Section C(Analytical and descriptive surveys... )
- 1. Descriptive Surveys:- A descriptive survey attempts to picture or document current conditions or attitudes, that is, to describe what exists at the moment. For example, the Department of Labor regularly conducts surveys on the amount of unemployment in the United States. Broadcast stations and networks continually survey their audiences to determine programming tastes, changing values, and lifestyle variations that might affect programming. In descriptive surveys of this type, researchers are interested in discovering the current situation in a given area.
- 2. Analytical Surveys:- Analytical surveys attempt to describe and explain why certain situations exist. In this approach two or more variables are usually examined to test research hypotheses. The results allow researchers to examine the interrelationships among variables and to draw explanatory inferences. For example, television station owners occasionally survey the market to determine how lifestyles affect viewing habits, or to determine whether viewers' lifestyles can be used to predict the success of syndicated programming.
- 3. To make predictions about events, concepts, or phenomena, researchers must perform detailed, objective analyses. One procedure to use in such analyses is a census, in which every member of the population is studied. Conducting a census for each research project is impractical, however, and researchers must resort to alternative methods. The most widely used alternative is to select a random sample from the population, examine it, and make predictions from it that can be generalized to the population. There are several procedures for identifying the units that are to compose a random sample.
- 4. If the scientific procedure is to provide valid and useful results, researchers must pay close attention to the methods they use in selecting a sample. Sampling procedures must not be taken lightly in the process of scientific investigation. It makes no sense to develop a research design for testing a valuable hypothesis or research question and then nullify this effort by neglecting correct sampling procedures. These procedures must be continually scrutinized to ensure that the results of an analysis are not sample-specific; that is, results are not based on the type of sample used in the study.
- 5. One goal of scientific research is to describe the nature of a population, that is, a group or class of subjects, variables, concepts, or phenomena. In some cases this is achieved through the investigation of an entire class or group, such as a study of prime- time television programs during the week of September 10 — 16. The process of examining every member of such a population is called a census. In many situations, however, the chance of investigating an entire population is remote, if not nonexistent, due to time and resource constraints. Studying every member of a population is also generally cost prohibitive, and may in fact confound the research because measurements of large numbers of people often affect measurement quality.
- 6. The usual procedure in these instances is to select a sample from the population. A sample is a subset of the population that is taken to be representative of the entire population. An important word in this definition is representative. A sample that is not representative of the population, regardless of its size, is inadequate for testing purposes: the results cannot be generalized.
- 7. A probability sample is selected according to mathematical guidelines whereby the chance for selection of each unit is known. A non-probability sample does not follow the guidelines of mathematical probability. However, the most significant characteristic distinguishing the two types of samples is that probability sampling allows researchers to calculate the amount of sampling error present in a research study; non-probability sampling does not.
- 8. In deciding whether to use a probability or a non-probability sample, a researcher should consider four points. 1. Purpose of the study: Some research studies are not designed for generalization to the population, but rather to investigate variable relationships or to collect exploratory data for designing questionnaires or measurement instruments. A non-probability sample is often appropriate in situations of these types. 2. Cost versus value: The sample should produce the greatest value for the least investment. If the cost of a probability sample is too high in relation to the type and quality of information collected, a non-probability sample is a possible alternative.
- 9. 3. Time constraints: In many cases researchers collecting preliminary information operate under time constraints imposed by sponsoring agencies, management directives, or publication guidelines. Since probability sampling is often time-consuming, a non-probability sample may provide temporary relief. 4. Amount of error allowed: In preliminary or pilot studies, where error control is not a prime concern, a non-probability sample is usually adequate.
- 10. Probability sampling generally incorporates some type of systematic selection procedure, such as a table of random numbers, to ensure that each unit has an equal chance of being selected. However, it does not always guarantee a representative sample from the population, even when systematic selection is followed. It is possible to randomly select 50 members of the student body at a university in order to determine the average height of all students enrolled and, by extraordinary coincidence, end up with 50 candidates for the basketball team. Such an event is unlikely, but it is possible, and this possibility underscores the need to replicate any study.
