuring that the research is conducted in such a manner that the well-being of research participants is the main concern and that participants are not placed at risk of injury or harm in any way. Under no circumstances is the researcher allowed to conduct research which causes participants severe distress. The researcher must be aware that in all scientific research with human par-ticipants, there is a need to balance the benefits to society from the findings of the investigation against any discomfort or risks to the research participants. As well as ensuring that no psychological or physical harm is caused to participants, a researcher must also respect participants’ rights as individuals. Psy-chologists must follow key ethical principles.

ConfidentialityParticipants have a right to privacy, so any details of their involvement in a study (for example, test results or personal data) cannot be disclosed unless their written consent is obtained. The confidenti-ality requirement applies to the access of research data by others, and to the storage and disposal of research data. Wherever possible, the procedures for establishing confidentiality must be explained to participants at the beginning of the research.

Voluntary participationThe researcher must try to ensure that participants consent to be involved in the research voluntarily. Participants must not be pressured to take part in a study and researchers should avoid the use of coer-cion to gain participants’ involvement in research. The researcher must also ensure that prospective participants do not experience negative conse-quences if they do not agree to be involved in the research. It is important for researchers to avoid inadvertently making participants believe that they

have to participate, even though the researcher says that they don’t have to. A child might feel that they have to participate because the researcher who is asking is an adult. To try to please the adult, they do what they believe the adult wants. Researchers also need to be aware that participants who feel they lack power may feel obligated to participate. This could include groups of people such as employees, chil-dren and refugees.

Right to withdrawThe researcher must inform participants of what the research is about and that they are free to par-ticipate, or to decline to participate, or to withdraw from a study at any time during the study should they choose to do so. Furthermore, the researcher must ensure that there are no negative consequences for withdrawing from the study. This includes ensuring that the researcher doesn’t ask a participant why they choose to withdraw.

In addition, as researchers must always minimise any potential harm to participants, they may find it necessary to withdraw a participant themselves. For example, if a researcher can see that the research is making a participant very anxious, then they may need to remove the person from the study.

Informed consent proceduresWherever possible, participants must be appropri-ately informed of the type of study and the reason(s) for the research prior to agreeing to participate. Such informed consent must be appropriately docu-mented; for example, completion of a consent form.

When the research necessarily involves partici-pants in activities that produce physical or mental stress, the researcher must inform participants about the nature of the research procedures to be used and the physical and psychological effects that should be expected. If unexpected, potentially harmful stress occurs, the researcher must immedi-ately end the participants’ involvement in the study and ensure the participants’ reactions are treated. If research procedures involve stressful conditions, the experimenter must ensure that no psychologically vulnerable person participates.

For participants who are legally incapable of giving informed consent (for example, children and intellectually disabled people), the researcher must provide an appropriate explanation, obtain the participants’ consent and/or obtain appropriate consent from the persons who are legally respon-sible for participants’ wellbeing. Researchers need to be aware of people who may be vulnerable (groups such as children or the mentally ill) and not to abuse their position of power when gaining consent.


Accurate reportingA researcher must report all data accurately. Researchers have their hypotheses and must take care to report data accurately, rather than merely presenting their hypothesis as being correct.

DeceptionSometimes, giving participants information about a study may influence their behaviour during the research and affect the accuracy of the results. In these instances, when it is necessary for scientific reasons to conduct a study without fully informing participants of the true purpose of the study prior to its commencement, the researcher must ensure that participants do not suffer distress from the research procedures. In all cases involving decep-tion, participants must be debriefed at the conclu-sion of the study. For example, when investigating people’s attitudes, it may be necessary to use deception. This is because, if people know what the research is really about, they may present a par-ticular attitude just to be viewed positively by the researcher.

DebriefingDuring debriefing, participants are informed of the purpose of the research at the conclusion of the study. Also, the researcher must correct any mis-taken attitudes or beliefs that participants may have about the research.

The researcher must anticipate the possible effects to participants of being involved in the research and provide information about services available to them to treat any unnecessary distress that occurs as a result of their participation.

Once the entire study has been completed, the researcher must provide an opportunity for par-ticipants to obtain appropriate information about the study, including its procedures, results and conclusions.

Professional conductAt all times throughout the research, researchers are expected to conduct themselves in a professional manner. They must not behave in a manner that brings disrepute to the psychology profession, or to scientific research. For example, they must not use a position of authority to put pressure on people to agree to participate in research. In addition, if psy-chologists are involved in conducting research with colleagues who are not psychologists, such as bio-logists or medical doctors, they have a responsibility to ensure their research colleagues agree to follow the ethical code of conduct and guidelines for psy-chologists prior to conducting the research.

