SAMPLING TECHNIQUES

Sampling is the process or technique of selecting a suitable sample for the purpose of determining parameters or characteristics of the whole population. To carry out a study, one might bear in mind what size the sample should be, and whether the size is statistically justified and lastly, what method of sampling is to be used.

The concept of sampling in qualitative research

In qualitative research the issue of sampling has little significance as the main aim of most qualitative inquiries is either to explore or describe the diversity in a situation, phenomenon or issue. Qualitative research does not make an attempt to either quantify or determine the extent of this diversity.

The class, families living the city or electorates from which you select a few students, families, electors to question in order to find answers to your research questions are called the population or study population, and are usually denoted by the letter (N).The small group of students, families or electors from whom you collect the required information to estimate the average age of the class, average income or the election outcome is called the sample. The number of students, families or electors from whom you obtain the required information is called the sample size and is usually denoted by the letter (n).

The way you select students, families or electors is called the sampling design or strategy. Each student, family or elector that becomes the basis for selecting your sample is called the sampling unit or sampling element. A list identifying each student, family or elector in the study population is called the sampling frame.

Your findings based on the information obtained from your respondents (sample) are called sample statistics. From sample statistics we make so estimate of the answers to our research questions in the study population. The estimates arrived at from sample statistics are called population parameters or the population mean.

Principles of sampling

• Principle one- In a majority of cases of sampling there will be a difference between the sample statistics and the True population mean, which is attributable to the selection of random elements in the sample.

• Principle two- the greater the sample size, the more accurate will be the estimate of the true population mean.

• Principle three -The greater the deference in the variable under study in a population for a given sample size, the greater will be the difference between the sample statistics and the true population mean.

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Factors affecting the inferences drawn from a sample

• The size of the sample -As a rule, the larger the sample size, the more accurate will be the findings

• The extent of variation in the sampling population-As a rule, the higher the variation with respect to the characteristics under study in the study population, the greater will be the uncertainty for a given sample size.

Types of sampling technique

How ‘representative’ is one’s sample may be a common question. Researchers always try to draw a representative sample to draw any conclusion about the ‘real world’. This is a part of the researcher’s responsibility. There are two basic sampling techniques: probability and non-probability sampling.

A probability sample is defined as a sample in which every element of the population has an equal chance of being selected. If sample units are selected on the basis of personal judgment, the sample method is a non-probability sample. In detail sampling technique can be classified as:

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A sampling frame is the list of elements from which the sample may be drawn. Sampling frame might be a list of all members of an institute or workers in a company. The sampling unit is a single element or group of elements subject to selection in the sample. Flights can be selected as sampling units. The term primary sampling units (PSUs) designates units selected in the first stage of sampling. If successive stages of sampling are conducted, sampling units are called secondary sampling units or tertiary sampling units (if three stages are necessary).

Representative sampling plans

Simple Random Sample is defined as Selections are made from a specified and defined population (i.e., the frame is known). Each unit is selected with known and non-zero probability, so that every unit in the population has an equal (known) chance of selection. The method of selection is specified, objective and replicable.

Stratified Random Sampling

The population is observed to be heterogeneous in nature. In order to apply simple random techniques to such a heterogeneous population, we have to group them as homogeneously as possible, where each group is termed a ‘stratum’ (in plural ‘strata’). Then samples are drawn equally or proportionately from each stratum and, therefore, the procedure is called stratified random sampling.

The procedure for selecting a stratified Sample

Step 1 Identify all elements or sampling units in the sampling population.

Step 2 Decide upon the different strata (k) into which you want to stratify the population.

Step 3 Place each element into the appropriate stratum.

Step 4 Number every element in each stratum separately.

Step 5 Decide the total sample size (n)

Step 6 Decide whether you want to select proportionate or disproportionate stratified sampling and follow the steps below:

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Cluster (Multistage) Sampling

In cluster sampling we have to have a number of clusters which are characterized by heterogeneity in between and homogeneity within. Cluster sampling is used for a variety of purposes particularly for large sample surveys or a nation-wide survey. If we consider two stages to conduct the survey, then it is called two-stage cluster sampling. If someone considers more than two stages to collect the data, then it is called multistage sampling.

Cluster sampling is the successive random sampling of units and submits of the population. Stratified sampling involves dividing the population into groups called strata and then sampling subjects from within the strata. Cluster sampling on its own part involves dividing the population into large numbers of groups called clusters and then successively sampling such clusters from very large to the smallest of clusters before finally sampling subjects.

The procedure is as follows:

First, define the population

Second, identify all possible clusters in the population from the largest to the smallest

Third, successively sample clusters from the very large groups to the large groups to subgroups to sub-sub groups etc. until you get to the stage of individual subjects

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Randomly select the subjects. This is a very useful method when dealing with a large population or when a list at the macro levels of sampling will be difficult, if not impossible, to compile.

Sequential (Multiphase) Sampling

This is a sampling scheme where the researcher is allowed to draw sample on more than one occasions. It may be economically more convenient to collect information by a sample and then use this information as a basis for selecting a sub-sample for further study. This procedure is called double sampling, multiphase sampling or sequential sampling. This is a technique frequently used to draw samples in industries for ensuring the quality of their products.

Systematic (Quasi-random) Sampling

In systematic random sampling, we range the population from which selections are to be made in a list or series, choose a random staring point and then count through the list selecting every N th

unit. Systematic sampling has been classified under the `mixed' sampling category because it has the characteristics of both random and non-random sampling designs.

This is often confused with the simple random method. It is, however, more systematic – as the name suggests. The procedure is as follows:

First, secure a list of the entire population in which every subject is listed only once

Second, number every subject in the list

Third, determine the size of the sample you want to draw from the population.

Fourth calculate the sampling interval, which is the result of dividing the population size by the proposed sample size.

Fifth, randomly (using a mechanical device) draw from the sampling frame (i.e. the population list) the first member of your sample. This first member must be drawn from the section of the population not above (but could be equal to) the number that corresponds to the sampling interval.

Sixth, beginning with the number signifying the first selected case as indicated above, go down the population list, systematically adding the sampling interval to selected cases until the required number of cases to fill the sample size has been attained.

The advantage of systematic random sampling is that it is easier to use that simple random

sampling in situations where the sampling frame (i.e. the list of the entire population) is very

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long (for instance, a telephone directory). The major weakness of the method, which it shares

with simple random sampling, is that it requires a list of the population

Non-probability Sampling Methods

In non-probability sampling, the probability of selecting population elements is unknown.