- 11. Nonprobability sampling is frequently used in mass media research, particularly in the form of available samples, samples using volunteer subjects, and purposive samples. Mall intercepts use nonprobability sampling. An available sample (also known as convenience sample) is a collection of readily accessible subjects for study, such as a group of students enrolled in an introductory mass media course, or shoppers in a mall. Although available samples can be helpful in collecting exploratory information and may produce useful data in some instances, the samples are problematic because they contain unknown quantities of error. Researchers need to consider the positive and negative qualities of available samples before using them in a research study.
- 12. Subjects who constitute a volunteer sample also form a nonprobability sample, since the individuals are not selected mathematically. The characteristics of volunteer subjects can be defined on the basis of several studies and found that such subjects, in comparison with nonvolunteers, tend to exhibit higher educational levels, higher occupational status, greater need for approval, higher intelligence, and lower authoritarianism. They also seem to be more sociable, more "arousal- seeking," and more unconventional; they are more likely to be first children, and they are generally younger.
- 13. These characteristics mean that the use of volunteer subjects may significantly bias the results of a research study and may lead to inaccurate estimates of various population parameters. Also, available data seem to indicate that volunteers may, more often than nonvolunteers, provide data to support a researcher's hypothesis. In some cases volunteer subjects are necessary—for example, in comparison tests of products or services. However, volunteers should be used with caution because, as with available samples, there is an unknown quantity of error present in the data.
- 14. A purposive sample includes subjects selected on the basis of specific characteristics or qualities and eliminates those who fail to meet these criteria. Purposive samples are often used in advertising studies: researchers select subjects who use a particular type of product and ask them to compare it with a new product. A purposive sample is chosen with the knowledge that it is not representative of the general population; rather it attempts to represent a specific portion of the population. In a similar method, the quota sample, subjects are selected to meet a predetermined or known percentage.
- 15. Another nonprobability sampling method is to select subjects haphazardly on the basis of appearance or convenience, or because they seem to meet certain requirements (the subjects look educated). Haphazard selection involves researcher subjectivity and introduces error. Some haphazard samples give the illusion of a probability sample; these must be carefully approached.
- 16. 1. Simple Random Sample :- The most basic type of probability sampling is the simple random sample, where each subject or unit in the population has an equal chance of being selected. If a subject or unit is drawn from the population and removed from subsequent selections, the procedure is known as random sampling without replacement — the most widely used random sampling method. Random sampling with replacement involves returning the subject or unit into the population so that it has a chance of being chosen another time. Sampling with replacement is often used in more complicated research studies such as nationwide surveys.
- 17. Simple random samples for use in telephone surveys are often obtained by a process called random digit dialing. One method involves randomly selecting four-digit numbers (usually generated by a computer or through the use of a random numbers table) and adding them to the three-digit exchange prefixes in the city in which the survey is conducted. A single four-digit series may be used once, or it may be added to all the prefixes. Unfortunately, a large number of the telephone numbers generated by this method of random digit dialing are invalid because some phones have been disconnected, some numbers generated have not yet been assigned, and for other reasons. Therefore, it is advisable to produce at least three times the number of telephone numbers needed; if a sample of 100 is required, at least 300 numbers should be generated to allow for invalid numbers.
- 18. A second random digit dialing method that tends to decrease the occurrence of invalid numbers involves adding from one to three random digits to a telephone number selected from a phone directory or list of phone numbers. One first selects a number from a list of telephone numbers (a directory or list purchased from a supplier). Assume that the number 448-3047 was selected from the list. The researcher could simply add a predetermined number, say 6, to produce 448-3053; or a predetermined two-digit number, say 21, to achieve 448-3068; or even a three-digit number, say 112, to produce 448-3159. Each variation of the method helps to eliminate many of the invalid numbers produced in pure random number generation, since telephone companies tend to distribute telephone numbers in series, or blocks. In this example, the block 30— is in use, and there is a good chance that random add-ons to this block will be residential telephone numbers.
- 19. As indicated here, random number generation is possible via a variety of methods. However, two rules are always applicable: (1)each unit or subject in the population must have an equal chance of being selected, and (2) the selection procedure must be free from subjective intervention by the researcher. The purpose of random sampling is to reduce sampling error; violating random sampling rules only increases the chance of introducing such error into a study. Similar in some ways to simple random sampling is a procedure called systematic sampling, in which every X subject or unit is selected from a population.