Use of animals in researchAlthough psychology is primarily interested in human behaviour, about five to ten per cent of research involves non-human participants. Within this group, most are mice, rats, hamsters and pigeons. About five per cent of the animals used are monkeys and other primates.

Ethical guidelines have been established for psy-chologists (and other researchers) to ensure that all reasonable steps are taken to minimise the discom-fort, illness and pain to animals used in research. The use and care of laboratory animals must be directly supervised by a person competent to ensure their comfort, health and humane treatment. The care and use of animals in research must follow the National Health and Medical Research Council Guidelines for Use of Animal Subjects.

If an animal is to be subjected to pain, stress or deprivation, research may occur only if no other alternative is available and the research can be justi-fied. If surgery is to occur, the animals must be given the appropriate anaesthesia so they do not experi-ence pain. When an animal’s life is to be terminated, it must be done quickly and painlessly. Additional information on the use of animals in research is in box 2.3.

Many arguments have been presented against the use of animals in psychological research. One argument is that it is not possible to apply (generalise) the results of animal studies to humans because the species are not the same even when there are apparent similarities. An issue for researchers is how far they can generalise about human behaviour from the results of animal studies. If laboratory animals die after prolonged


1. What are ethics?2. Why are ethical considerations and guidelines

necessary for psychological research?3. What is the ethical responsibility of a researcher

who conducts research with human participants, but does not fully inform them of the true purpose of the research before the study begins because it may influence the participants’ behaviour?

4. According to the Australian Psychological Society code of ethics, if a research participant became dis-tressed during the research, what should occur?

5. What is meant by the statement ‘participants must be debriefed at the conclusion of the study’?


sleep loss, would humans? If a drug causes a brain disorder in animals, should it be banned for human use? Another argument is that humans should respect

animals and protect them from harm rather than use them in research. It is also suggested that humans do

not have the right to dominate other species.

BOX 2.3

Jh^c\�Vc^bVah�^c�gZhZVgX]There are many issues, particularly in medicine and

psychology, important for the wellbeing of the human

species that need to be researched. However, much of

this research is too dangerous for human participants

to be involved in. Hence, for many years, animals, par-

ticularly rats and rhesus monkeys, have been used in

psychological research.

The main reasons animals have been used in

research are:

1. some psychologists are genuinely interested in

studying animal behaviour. This field of study is

commonly referred to as ethology

2. some studies cannot be conducted with humans

due to the risk of psychological and/or physical

harm that may be caused, or because suitable

human participants are unavailable

3. bodily systems and/or behaviours of some animals

are similar to those of humans; therefore, using

animals can be a ‘starting point’ for learning more

about human behaviour

4. animals have practical advantages. For example,

studying the effects of ageing from birth through

to ‘old age’ is not generally practical in humans

because most people live until 75-plus years

compared with rats which have a life expectancy

of two years. Another advantage is that some

animal species breed a lot faster than humans. For

example, rats produce a new generation every three

months and can be used to study the development

of certain behaviours over successive generations

within a relatively short period of time. Animals

can also be kept for long periods of time in cap-

tivity in laboratories and it is easier to observe their

behaviour under these conditions

5. the behaviour of animals can usually be controlled to

an extent not possible with human participants. For

example, a rat can be raised from birth in a cage. The

rat can then be used in a learning experiment and

the psychologist will have a good idea of what it has

already learned before the experiment is conducted

6. when certain experiments require large numbers

of participants who have, for example, the same

genetic background, animal subjects are more

easily obtained than humans

7. animals don’t usually have expectations and they

are not able to guess the purpose of an experiment.

‘Participant expectations’ in humans can influence

the results of an experiment.

LEARNING ACTIVITY 2.30Applying ethical guidelines to research

1. Class discussion Discuss the ethical issues relevant to the research

described in box 2.4.2. Suppose you have been asked to sit on an ethics

committee. The task of the committee is to approve or reject proposals for psychological research.

The following proposal has been presented to your committee for approval. Your task is to evaluate the proposal in terms of whether they meet the guide-lines, then write your recommendations making mention of:(a) whether the committee would approve or reject

the proposal as it is presented(b) if the proposal is rejected, on the basis of which

ethical guidelines it is rejected(c) how the research proposal could be changed so

the research could go ahead, but meet ethical guidelines it has (or may have) breached.