Quota sampling

It is a technique which can be used when the aim is to sample from a heterogeneous population for which no exhaustive sampling frame exists. The basic idea is that a representative sample can be obtained without recourse to an exhaustive sampling frame if the population can be subdivided on one or more variables, and if the subdivisions constitute known proportions of the population. Thus, to take a sample example, if a population is known to be made up of 55% females and 45% males, then the proportion of male and female in the final sample are also required to be 55% and 45% respectively. Note, though, that like the stratified sample, the quota sample is representative of the population only in relation to the variable on which the population is initially divided.

Where quota sampling differs from stratified sampling is in the nature of the sampling process itself. Whereas stratified sampling is probabilistic and requires an exhaustive sampling frame and objective sampling method of selecting individuals for inclusion in the sample, quota sampling is non- probabilistic. This is because the researcher is aiming to obtain data from fixed number of individuals (the “quotas”) who possess certain characteristics, and will make decisions about which individuals to include in the sample on the basis of whether they possess those characteristics, rather than by applying a fixed procedure, as in probabilistic sampling. The probability of any particular individual being included in a sample obtained by the quota method therefore cannot be known.

The procedure for taking a quota sample

Step 1: decide the variable or variables on which the sample is required to be representative of the population, and determine what proportion of the population fall into each of the different categories which are to be used.

For example, if the population is to be quota on the basis of income levels, then the population would need to be divided in to several income levels and the proportion of the population falling into each level found.

Step 2: decide the desire final sample size, and determine the quota for each category in the sampling frame.

Step3: finally, sample from the population using one of the non-probabilistic procedures until each quota is filled.

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Judgmental sampling

Judgmental sampling simply involves the application of the researcher’s judgment or expert knowledge to decide which members of the population should take into the sample. The problem of the unrepresentative nature of the samples obtained by this method is often exacerbated by combining the judgmental approach with opportunity sampling.

Snowball (Network or Chain) Sampling

A situation where we contact a member of a group and through him then we contact other members. E.g. heroine dealer, crime committer ……

Accidental

A situation where half hazard sampling or convenient. In this technique one reaches out take the cases that fall in two hand continue the process until the sample which is designated size.

SAMPLE SIZE DETERMINATION

‘How many people do I need in my sample?’ This is an incredibly common question, with the

unfortunate answer ‘it depends’. Sample size, as well as appropriate sampling strategies, very

much depends on the nature of your research and the shape and form of the data you intend to

collect. I think the best way to come up with a figure is to consider: your goals (transferability or

generalizability); the parameters of your population (how large it might be and how easy it is to

identify and find its elements); and the type of data you plan to collect.Working with qualitative data

Many researchers who collect qualitative data in order to understand populations are not looking

for representativeness. Their goal is often rich understanding that may come from the few, rather

than the many. Applicability comes from the ‘lessons learned’, that might – depending on

context – be applicable in alternative or broader populations. Such studies are not so much

dependent on representativeness and sample size as they are on the ability of the researcher to

argue the ‘relativeness’ of any sample (even a single case) to a broader context.

There are, however, researchers who wish to gather qualitative data AND represent a defined

population with some level of confidence, but they are often unsure how to do this because the

nature of collecting qualitative data generally limits sample size. In this case, rather than rely on

numbers, it will be up to the researcher to logically argue that their sample captures all the

various elements/characteristics of the population under study.

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Alternatively, researchers working with qualitative data can follow size guidelines required for

minimal statistical analysis (covered below).This will allow them the option of quantitatively

summarizing some of their qualitative findings in order to make more mathematical

generalizations about their population.

Working with quantitative data

If your intention is to work with quantified data, the basic rule of thumb is to attempt to get as

large a sample as possible within time and expense constraints. The logic is that the larger the

sample, the more likely it can be representative, and therefore generalizable. As for minimum

requirements, these are often determined by your anticipated level of statistical analysis.

Minimal statistical analysis: If your goal is to do just basic statistical analysis (sometimes used

to support more qualitative data analysis), you will generally need a minimum of about 30

respondents. Because statistical analysis is based on probability, the use of smaller numbers can

make it difficult to show statistical significance. This is particularly relevant for any findings

with large standard deviations (widely distributed results). Keep in mind that with small samples,

you will need to argue representativeness.

Intermediate statistical analysis: As you move to more sophisticated analysis, the use of any

‘subdivisions’ will require approximately 25 cases in each category. For example, you may have

a sample of 500 members of a particular community, but only 263 females. Out of this, there are

62 mothers with children under 18, and only 20 mothers with children under five. Statistical

analysis of mothers with children under five would be difficult. Similarly, if you want to show

significance in multivariate analysis (the analysis of simultaneous relationships among several

variables), you will need at least ten cases for each variable you wish to explore.

Advanced statistical analysis: If you want to represent a known population with a defined level

of confidence, you can actually calculate the required size using the following formula:

n = ((K × S)/E)2

where K is desired confidence level, S is sample standard deviation, and E is the required

level of precision.

If you, have little desire to work the above formula, you can use a ‘sample size calculator’ where

the only things you need to know are: the population size; the confidence interval – what range

you will accept above and below the mean, say ± 5%; and the confidence level – how sure you

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want to be that your findings within your confidence interval are more than coincidental.

Researchers usually shoot for a confidence level of 95% or 99%.

The calculator here used to produce Table below was found on the internet by typing ‘sample

size calculator’ into the Google search engine, and was found at www.

surveysystem.com/sscalc.htm. Table below gives you some idea of the required sample size for

more commonly used confidence levels. Note that: (1) as the population increases, shifts in

corresponding sample size do not increase as dramatically; (2) as you increase your levels of

confidence, your required sample size will increase significantly.

REQUIRED SAMPLE SIZE

Working with both quantitative and qualitative data

If you are working with both data types, you will find that the nature of collecting qualitative

data will limit your sample size. However, any planned statistical analysis will require a

minimum number of cases. The best advice is look above to determine the minimum size

necessary for any statistical analysis you wish to do, then consider the practicalities of collecting

and analyzing qualitative data from this sample. Unless you have unlimited time and money,

there will usually be some trade-off between the collection of rich, in-depth qualitative data and

the level of statistical analysis that might be possible.

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CONDUCTING RESEARCH: DATA COLLECTION METHODS

At the end of the planning stage of research, the next logical step to be taken leads the research to the stage of data collection.

1. EXPERIMENTATION In this method of data collection, a researcher sets up a controlled, quasi-artificial, laboratory

research situation in which he/she then generates data by observing the relationship between two

(or more) variables by deliberately manipulating one variable to see whether this produces a

change in the other. In a sense, this method applies also whenever a more-or-less artificial setting

is put in place for the purpose of replicating in a controlled context a real-life possibility (for

instance, war gaming or simulation).