- 20. Advantages 1. Detailed knowledge of the population is not required. 2. External validity may be statistically inferred. 3. A representative group is easily obtainable. 4. The possibility of classification error is eliminated. Disadvantages 1. A list of the population must be compiled. 2. A representative sample may not result in all cases. 3. The procedure can be more expensive than other methods.
- 21. Systematic samples are frequently used in social research. They often save time, resources, and effort when compared to simple random samples. In fact, since the procedure so closely resembles a simple random sample, many researchers consider systematic sampling equal to the random procedure. The method is widely used in selecting subjects from lists such as telephone directories, Broadcasting/Cablecasting Yearbook, and Editor & Publisher. The degree of accuracy of systematic sampling depends on the adequacy of the sampling frame, or a complete list of members in the population.
- 22. Telephone directories are inadequate sampling frames in most cases, since not all phone numbers are listed, and some people do not have telephones at all. However, lists that include all the members of a population have a high degree of precision. Before deciding to use systematic sampling, one should consider the goals and purpose of a study, as well as the availability of a comprehensive list of the population. If such a list is not available, systematic sampling is probably ill- advised. One major problem associated with systematic sampling is that the procedure is susceptible to periodicity; that is, the arrangements or order of the items in the population list may bias the selection process. For example, consider the problem mentioned earlier of analyzing television programs to determine how the elderly are portrayed. Quite possibly, every 10th program listed may have been aired by Channel 1; the result would be a non representative sampling of the three networks.
- 23. Periodicity also causes problems when telephone directories are used to select samples. The alphabetical listing does not allow each person or household an equal chance of being selected. One way to solve the problem is to cut each name from the directory, place them in a "hat," and draw names randomly. Obviously, this would take days to accomplish and is not a real alternative. An easier way to use a directory is to tear the pages loose, mix them up, randomly select pages, and then randomly select names. Although this procedure doesn't totally solve the problem, it is generally accepted when simple random sampling is impossible. If periodicity is eliminated, systematic sampling can be an excellent sampling methodology.
- 24. Advantages 1. Selection is easy. 2. Selection can be more accurate than in a simple random sample. 3. The procedure is generally inexpensive. Disadvantages 1. A complete list of the population must be obtained. 2. Periodicity may bias the process.
- 25. Although a simple random sample is the usual choice in most research projects, some researchers don't wish to rely on randomness. In some projects, researchers want to guarantee that a specific sub sample of the population is adequately represented. No such guarantee is possible using a simple random sample. A stratified sample is the approach used when adequate representation from a sub sample is desired. The characteristics of the sub sample (strata or segment) may include almost any variable: age, sex, religion, income level, or even individuals who listen to specific radio stations or read certain magazines. The strata may be defined by an almost unlimited number of characteristics; however, each additional variable or characteristic makes the sub sample more difficult to find. Therefore, incidence drops.
- 26. Stratified sampling ensures that a sample is drawn from a homogeneous subset of the population, that is, from a population with similar characteristics. Homogeneity helps researchers to reduce sampling error. The stratified sampling ensures the proper representation of the stratification variables to enhance representation of other variables related to them. Taken as a whole, then, a stratified sample is likely to be more representative on a number of variables than a simple random sample. Stratified sampling can be applied in two different ways: Proportionate stratified sampling includes strata with sizes based on their proportion in the population. This procedure is designed to give each person in the population an equal chance of being selected. Disproportionate stratified sampling is used to over sample or over represent a particular stratum. The approach is used basically because the stratum is considered important for some reason: marketing, advertising, or other similar reasons.
- 27. Advantages 1. Representativeness of relevant variables is ensured. 2. Comparisons can be made to other populations. 3. Selection is made from a homogeneous group. 4. Sampling error is reduced. Disadvantages 1. Knowledge of the population prior to selection is required. 2. The procedure can be costly and time- consuming. 3. It can be difficult to find a sample if incidence is low. 4. Variables that define strata may not be relevant.