Proposal

Professor Davidson is interested in the effect of bul-lying on teenager’s stress levels. She believes that bul-lying leads teenagers to become stressed and anxious. She wants to divide her participants into two groups. Participants in one group would be verbally bullied. Participants in the other group would not be bullied. The bullies would be helpers of the researcher. Those being bullied wouldn’t know that the researcher had deliberately put the bullies into the situation. The researcher is concerned that telling the participants that the investigation is about bullying might make participants react differently than they would nor-mally and so wants to use deception and tell the par-ticipants that the investigation is about how people cooperate together. The researcher will debrief all participants at the end of the research and tell them the truth about the research.


BOX 2.4

Jh^c\�b^XZ�^c�gZhZVgX]Erasing memory helps depressed mice

By Deborah Zabarenko

Depressed loner mice get more sociable when re-

scarchers delete a memory molecule from their brains,

scientists say.

This molecular therapy worked about as well as

giving mice antidepressants, the researchers report

today in the journal HX^ZcXZ.

The finding might help treat human ills like social

phobia and post-traumatic stress, the scientists say.

They targeted a molecule in a section of the

brain known to be related to sensations of pleasure

and danger, says Dr Olivier Berton of the Univer-

sity of Texas Southwestern Medical Center, one of

the report’s authors.

‘We focused on this molecule in a region of the

brain that people call the reward pathway, which

people have studied a lot in relation to drugs that are

abused,’ he says.

Deleting the molecule from this part of the brain

meant that the mice were never depressed and fearful,

Berton says, even though conditions were set up that

normally would make them run and hide.

‘If we can identify such mechanisms in the brain,

that’s a way to develop antidepressants that work

faster and in more people,’ Berton says.

Depressed mice become withdrawn

To carry out the experiment, Berton and his colleagues

had to find a way to reliably make mice depressed.

They did this by putting ordinarily sociable mice in

cages with aggressive, bullying mice.

The sociable mice regularly fought with the bullies,

and over a period of days became withdrawn and

fearful of strange mice. Even when the bullies were

removed, the depression stayed.

They perked up when dosed with antidepressants

for a month, Berton says.

Deleting the molecule involved anesthetising the

mice, then injecting this very specific part of their

brains with a virus that disables the molecule.

This kind of technique has been used experimentally

in research into Parkinson’s disease. Berton says.

An alternate to antidepressants?

The result in mice was to block the typically depressed

response to bullying, mimicking the response to

chronic antidepressant therapy.

The next step is to record the electrical activity of

brain cells in the reward pathway, Berton says.

‘We’re trying to understand this response to stress

from the molecular to the cellular to the neural circuit

level of understanding,’ he says.

HdjgXZ: Reuters, www.abc.net.au,

10 February 2006

Figure 2.24

Psychologists

must ensure that

research animals

are well cared for,

humanely treated

and experience

minimal pain and

suffering.

8]VeiZg�G

Zk^Zl


Short answer questions

1. Briefly define each of the following research investigation designs, ensuring you refer to a key distinguishing feature of each design.a. experimental designb. quantitative observational designc. qualitative design

2. Choose one of the following topics and explain how each of the research designs defined in question 2 could be used to investigate the topic of interest.a. Gender role differences in the play of pre-

school childrenb. The effect of mood on the quality of study

for an examc. The effect of detention on subsequent

behaviourd. The effect of the presence of other people

on behaviour

3. In an experiment, what is the relationship between the independent variable and the dependent variable?

4. Describe three examples of:a. qualitative datab. quantitative data.

5. A psychologist wants to investigate teenagers’ attitudes to eating healthy food. Discuss the advantages and disadvantages of collectinga. qualitative datab. quantitative data

as part of the investigation.

6. Describe two key differences between using focus groups and using the Delphi technique to collect qualitative data.

7. Explain the meaning of the statement ‘qualitative data are subjective’.

8. Explain the factors that affect the validity and reliability of data.

9. A researcher is investigating the differences in aggression levels of males and females. The sample of participants for the research was recruited from first-year university students. There are 20 males and 15 females. In what ways could this sample be considered unrepresentative?

10. The exam results for two different classes for the same exam are presented as percentages below: Group 1: 65, 77, 84, 32, 95, 34, 66, 78 Group 2: 72, 88, 60, 32, 92, 56, 70, 75a. Calculate the mean exam score for each

group.b. Calculate the median for each group of

scores.c. Generate a graphical representation of the

data to compare mean scores.d. Interpret patterns in the data.

Extended response questions

1. A researcher wants to investigate the hypothesis that ‘high levels of stress negatively affect people’s health’. Outline a plan for this research by evaluating the choice(s) of investigation design and any relevant ethical considerations.

2. Choose a piece of research that has been completed in the area of psychology. Evaluate the ethical considerations that are relevant to the research.

chapter introduction to psychology...introduction to psychology 11and behaviour can all be described...

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