The manipulated variable is referred to as the independent variable because it is independently

manipulated by the researcher. The variable examined for the effects of the manipulation(s) is

conveniently referred to as the dependent variable.

It must be noted that in the social sciences and the humanities, a pure experiment in which the

researcher has total control of the research setting, is actually an ideal. Nonetheless, the

ingredients of an experiment include the following:

a. A list of variables, including at least an independent variable (called the experimental

variable) and at least a dependent variable;

b. At least one experimental or study group (to be exposed to the independent variable) and

at least one control group (that will not be exposed to the independent variable). The

assignment of subjects from the population and into the groups are expected to be done

randomly and, from time to time, in combination with precision matching (in which in

addition to randomization, the researcher matches pairs of subjects that are as similar as

possible on variables or characteristics being controlled for and assigns one to the

experimental group and the other to the control group);

c. An appropriate research design. The researcher has to select an experimental research

design and adapt it to his/her needs.

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Studies that seek to establish causality (X causes Y) are embarking on a very ambitious

enterprise. This is because the logic of causality not only insists that x is a necessary condition

for y, it also insists that x is a sufficient condition for y (in other words, that x not only causes y,

but that whenever there is x, there will be y). Experiments are useful here because they help to

generate data that could assist to develop three crucial types of research evidence that are

required to establish causation beyond reasonable doubt. These are:

a. Evidence of Concomitant Variation between dependent and independent variables that

suggests either that the two are associated or they are not associated. In other words, such

evidence indicates the extent to which the variables concomitantly vary (whether change

in x leads to change in y). If the two variables are not associated, there can be no talk of

covariance – whether the two co-vary;

b. Evidence of Time-Order, that such an association is temporally continuous, and that the

presumed effect (dependent variable) did not occur before the presumed cause

(independent variable); and

c. Evidence of Elimination of Alternative Explanations, to the effect that other factors that

could be construed as possible determining conditions of the dependent variable (such as

enduring characteristics of subjects, extraneous events other than exposure to

experimental stimulus in the form of the independent variable, maturation/developmental

changes, influence of measurement procedures at the levels of instrumentation or pretest)

are eliminated from the research setting.

The basic measuring instrument for experimentation is the recording schedule. It takes the form

of either an interview schedule or a questionnaire. Issues relating to its construction will be taken

up later along with those relating to questionnaire construction.

2. DOCUMENT ANALYSIS

This is the method by which we generate data from records and documents (print and electronic,

audio and visual, published and broadcast). For the purpose of discussion two basic types of

document analysis are identified below.

A. Historical Methods/Library/Archival Search

The basic purpose of this method is to enable the researcher to reconstruct the past systematically and objectively through the collection, evaluation, verification and synthesizing of recorded

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evidence in order to establish facts and reach defensible conclusions as required in relation to research questions, objectives and hypotheses.

Its characteristics are as follows:

i. It depends on data observed by others and, for this reason; the researcher has to test the data for: authenticity, accuracy and significance.

ii. It is rigorous, systematic and exhaustiveiii. It is a critical method

The researcher will find a research notebook useful as he/she moves about tracing documents

and records, noting down references and major points in addition to photocopying and scanning

within the limits of research ethics, the law, and institutional procedures.

B. Content Analysis

Content Analysis involves the objective, systematic, often quantitative use of manifest

communication material and documents to generate data. The method enables the researcher to

distill from manifest content elements of latent content, influencing factors and intent of the

material in question. However, it deals first and foremost with manifest content, with the line and

not between the lines. No doubt, the researcher is often interested in the forces behind the

content, but he/she codes content only in terms of what he/she sees.

As outlined above, content analysis is objective in that it prescribes that categories used to collect

data must be defined so precisely that different researchers can analyze the same content using

these definitions and arrive at the same results. It is systematic in that it insists that the selection

of content to be analyzed must be based on a formal, predetermined, unbiased plan. The

researcher cannot choose to examine only those elements in the content that happen to fit his/her

objectives and ignore others. This characteristic separates content analysis from the run-of-the

mill, argumentative, biased collection of data to prove a point.

Content analysis is often, though not always, quantitative. Its result are usually expressed

numerically is such ways as frequencies, percentages, ratios, and contingency tables, among

others. The preference for quantification is based on the assumption that the precise language of

mathematics allows for consensus on what is right and what is incorrect.

In effect, therefore, content analysis helps in:

the study of attributes of content;

drawing conclusions about sources of content;

drawing conclusions about context, target and audience of content;

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Drawing conclusions about intent of content.

Types of Content Analysis

There are two broad types, namely,

(i) Analysis of “What Categories – focusing on substance and

(ii) Analysis of “How” Categories – focusing on form of content

I. “What” Categories: These include examination of:

Subject matter: Such content analysis answers the most elementary question: what is the

communication or content about? Is it about war or about peace? Is it about strategy or

tactics? Is it about quality of personnel, or quality of materials?

Direction: This focuses on the orientation of content, referring to the pro and con

treatment of the subject matter. Does the content condemn war or commend it?

Does it support peace or oppose it? Is it favourable toward adopted strategy (or tactic) or

is its content unfavourable, or neutral? Is its position positive in assessing quality of

personnel (or materials), or is it negative, neutral, or not clear? Does it approve or

disapprove, commend or condemn?

Authority: This type of analysis focuses on the source of the content; in other words, the

person, group, institution, country, subject, etc, or in whose name the content is made.

Target: This focuses on the audience or object to which the content is directed.

II. “How” Categories: Content analyses in this category include those that focus on:

Form or Type: This has to do with ordinary distinctions among forms in which content is

presented. For instance, a study of books on the Nigerian Civil War has to answer the

question of what type of book? Fiction or non-fiction? A study of security concerns in

radio broadcasts that could express these concerns: news, entertainment, interviews and

commentaries).

Statement Analysis: This is done more in the humanities than in the social sciences. It

refers to the grammatical or syntactical (sentence – building) form in which the content is

made or its structural component – how much is fact, preference etc

Intensity: This type of form analysis, often identified as dealing with sentimentalisation, refer

to the strength or excitement value of the content. Is it on the front page of the newspaper, or

is it buried inside? Is it the first item on television network news, or the very last? Does it

take 50 pages of a 60-page document, or is it treated in only 50 words in the same document?

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Stages in Content Analysis I. Identify and operationally define your concepts.