- 28. The usual sampling procedure is to select one unit or subject at a time. But this requires the researcher to have a complete list of the population. In some cases there is no way to obtain such a list. One way to avoid this problem is to select the sample in groups or categories; this procedure is known as cluster sampling. Cluster sampling creates two types of error: in addition to the error involved in defining the initial clusters, errors may arise in selecting from the clusters. To help control such error, it is best to use small areas or clusters, both to decrease the number of elements in each cluster and to maximize the number of clusters selected.
- 29. In many nationwide studies, researchers use a form of cluster sampling called multistage sampling, in which individual households or persons are selected, not groups. Usually demographic quotas are established for a research study, which means that a certain percentage of all respondents must be of a certain sex or age. In this type of study, researchers determine which person in the household should answer the questionnaire by using a form of random numbers table.
- 30. Advantages 1. Only part of the population need to be enumerated. 2. Costs are reduced if clusters are well defined. 3. Estimates of cluster parameters are made and compared to the population. Disadvantages 1. Sampling errors are likely. 2. Clusters may not be representative of the population. 3. Each subject or unit must be assigned to a specific cluster.
- 31. Many qualitative data analysts undertake forms of content analysis. One of the enduring problems of qualitative data analysis is the reduction of copious amounts of written data to manageable and comprehensible proportions. Data reduction is a key element of qualitative analysis, performed in a way that attempts to respect the quality of the qualitative data. One common procedure for achieving this is content analysis, a process by which the ‘many words of texts are classified into much fewer categories’.
- 32. The term ‘content analysis’ is often used sloppily(imprecise). In effect, it simply defines the process of summarizing and reporting written data – the main contents of data and their messages. More strictly speaking, it defines a strict and systematic set of procedures for the rigorous analysis, examination and verification of the contents of written data. Or ‘a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use’. Texts are defined as any written communicative materials which are intended to be read, interpreted and understood by people other than the analysts
- 33. Content analysis starts with a sample of texts (the units), defines the units of analysis (e.g. words, sentences) and the categories to be used for analysis, reviews the texts in order to code them and place them into categories, and then counts and logs the occurrences of words, codes and categories. From here statistical analysis and quantitative methods are applied, leading to an interpretation of the results. Put simply, content analysis involves coding, categorizing (creating meaningful categories into which the units of analysis – words, phrases, sentences etc. – can be placed), comparing (categories and making links between them), and concluding – drawing theoretical conclusions from the text.
- 34. Features of the process of content analysis: breaking down text into units of analysis undertaking statistical analysis of the units presenting the analysis in as economical a form as possible. some other important features of content analysis, including, for example, examination of the interconnectedness of units of analysis (categories), the emergent nature of themes and the testing, development and generation of theory.
- 35. Step 1: Define the research questions to be addressed by the content analysis: This will also include what one wants from the texts to be content-analysed. The research questions will be informed by, indeed may be derived from, the theory to be tested. Step 2: Define the population from which units of text are to be sampled: The population here refers not only to people but also, and mainly, to text – the domains of the analysis. For example, is it to be newspapers, programmes, interview transcripts, textbooks, conversations, public domain documents, examination scripts, emails,online conversations and so on?
- 36. Step 3: Define the sample to be included: Here the rules for sampling people can apply equally well to documents. One has to decide whether to opt for a probability or non-probability sample of documents, a stratified sample (and, if so, the kind of strata to be used), random sampling, convenience sampling, domain sampling, cluster sampling, purposive, systematic, time sampling, snowball and so on. Step 4: Define the context of the generation of the document: This will examine, for example: how the material was generated, who was involved; who was present; where the documents come from; how the material was recorded and/or edited; whether the person was willing to, able to, and did tell the truth; whether the data are accurately reported ,whether the data are corroborated; the authenticity and credibility of the documents; the context of the generation of the document; the selection and evaluation of the evidence contained in the document.