Conduct sampling for title of publication/material and for time/period: A study of

legal provisions for defence and security in Nigeria (1914 to 1999) could go ahead and

study the entire population of provisions. If, however, sampling for both title of material

(e.g. constitutions, statutes, administrative provisions and conventions) as well as period

to be covered. In the same vein, a study of newspaper coverage of Defence Headquarters

has to sample for newspapers as well as period in which selected newspapers will be

content-analyzed.

II. Establish the Unit of Analysis: This is the basic coding unit. It is the smallest

unit or division or segment of content upon which the decision on what kind of

score the content is to receive is based. This coding unit could be a particular

amount of space or time, a key word, theme or item.

III. Establish the Content Unit: As indicated above, the basic coding unit is the

smallest division of content on which the decision on how to score content is

based. Sometimes, however, a decision on how to score content cannot be arrived

at within the basic coding unit. In such situations there is obviously the need to go

beyond the basic coding unit and make the required decision in terms of the

content’s wider context. It is to ensure uniformity that, at the planning stage, this

problem is anticipated and provided for by the setting up of the context Unit. The

context unit is the largest division of content that the researcher/coder may have

to consult in order to be able to assign a score to the basic coding unit.

IV. Identify and operationally define your concepts and variables, painstakingly

outlining related coding categories and their meanings.

V. On the basis of (iv) above, construct appropriate measuring instrument called

Coding Schedule. A coding schedule is essentially like an interview schedule and

a questionnaire, with the specific difference that while an interview schedule or

questionnaire is administered on and responded to by human beings, a coding

schedule has content as its subject. The details of how to construct a coding

schedule are, therefore, adequately covered in the treatment of questionnaire

construction below, so long as it is borne in mind that adjustments have to be

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made in the construction of a coding schedule to put in proper focus the subjects

of content analysis.

VI. Test for Coder Reliability: Select judges/codes up to three or any other odd

number above three to pre-test your coding schedule on content. In a three-judge

test for coder reliability, the formula for calculating the Coefficient of Reliability

(whose result range from zero, indicating no reliability, to 1, indicating 100

percent reliability) is 3m

N1 + N2 + N3. In the formula, M is the number of

coding decisions on which all judges agree. N1, N2, N3 refers to number of

coding decisions made by each of the three judges. The closer the coefficient of

reliability is to 1, the greater is the confidence to go ahead and use the coding

schedule to collect data. A figure of about 0.85 and above is considered

comfortable. For other figures, it is suggested that the clarity in operational

definitions be enhanced and the pre-test be repeated until the coefficient of

reliability rises to an acceptable level.

VII. Finally, go ahead and collect data.

3. FIELD METHODS

Field methods are defined in terms of where much of the data collection associated with their

application takes place – literally in the field (and not in libraries or laboratories). In essence,

field methods involve the collection of data in the field. It involves the study of human

institutions, characteristics or behavior as they occur in their natural settings.

For research that adopts field methods to collect data, the goal should not be to draw conclusions

about cause-effect relationships (co-variation), since that would be impossible to attain through

collection of data in more or less natural settings. Rather, the goal has to be more of establishing

co-relationship; that is, to see the degree to which two variables co-relate (degree of correlation).

Field methods allow for more normal or natural conditions of selection and exposure. For this

reason, it is often suggested that, ideally, laboratory findings should be cross-validated by field

studies. In the same manner, suggestive evidence of relationships obtained through field studies

should be scrutinized further under the most rigorous control of experimentation.

Types of Field Methods

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These include four basic types

A. Observation Method

I. Director observation

II. Indirect observation

III. Participant observation

IV. Non-participant observation

V. Controlled observation

VI. Uncontrolled observation

B. The Interview Method

I. Loosely structured interview

II. Highly structured interview, often with interview schedule

III. Open interview

IV. Closed interviews

V. Face-to-face interviews

VI. Telephone interviews

VII. Oral interview

VIII. Internet interviews

IX. Focus Group Discussion (FGD)

X. Panel Studies

XI. Elite Interview

Highly structured interviews are often confused with the Questionnaire. The basic difference is

that, in interviews, the measuring instrument, called the interview schedule, is filled by the

researcher or his/her field assistant, whereas the questionnaire is filled by respondents (research

subjects). This basic difference has implications for the construction of measuring instruments,

since the questionnaire has to contain more instructions on how it is to be filled than the

interview schedule.

C. The Questionnaire

(i) Group Questionnaire

(ii) Privately Filled Face-to-Face Questionnaire

(iii) Mail Questionnaire

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(iv) Electronic Questionnaire

D. Combined

This is a combination of any of the preceding methods.

4. General Principles Guiding Instrument Construction

As indicated earlier, each method of data collection has its own instrument for

measuring/recording such data. Experiments require the use of recording schedules that have

much in common with interview schedules. Document analysis requires the use of research

notebooks and coding schedule, depending on whether you are involved only in library/archival

search or also in content analysis.

For field methods, the instrument varies from field notebooks (for observations and certain

interviews, including loosely structured, open, oral and elite interviews and FGDs) to interview

schedules for highly structured interviews and the questionnaire for the questionnaire method.

For those instruments that require elaborate construction as the recording schedule, the coding

schedule, the interview schedule and the questionnaire, the general principles underlying such

construction are itemized below.

(a) Define the research problem

(b) From (a), generate required variables

(c) From each variable, exhaustively generate categories to cover range of possible values. In

case(s) where it is felt that this cannot be exhaustive, create an open space in which to

indicate appropriate value.

(d) Items listed in the instrument should be appropriate.

(e) Items aimed at people should be as simple as possible. So, be clear and unambiguous.

Use simple language. Avoid vogue words.

(f) Items should be short and easy to follow, especially if they are aimed at people.

(g) Avoid negative biased or leading items, especially if the instrument is aimed at people

(h) Avoid double-barrel items.

(i) Avoid hypothetical items in dealing with people

(j) Avoid personalized and embarrassing items when dealing with people.

Instrument Design

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Most instruments that are targeted at people (recording schedules, interview schedules and

questionnaires) are often in three parts whereas the coding schedule often comprises only one

part (essentially the list of variables and their possible values).

Introductory Part This is made up of a short introductory note containing:

• self-introduction by researcher/assistant;

• purpose of study (make this general, not too specific);

• statement pleading the respondent fills the instrument himself/herself if a questionnaire;

• assurance of anonymity to ensure sincerity, and

• Guideline on how the instrument is to be returned to researcher/assistant if a

questionnaire.

Main Body

The length of this section will depend on the goal of the study. The general rule, however, is that

the section should not carry redundant items. This is especially so for instruments to be

administered on people, in order not to complicate response rate problems.

Closing Section

For instruments administered on people, show gratitude and, for questionnaires, remind

respondents about what next to do in terms of getting the instrument back to the researcher or

his/her representative.

Range of Items

In constructing the instrument, it is important to make wise use of the range of items available.

These include the following:

(a) Filter Items: These help to eliminate subjects as required

(b) General versus specific items: In terms of structure, it is usually preferred that general items

be listed before specific ones.

(c) Biographical Items: These items seek to collect data on demographic attributes of subjects.

When the subjects are human beings, the question arises: do you list demographic items first – or

last? There are two suggestions here. One is that, because people often get touchy when you start

by asking them demographic questions, and also because items that have direct bearing on the

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research should be asked first, demographic items should be deferred to the very end. Another

suggestion is that demographic items should be listed first because they often provide

independent variables, but even more so in studies whose main goal is to study demographic

issues.

(d) Matrix Items: A matrix item is a combination of items with the same set of answers. This

helps to safe space.

(e) Free-Answer Items: These are items with open-ended responses

(f) Multiple-Type Items: These are close-ended items with several options listed as responses to

them

(g) Dichotomous items: These are items with only two possible responses.

(h) Factual Items: These are items that seek to measure the knowledge level of subjects. They

are often problematic.

(i) Opinion Items: These are items that tap at the domain of opinion. They are also problematic,

because they need to simultaneously be extensive (be many-sided) and intensive as well as

sensitive to nuances.

Writing a research report

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Format of the search report

Although some flexibility and range latitude is permitted in the format of a research report, the

information in this section is useful and consistent with one of the widely used style manuals –

American Psychological Association (APA). Thus, the outline given below presents the

sequence of topics covered in the typical research report. Generally, the layout of a research

report should comprise three broad parts: (1) preliminary section, (2) main text, and (3) reference

section. Each main section consists of several subsections.

1. Preliminary section

Title page

Acknowledgements

List of tables and figures

Abstracts

2. Main text

Chapter one: Introduction

Chapter two: Review of related literature

Chapter three: Methods of the study

Chapter four: Results and Discussion

Chapter five: Summary, Conclusion and Recommendations

3. The reference section

References

Appendices (if appropriate)

I. Preliminary section

The preliminary section of a research report includes:

A. Title page

The first page of the report is the title page. This page includes the following:

Title of the study,

Name of the institution to which the report will be submitted,

Author’s affiliation and

Date of submission of the report

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The title should be concise and should indicate clearly the purpose of the study. One should keep

in mind its possible usefulness to the reader who may scan a bibliography in which it may be

listed. The title should not claim more for the study than it actually delivers. It should not be

stated so broadly that it seems to provide answer that cannot be generalized, either from the data

gathered or from the methodology employed. Besides, it should not be vague and ambiguous.

B. Acknowledgments

It is a section that recognizes individuals and institutions to which the researcher is indebted for

providing credible assistance in due course of his or her study. Acknowledgement should be as

brief and simple as possible.

C. Table of contents

Table of contents includes list of major classifications of the report with their corresponding page

numbers for easy reference.

D. List of tables and figures

When you have figures and tables in your report, you need a separate page for list of titles of

tables and figures along with their corresponding page numbers.

E. Abstract

The abstract describes the study in 100 to 150 words. Included in this summary are the problem

under study, characteristics of the subjects, the procedures used, the findings of the study, and

the conclusions reached by the researcher. A good abstract will increase the readership of the

article because many persons start their views with abstracts.

II. Main body of the report or text

The main body of the report consists of five chapters: (1) introduction, (2) review of related

literature (3) method of the study, (4) presentation and data analysis (results and discussion) and

(5) summary, conclusion and recommendations.

A. Introduction

The major subdivisions of this part are generally the ones shown in the researcher’s proposal. A

well written introduction may have these components:

Background to the problem,

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Statement of the problem,

Objectives of the study,

Justification and significance of the study,

Delimitation and limitation of the study,

Operational definitions of important terms,

Definitions of variables investigated and

Statement of hypothesis

The background material with some literature citations provides an insight in to where the ideas

come from. The researcher must give a clear and definitive statement of the problem. The

problem must indicate the need for the research. It also necessary to indicate why the problem is

important in terms of theory and /or practice.

The introduction part should also include a clearer rational for the hypotheses to be proposed,

definition of the variables investigated and controlled and a formal statement of each hypotheses.

Each hypothesis must be stated so that it is clear how it will be tasted. Terms must be clearly

described, and predicted outcomes must be measurable. Generally as the primary function of the

introduction is to state the main issue or problem to be addressed in the paper, it should be lucid,

complete, and concise. It has to be written in a lively and stimulating manner in order to arouse

the interest of the reader to go through the report.

B. Review of related literature

This is a section for documenting with insight theoretical and empirical investigations that had

been carried out as related to the study at hand. The researcher must demonstrate and

understanding of the existing literature pertinent to his or her study.

C. Methods of the study

The main body of the report continuous with design of the study, methodology, or with the

“method” section. This section should provide sufficient detail of the tasks, procedures and

measurements and descriptions. It includes detailed description of the manner in which decisions

have been made about the type of data needed for the study, the tools and approaches used for

their collection and method by which they have been collected. Definition of the population, the

size of the sample and the rational for the size, number of subjects involved and declined

statistical tools used and rational for using them will be dealt with in this unit.

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The methodological part of the research report, in general, should include at least two

subsections: subjects and procedures. The subsection on subjects needs to identify the

participants of the study, the number of persons included in the study, and the means by which

the participants were selected. Major demographic characteristics, such as age, sex,

socioeconomic status, race, are included as they relate to the study. Sufficient information must

be provided to permit the reader to be able to replicate the sample. The subsection on the

procedure, as its name indicates, describes the actual steps carried out in conduction the study.

This includes the measurement devices, if no separate section is provided; the experimental

treatments; the order of assessment, if more than one; the time period, if pertinent; and any

design features used to control potentially confounding variables. Again enough information

must be provided to permit replication. In addition to the “subject” and “procedures”, the method

section may also include other subsections when the need arises. For example, here could a

subsection on instrumentation or description of the instrument and methods of data analysis.

D. Result and discussion

The results and discussion section or chapter starts with presenting the data and statistical

analysis, and followed by textual discussion. All the relevant findings are presented, including

those that do not support the hypothesis. Data summaries in the form of tables and figures are

very useful to supplement textual material, because the old adage, “A picture is worth a thousand

words” is also applies here.