- 37. Step 5: Define the units of analysis This can be at very many levels, for example, a word, phrase, sentence, paragraph, whole text, people and themes. Different levels of analysis will raise different issues of reliability, It is assumed that the units of analysis will be classifiable into the same category text with the same or similar meaning in the context of the text itself (semantic validity) although this can be problematic .The description of units of analysis will also include the units of measurement and enumeration. The coding unit defines the smallest element of material that can be analysed, while the contextual unit defines the largest textual unit that may appear in a single category. Sampling units are those units that are included in, or excluded from, an analysis; they are units of selection. Recording/coding units are units that are contained within sampling units and are smaller than sampling units, thereby avoiding the complexity that characterises sampling units; they are units of description. Context units are ‘units of textual matter that set limits on the information to be considered in the description of recording units’; they are units that ‘delineate the scope of information that codersneed to consult in characterising the recording units’.
- 38. Step 6: Decide the codes to be used in the analysis Codes can be at different levels of specificity and generality when defining content and concepts. There may be some codes which subsume(to place under another as belonging to it) others, thereby creating a hierarchy of subsumption – subordination and superordination – in effect creating a tree diagram of codes. Some codes are very general; others are more specific. Codes are astringent, pulling together a wealth of material into some order and structure. They keep words as words; they maintain context specificity.
- 39. Step 7: Construct the categories for analysis : Categories are the main groupings of constructs or key features of the text, showing links between units of analysis. For example, a text concerning teacher stress could have groupings such as ‘causes of teacher stress’, ‘the nature of teacher stress’, ‘ways of coping with stress’ and ‘the effects of stress’. Categories are inferred by the researcher, whereas specific words or units of analysis are less inferential; the more one moves towards inference, the more reliability may be compromised, and the more the researcher’s agenda may impose itself on the data.
- 40. Step 8: Conduct the coding and categorizing of the data: Once the codes and categories have been decided, the analysis can be undertaken. This concerns the actual ascription of codes and categories to the text. Step 9: Conduct the data analysis: Once the data have been coded and categorized, the researcher can count the frequency of each code or word in the text, and the number of words in each category. This is the process of retrieval, which may be in multiple modes, for example words, codes, nodes and categories. Some words may be in more than one category, for example where one category is an overarching category and another is a subcategory.
- 41. Step 10: Summarizing : By this stage the investigator will be in a position to write a summary of the main features of the situation that have been researched so far. The summary will identify key factors, key issues, key concepts and key areas for subsequent investigation. It is a watershed stage during the data collection, as it pinpoints major themes, issues and problems that have arisen, so far, from the data (responsively) and suggests avenues for further investigation. Step 11: Making speculative inferences : This is an important stage, for it moves the research from description to inference. It requires the researcher, on the basis of the evidence, to posit some explanations for the situation, some key elements and possibly even their causes. It is the process of hypothesis generation or the setting of working hypotheses that feeds into theory generation.
- 42. Research designs are either experimental or non- experimental. Experimental research is conducted mostly in laboratories in the context of basic research. The principle advantage of experimental designs is that it provides the opportunity to identify cause-and-effect relationships. Non-experimental research, e.g., case studies, surveys, correlation studies, is non-manipulative observational research usually conducted in natural settings. While laboratory-controlled experimental studies tend to be higher in internal validity, non-experimental studies tend to be higher in external validity.
- 43. One major limitation of experimental research is that studies are typically conducted in contrived or artificial laboratory settings. Results may not generalize or extrapolate to external settings. Two exceptions to this rule are natural experiments and field experiments. Natural experiments document and compare the behaviors of subjects before and after some natural event; e.g., floods, tornadoes, hurricanes. Field experiments involve manipulating conditions in the natural setting for the purpose of determining their influence on behavior. The field experiment is unique insofar as it tends to be moderately high on both external and internal validity.
- 44. In experimental research, the investigator manipulates conditions for the purpose of determining their effect on behavior. Subjects should be unaware of their membership in an experimental group so that they don’t act differently. In the simplest experimental design, investigators administer a placebo to the control group and a treatment to the experimental group. Experimental designs vary in terms of subjects’ assignments to different groups, whether subjects were pre-tested, whether different treatments were administered to different groups, and the number of variables being investigated.
- 45. Experiments are typically structured in terms of independent, organism, and dependent variables. The independent variable is a manipulated environmental stimulus dimension, the organism-variable is some dimension (e.g., sex, race) of more or less stable characteristics of the organism, and the dependent variable is a behavioral dimension that reflects the influence of the independent and organism-variables. The general objective in experimental research is to define the relationship between the antecedent (independent and organism) variables and the consequent (dependent) variables.