Following tables and figures there are discussion on the implications of findings, including

whether the hypotheses were supported or should be rejected; if specific hypotheses were

proposed in the introduction part, the outcome of the tests of these hypotheses can be

summarized here. It is appropriate to discuss both the theoretical implications and practical

application of the study.

Some tips on the presentation and analysis of results:

As tables or figures are self-explanatory, the textual discussion shouldn’t be a duplicate

of the table. Only important facts that lead to generation will be discussed.

Tables and figures shouldn’t be used when the data cannot readily be presented in a few

sentences in the text.

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Data in the text and in tables or figures should not be redundant; rather they should be

supplementary.

The text should indicate what the reader should expect to see in the tables and figures so

as to clarify their meanings.

Relevant statistical tests should be summarized to inform the reader of the reliability of

the observed trend.

Tables that are too lengthy (or as cumbersome) may better be placed in the appendix.

The level of significance for statically analyses should be presented

E. Summary, Conclusion and Recommendation

a. Summary and Conclusion

This part begins with a brief restatement of the preceding three chapters: Introduction,

Methodology, and Result and Discussion. The researcher should refresh the reader’s mind by

revising (in a brief) the statement of the problem, the hypotheses, methods used and discussion

of the findings. This section must focus attention to announce the retention or rejection of the

hypotheses. The researcher should also include conclusions that reflect whether the original

problem is better understood, or even resolved, as a result of this study. Most readers scan this

section in order to get an overview of the study and judge its relevance. It should, this, be written

with maximum diligence and clarity.

b. Recommendation

As a result of the outcomes of your study, you may forward possible solution that may alleviate

the problem. New hypotheses may ale be proposed if the data do not support the original

hypotheses; proposal for future is appropriate. Unanswered questions that were raised in the due

course of the study and which required future investigation in the area should be forwarded. In

general, in order to be acceptable, recommendations should:

Be clear and unambiguous,

Be realistic, plausible, and operational,

Point out the responsible body or part to translate the suggested solutions in to practice,

and

Be modest than assertive.

III. The reference section

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References consists of all the documents including journal articles, books technical reports,

computer programs and unpublished works that are mentioned in the text of the manuscript.

References are arranged in alphabetical order by the last names of the first- named authors.

When no author is listed, the first word of the title or sponsoring organization is used to begin the

entry.

Style of writing

The style of the research report should generally be concise, clear, logical, simple, and formal.

The following point should be considered in writing research report:

Avoid using slang, proverbial or polite phrases.

As objectivity is the primary goal there should be no element of exhortation or

persuasion. The research report should describe and explain, rather than try to convince

or move to action.

Avoid stories of personal experience and argumentation discourses.

Make the form of the report impersonal. For example, the statement, “I randomly selected

100 subjects for the study” is not in line with the formal style of writing a report. The best

way could be: “one hundred subjects were randomly selected for the study.”

Only the last names of cited authorities are used. Title such as Professor, Dr., Ato, Mr.,

Dear, etc. omitted.

The past tense should be used in describing research procedures that have been

completed.

With the exception of commonly accepted abbreviations (e.g., IQ), avoid their use in

your report. Abbreviation, of course, may used only after their referent has been spelled

out.

To ensure flow of ideas, avoid statistical formulas and computations in the text of the

report.

Writing research reports effectively is an easy task. Good reports are not written hurriedly. Even

skillful and experienced writers revise many times before they submit a manuscript for

publication.

Pagination

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Every page in the research report is given a number. There are two series separate page numbers:

Roman number and Arabic numbers. The preliminary part of the report is numbered with small

roman numbers. Page number of the preliminaries should be placed in the center at the bottem of

the page without any punctuation following them. Arabic numbers are used to number all other

pages beginning with the first page of the first chapter.

These numbers appear without punctuation on the bottom right hand corner of the pages.

Tables and illustrations

A table is a systematic method of presenting statistical data in vertical columns and horizontal

rows, according to some classification of subject matter. Tables enable the reader to comprehend

and interpret masses of data rapidly and grasp significant details and relationships at a glance.

Tables and figures should be used sparingly; too many will over whole the reader.

Tables and illustrations should be placed as close as possible to the parts of the next to which

they relate. Good tables are relatively simple, concentrating on a limited number of ideas.

Including too much data in a table minimizes the value of tabular presentation. As simplicity and

unity is the essential characteristics of good tables, the following are the general guiding

principles of using tables in research report writing:

Do not include too much data on a single table; this will confuse the reader to see

expected relationship. It is often advisable to use several tables rather than to include too

many details in a single one.

Tables need to be gives headings. A table is usually labeled table, given an Arabic

numerals and captioned. Type both label and caption flush left on separate lines above

the table and capitalize them as you would a title (do not use all capital letters).

If a table is large enough to cover more than half a page, place by itself at the center of a

page. If it is short and less than half a page retain it with the next it refer to.

Text references or discussion about a given table should be done according to the

placement (separate or same page) of the table. That is, if a table is on a separate page

following the reference, discussion, implication, then the text reference should identify

tables by number, rather than by such expressions as “the table above” or “the following

table.”

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If a table is too long and detail so as to interrupt the flow of the textual discussion, put it

in the appendix.

Give the sources of the table and any notes immediately below the table.

To avoid confusion between notes to the text and note to the table, designate notes to the

table with different form of lettering.

Figures

A figure is a device that presents statistical data in graphic form. The term figure is applied to a

wide variety of charts, maps, sketches, diagrams, and drawings. Many of the guidelines given for

good tables work for figures.

Ethical considerations in behavioral research

In planning a behavioral research project, sometimes it is important to consider the ethical guideline designed to protect the subjects. If your research project involves some elements of risks, however minor, and raises questions about the ethics of the process, you have to follow the APA (the American Psychological Association) code of ethics. Highlighted, hereafter, are the most important areas with which ethical guidelines ideal:

a. Informed consent: whenever possible, subjects should be informed of the purpose of the research. When subjects are not competent to give informed consent due to age, illness, or disability, the informed consent of parent, guardians or responsible agents must be secured. The freedom to participate is basic, and it includes the freedom to withdraw from an experiment or research at any time.

b. Invasion of privacy: ordinarily it is justifiable to observe and record behavior that is essentially public, behavior that others normally would be in a position to observe. It is an invasion of private to observe and record intimate behavior that the subject has reason to believe is private. To be free from invasions of privacy, the researcher should explain the reasons and secure permission. If the subjects are volunteers to participate with full knowledge of the purposes and procedures employed, intimate behaviors can be observed ethically.

c. Confidentiality: the ethical research holds all information that he or she may gather about the subject in strict confidence, disguising the participant identity in all records and reports. No one should be in a position to threaten the subject’s anonymity nor any information be released without his or her permission.

d. Protection from stress, harm, or danger: in using treatment that may have a temporary or permanent effect on the subjects, the researcher must take all precaution to protect their well-being.

Five principles for research ethics

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Here are five recommendations APA's Science Directorate gives to help researchers steer clear

of ethical quandaries:

1. Discuss intellectual property franklyAcademe's competitive "publish-or-perish" mindset can be a recipe for trouble when it comes

to who gets credit for authorship. The best way to avoid disagreements about who should get

credit and in what order is to talk about these issues at the beginning of a working relationship,

even though many people often feel uncomfortable about such topics.

"It's almost like talking about money," explains Tangney. "People don't want to appear to be

greedy or presumptuous."

APA's Ethics Code offers some guidance: It specifies that "faculty advisors discuss publication

credit with students as early as feasible and throughout the research and publication process as

appropriate." When researchers and students put such understandings in writing, they have a

helpful tool to continually discuss and evaluate contributions as the research progresses.

However, even the best plans can result in disputes, which often occur because people look at the

same situation differently. "While authorship should reflect the contribution," says APA Ethics

Office Director Stephen Behnke, JD, PhD, "we know from social science research that people

often overvalue their contributions to a project. We frequently see that in authorship-type

situations. In many instances, both parties genuinely believe they're right." APA's Ethics Code

stipulates that psychologists take credit only for work they have actually performed or to which

they have substantially contributed and that publication credit should accurately reflect the

relative contributions: "Mere possession of an institutional position, such as department chair,

does not justify authorship credit," says the code. "Minor contributions to the research or to the

writing for publications are acknowledged appropriately, such as in footnotes or in an

introductory statement."

The same rules apply to students. If they contribute substantively to the conceptualization,

design, execution, analysis or interpretation of the research reported, they should be listed as

authors. Contributions that are primarily technical don't warrant authorship. In the same vein,

advisers should not expect ex-officio authorship on their students' work.

Matthew McGue, PhD, of the University of Minnesota, says his psychology department has

instituted a procedure to avoid murky authorship issues. "We actually have a formal process here

where students make proposals for anything they do on the project," he explains. The process

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allows students and faculty to more easily talk about research responsibility, distribution and

authorship.

Psychologists should also be cognizant of situations where they have access to confidential ideas

or research, such as reviewing journal manuscripts or research grants, or hearing new ideas

during a presentation or informal conversation. While it's unlikely reviewers can purge all of the

information in an interesting manuscript from their thinking, it's still unethical to take those ideas

without giving credit to the originator.

"If you are a grant reviewer or a journal manuscript reviewer [who] sees someone's research

[that] hasn't been published yet, you owe that person a duty of confidentiality and anonymity,"

says Gerald P. Koocher, PhD, editor of the journal Ethics and Behavior and co-author of "Ethics

in Psychology: Professional Standards and Cases" (Oxford University Press, 1998).

Researchers also need to meet their ethical obligations once their research is published: If authors

learn of errors that change the interpretation of research findings, they are ethically obligated to

promptly correct the errors in a correction, retraction, erratum or by other means.

To be able to answer questions about study authenticity and allow others to reanalyze the results,

authors should archive primary data and accompanying records for at least five years, advises

University of Minnesota psychologist and researcher Matthew McGue, PhD. "Store all your data.

Don't destroy it," he says. "Because if someone charges that you did something wrong, you can

go back."

"It seems simple, but this can be a tricky area," says Susan Knapp, APA's deputy publisher. "The

APA Publication Manual Section 8.05 has some general advice on what to retain and suggestions

about things to consider in sharing data."

The APA Ethics Code requires psychologists to release their data to others who want to verify

their conclusions, provided that participants' confidentiality can be protected and as long as legal

rights concerning proprietary data don't preclude their release. However, the code also notes that

psychologists who request data in these circumstances can only use the shared data for

reanalysis; for any other use, they must obtain a prior written agreement.

2. Be conscious of multiple rolesAPA's Ethics Code says psychologists should avoid relationships that could reasonably impair

their professional performance or could exploit or harm others. But it also notes that many kinds

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of multiple relationships aren't unethical--as long as they're not reasonably expected to have

adverse effects.

That notwithstanding, psychologists should think carefully before entering into multiple

relationships with any person or group, such as recruiting students or clients as participants in

research studies or investigating the effectiveness of a product of a company whose stock they

own.

For example, when recruiting students from your Psychology 101 course to participate in an

experiment, be sure to make clear that participation is voluntary. If participation is a course

requirement, be sure to note that in the class syllabus, and ensure that participation has educative

value by, for instance, providing a thorough debriefing to enhance students' understanding of the

study. The 2002 Ethics Code also mandates in Standard 8.04b that students be given equitable

alternatives to participating in research.

Perhaps one of the most common multiple roles for researchers is being both a mentor and lab

supervisor to students they also teach in class. Psychologists need to be especially cautious that

they don't abuse the power differential between themselves and students, say experts. They

shouldn't, for example, use their clout as professors to coerce students into taking on additional

research duties.

By outlining the nature and structure of the supervisory relationship before supervision or

mentoring begins, both parties can avoid misunderstandings, says George Mason University's

Tangney. It's helpful to create a written agreement that includes both parties' responsibilities as

well as authorship considerations, intensity of the supervision and other key aspects of the job.

"While that's the ideal situation, in practice we do a lot less of that than we ought to," she notes.

"Part of it is not having foresight up front of how a project or research study is going to unfold."

That's why experts also recommend that supervisors set up timely and specific methods to give

students feedback and keep a record of the supervision, including meeting times, issues

discussed and duties assigned.

If psychologists do find that they are in potentially harmful multiple relationships, they are

ethically mandated to take steps to resolve them in the best interest of the person or group while

complying with the Ethics Code.

3. Follow informed-consent rules

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When done properly, the consent process ensures that individuals are voluntarily participating in

the research with full knowledge of relevant risks and benefits.

"The federal standard is that the person must have all of the information that might reasonably

influence their willingness to participate in a form that they can understand and comprehend,"

says Koocher, dean of Simmons College's School for Health Studies.

APA's Ethics Code mandates that psychologists who conduct research should inform participants

about:

The purpose of the research, expected duration and procedures.

Participants' rights to decline to participate and to withdraw from the research once it has

started, as well as the anticipated consequences of doing so.