- 46. Experimental Research is often used where: There is time priority in a causal relationship (cause precedes effect) There is consistency in a causal relationship (a cause will always lead to the same effect) The magnitude of the correlation is great.
- 47. Identify and define the problem. Formulate hypotheses and deduce their consequences. Construct an experimental design that represents all the elements, conditions, and relations of the consequences. 1. Select sample of subjects. 2. Group or pair subjects. 3. Identify and control non experimental factors. 4. Select or construct, and validate instruments to measure outcomes. 5. Conduct pilot study. 6. Determine place, time, and duration of the experiment. Conduct the experiment. Compile raw data and reduce to usable form. Apply an appropriate test of significance.
- 48. Manipulation of an independent variable. An attempt is made to hold all other variables except the dependent variable constant - control. Effect is observed of the manipulation of the independent variable on the dependent variable - observation. Experimental control attempts to predict events that will occur in the experimental setting by neutralizing the effects of other factors. Methods of Experimental Control Physical Control Gives all subjects equal exposure to the independent variable. Controls non experimental variables that affect the dependent variable. Selective Control - Manipulate indirectly by selecting in or out variables that cannot be controlled. Statistical Control - Variables not conducive to physical or selective manipulation may be controlled by statistical techniques (example: covariance).
- 49. Experimental Design - A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted. Validity of Experimental Design: Internal Validity asks did the experimental treatment make the difference in this specific instance rather than other extraneous variables? External Validity asks to what populations, settings, treatment variables, and measurement variables can this observed effect be generalized?
- 50. History - The events occurring between the first and second measurements in addition to the experimental variable which might affect the measurement. Example: Researcher collects gross sales data before and after a 5 day 50% off sale. During the sale a hurricane occurs and results of the study may be affected because of the hurricane, not the sale. Maturation - The process of maturing which takes place in the individual during the duration of the experiment which is not a result of specific events but of simply growing older, growing more tired, or similar changes. Example: Subjects become tired after completing a training session, and their responses on the Posttest are affected.
- 51. Pre-testing - The effect created on the second measurement by having a measurement before the experiment. Example: Subjects take a Pretest and think about some of the items. On the Posttest they change to answers they feel are more acceptable. Experimental group learns from the pretest. Measuring Instruments - Changes in instruments, calibration of instruments, observers, or scorers may cause changes in the measurements. Example: Interviewers are very careful with their first two or three interviews but on the 4th, 5th, 6th become fatigued and are less careful and make errors. Statistical Regression - Groups are chosen because of extreme scores of measurements; those scores or measurements tend to move toward the mean with repeated measurements even without an experimental variable. Example: Managers who are performing poorly are selected for training. Their average Posttest scores will be higher than their Pretest scores because of statistical regression, even if no training were given.
- 52. Differential Selection - Different individuals or groups would have different previous knowledge or ability which would affect the final measurement if not taken into account. Example: A group of subjects who have viewed a TV program is compared with a group which has not. There is no way of knowing that the groups would have been equivalent since they were not randomly assigned to view the TV program. Experimental Mortality - The loss of subjects from comparison groups could greatly affect the comparisons because of unique characteristics of those subjects. Groups to be compared need to be the same after as before the experiment. Example: Over a 6 month experiment aimed to change accounting practices, 12 accountants drop out of the experimental group and none drop out of the control group. Not only is there differential loss in the two groups, but the 12 dropouts may be very different from those who remained in the experimental group. Interaction of Factors, such as Selection Maturation, etc. - Combinations of these factors may interact especially in multiple group comparisons to produce erroneous measurements.
- 53. Pre-Testing -Individuals who were pretested might be less or more sensitive to the experimental variable or might have "learned" from the pre-test making them unrepresentative of the population who had not been pre-tested. Example: Prior to viewing a film on Environmental Effects of Chemical, a group of subjects is given a 60 item antichemical test. Taking the Pretest may increase the effect of the film. The film may not be effective for a nonpretested group. Differential Selection - The selection of the subjects determines how the findings can be generalized. Subjects selected from a small group or one with particular characteristics would limit generalizability. Randomly chosen subjects from the entire population could be generalized to the entire population. Example: Researcher, requesting permission to conduct experiment, is turned down by 11 corporations, but the 12th corporation grant permission. The 12th corporation is obviously different then the others because they accepted. Thus subjects in the 12th corporation may be more accepting or sensitive to the treatment.
- 54. Experimental Procedures - The experimental procedures and arrangements have a certain amount of effect on the subjects in the experimental settings. Generalization to persons not in the experimental setting may be precluded. Example: Department heads realize they are being studied, try to guess what the experimenter wants and respond accordingly rather than respond to the treatment. Multiple Treatment Interference - If the subjects are exposed to more than one treatment then the findings could only be generalized to individuals exposed to the same treatments in the same order of presentation. Example: A group of CPA’s is given training in working with managers followed by training in working with comptrollers. Since training effects cannot be deleted, the first training will affect the second.
- 55. Pre-Test - The pre-test, or measurement before the experiment begins, can aid control for differential selection by determining the presence or knowledge of the experimental variable before the experiment begins. It can aid control of experimental mortality because the subjects can be removed from the entire comparison by removing their pre-tests. However, pre-tests cause problems by their effect on the second measurement and by causing generalizability problems to a population not pre-tested and those with no experimental arrangements.
- 56. Control Group -The use of a matched or similar group which is not exposed to the experimental variable can help reduce the effect of History, Maturation, Instrumentation, and Interaction of Factors. The control group is exposed to all conditions of the experiment except the experimental variable. Randomization - Use of random selection procedures for subjects can aid in control of Statistical Regression, Differential Selection, and the Interaction of Factors. It greatly increases generalizability by helping make the groups representative of the populations. Additional Groups - The effects of Pre-tests and Experimental Procedures can be partially controlled through the use of groups which were not pre-tested or exposed to experimental arrangements. They would have to be used in conjunction with other pre-tested groups or other factors jeopardizing validity would be present.
- 57. In an experiment, the independent variable is the variable that is varied or manipulated by the researcher, and the dependent variable is the response that is measured. An independent variable is the presumed cause, whereas the dependent variable is the presumed effect. The IV is the antecedent, whereas the DV is the consequent. In experiments, the IV is the variable that is controlled and manipulated by the experimenter; whereas the DV is not manipulated, instead the DV is observed or measured for variation as a presumed result of the variation in the IV.
- 58. "In nonexperimental research, where there is no experimental manipulation, the IV is the variable that 'logically' has some effect on a DV. For example, in the research on cigarette-smoking and lung cancer, cigarette-smoking, which has already been done by many subjects, is the independent variable.” When reseaerchers are not able to actually control and manipulate an IV, it is technically referred to as a status variable (e.g., gender, ethnicity, etc.). Even though researchers do not actually control or manipulate status variables, researchers can, and often do, treat them as IVs. The DV refers to the status of the 'effect'(or outcome) in which the researcher is interested; the independent variable refers to the status of the presumed 'cause,' changes in which lead to changes in the status of the dependent variable…any event or condition can be conceptualized as either an independent or a dependent variable. For example, it has been observed that rumor-mongering can sometimes cause a riot to erupt, but it has also been observed that riots can cause rumors to surface. Rumors are variables that can be conceived of as causes (IVs) and as effects (DVs).”
- 59. Some Examples of Independent and Dependent Variables The following is a hypothesis for a study. 1. "There will be a statistically significant difference in graduation rates of at-risk high-school seniors who participate in an intensive study program as opposed to at-risk high-school seniors who do not participate in the intensive study program.” IV: Participation in intensive study program. DV: Graduation rates. The following is a description of a study. 2. "A director of residential living on a large university campus is concerned about the large turnover rate in resident assistants. In recent years many resident assistants have left their positions before completing even 1 year in their assignments. The director wants to identify the factors that predict commitment as a resident assistant (defined as continuing in the position a minimum of 2 years). The director decides to assess knowledge of the position, attitude toward residential policies, and ability to handle conflicts as predictors for commitment to the position.” IV: knowledge of position, attitude toward policies, and ability to handle conflicts. DV: commitment to position (continuing in position for 2 years or not continuing).