Reasonably foreseeable factors that may influence their willingness to participate, such as

potential risks, discomfort or adverse effects.

Any prospective research benefits.

Limits of confidentiality, such as data coding, disposal, sharing and archiving, and when

confidentiality must be broken.

Incentives for participation.

Who participants can contact with questions.

Experts also suggest covering the likelihood, magnitude and duration of harm or benefit of

participation, emphasizing that their involvement is voluntary and discussing treatment

alternatives, if relevant to the research.

Keep in mind that the Ethics Code includes specific mandates for researchers who conduct

experimental treatment research. Specifically, they must inform individuals about the

experimental nature of the treatment, services that will or will not be available to the control

groups, how participants will be assigned to treatments and control groups, available treatment

alternatives and compensation or monetary costs of participation.

If research participants or clients are not competent to evaluate the risks and benefits of

participation themselves--for example, minors or people with cognitive disabilities--then the

person who's giving permission must have access to that same information, says Koocher.

Remember that a signed consent form doesn't mean the informing process can be glossed over,

say ethics experts. In fact, the APA Ethics Code says psychologists can skip informed consent in

two instances only: When permitted by law or federal or institutional regulations, or when the

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research would not reasonably be expected to distress or harm participants and involves one of

the following:

The study of normal educational practices, curricula or classroom management methods

conducted in educational settings.

Anonymous questionnaires, naturalistic observations or archival research for which

disclosure of responses would not place participants at risk of criminal or civil liability or

damage their financial standing, employability or reputation, and for which

confidentiality is protected.

The study of factors related to job or organization effectiveness conducted in

organizational settings for which there is no risk to participants' employability, and

confidentiality is protected.

If psychologists are precluded from obtaining full consent at the beginning--for example, if the

protocol includes deception, recording spontaneous behavior or the use of a confederate--they

should be sure to offer a full debriefing after data collection and provide people with an

opportunity to reiterate their consent, advise experts.

The code also says psychologists should make reasonable efforts to avoid offering "excessive or

inappropriate financial or other inducements for research participation when such inducements

are likely to coerce participation."

4. Respect confidentiality and privacyUpholding individuals' rights to confidentiality and privacy is a central tenet of every

psychologist's work. However, many privacy issues are idiosyncratic to the research population,

writes Susan Folkman, PhD, in "Ethics in Research with Human Participants" (APA, 2000). For

instance, researchers need to devise ways to ask whether participants are willing to talk about

sensitive topics without putting them in awkward situations, say experts. That could mean they

provide a set of increasingly detailed interview questions so that participants can stop if they feel

uncomfortable.

And because research participants have the freedom to choose how much information about

themselves they will reveal and under what circumstances, psychologists should be careful when

recruiting participants for a study, says Sangeeta Panicker, PhD, director of the APA Science

Directorate's Research Ethics Office. For example, it's inappropriate to obtain contact

information of members of a support group to solicit their participation in research. However,

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you could give your colleague who facilitates the group a letter to distribute that explains your

research study and provides a way for individuals to contact you, if they're interested.

Other steps researchers should take include:

Discuss the limits of confidentiality. Give participants information about how their data

will be used, what will be done with case materials, photos and audio and video

recordings, and secure their consent.

Know federal and state law. Know the ins and outs of state and federal law that might

apply to your research. For instance, the Goals 2000: Education Act of 1994 prohibits

asking children about religion, sex or family life without parental permission. Another

example is that, while most states only require licensed psychologists to comply with

mandatory reporting laws, some laws also require researchers to report abuse and neglect.

That's why it's important for researchers to plan for situations in which they may learn of

such reportable offenses. Generally, research psychologists can consult with a clinician or

their institution's legal department to decide the best course of action.

Take practical security measures. Be sure confidential records are stored in a secure

area with limited access, and consider stripping them of identifying information, if

feasible. Also, be aware of situations where confidentiality could inadvertently be

breached, such as having confidential conversations in a room that's not soundproof or

putting participants' names on bills paid by accounting departments.

Think about data sharing before research begins. If researchers plan to share their

data with others, they should note that in the consent process, specifying how they will be

shared and whether data will be anonymous. For example, researchers could have

difficulty sharing sensitive data they've collected in a study of adults with serious mental

illnesses because they failed to ask participants for permission to share the data. Or

developmental data collected on videotape may be a valuable resource for sharing, but

unless a researcher asked permission back then to share videotapes, it would be unethical

to do so. When sharing, psychologists should use established techniques when possible to

protect confidentiality, such as coding data to hide identities. "But be aware that it may be

almost impossible to entirely cloak identity, especially if your data include video or audio

recordings or can be linked to larger databases," says Merry Bullock, PhD, associate

executive director in APA's Science Directorate.

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Understand the limits of the Internet. Since Web technology is constantly evolving,

psychologists need to be technologically savvy to conduct research online and cautious

when exchanging confidential information electronically. If you're not a Internet whiz,

get the help of someone who is. Otherwise, it may be possible for others to tap into data

that you thought was properly protected.

5. Tap into ethics resourcesOne of the best ways researchers can avoid and resolve ethical dilemmas is to know both what

their ethical obligations are and what resources are available to them.

"Researchers can help themselves make ethical issues salient by reminding themselves of the

basic underpinnings of research and professional ethics," says Bullock. Those basics include:

The Belmont Report. Released by the National Commission for the Protection of

Human Subjects of Biomedical and Behavioral Research in 1979, the report provided the

ethical framework for ensuing human participant research regulations and still serves as

the basis for human participant protection legislation.

APA's Ethics Code which offers general principles and specific guidance for research

activities.

Moreover, despite the sometimes tense relationship researchers can have with their institutional

review boards (IRBs), these groups can often help researchers think about how to address

potential dilemmas before projects begin, says Panicker. But psychologists must first give their

IRBs the information they need to properly understand a research proposal.

"Be sure to provide the IRB with detailed and comprehensive information about the study, such

as the consent process, how participants will be recruited and how confidential information will

be protected," says Bullock. "The more information you give your IRB, the better educated its

members will become about behavioral research, and the easier it will be for them to facilitate

your research."

As cliché as it may be, says Panicker, thinking positively about your interactions with an IRB

can help smooth the process for both researchers and the IRBs reviewing their work.

Further reading

American Psychological Association. (2002). Ethical principles of psychologists and code of conduct. American Psychologist, 57(12).

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Sales, B.D., & Folkman, S. (Eds.). (2000). Ethics in research with human participants. Washington, DC: American Psychological Association.

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