Free AIOU Solved Assignment Code 8604 Spring 2023
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Course: “Research Methods in Education” (8604)
Semester: Spring, 2023
ASSIGNMENT No. 1
- 1 What are the sources of knowledge? Define scientific method and describe its different steps.
Sociologists make use of tried and true methods of research, such as experiments, surveys, and field research. But humans and their social interactions are so diverse that these interactions can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving ideas right or wrong rather than about exploring the nuances of human behavior.
However, this is exactly why scientific models work for studying human behavior. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results.
The scientific method involves developing and testing theories about the world based on empirical evidence. It is defined by its commitment to systematic observation of the empirical world and strives to be objective, critical, skeptical, and logical. It involves a series of prescribed steps that have been established over centuries of scholarship.
The scientific method is an essential tool in research.
But just because sociological studies use scientific methods does not make the results less human. Sociological topics are not reduced to right or wrong facts. In this field, results of studies tend to provide people with access to knowledge they did not have before—knowledge of other cultures, knowledge of rituals and beliefs, or knowledge of trends and attitudes. No matter what research approach they use, researchers want to maximize the study’s reliability, which refers to how likely research results are to be replicated if the study is reproduced. Reliability increases the likelihood that what happens to one person will happen to all people in a group. Researchers also strive for validity, which refers to how well the study measures what it was designed to measure. Returning to the crime rate during a full moon topic, reliability of a study would reflect how well the resulting experience represents the average adult crime rate during a full moon. Validity would ensure that the study’s design accurately examined what it was designed to study, so an exploration of adult criminal behaviors during a full moon should address that issue and not veer into other age groups’ crimes, for example.
In general, sociologists tackle questions about the role of social characteristics in outcomes. For example, how do different communities fare in terms of psychological well-being, community cohesiveness, range of vocation, wealth, crime rates, and so on? Are communities functioning smoothly? Sociologists look between the cracks to discover obstacles to meeting basic human needs. They might study environmental influences and patterns of behavior that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on negative behaviors or challenging situations, researchers might study vacation trends, healthy eating habits, neighborhood organizations, higher education patterns, games, parks, and exercise habits.
Sociologists can use the scientific method not only to collect but also to interpret and analyze the data. They deliberately apply scientific logic and objectivity. They are interested in—but not attached to—the results. They work outside of their own political or social agendas. This doesn’t mean researchers do not have their own personalities, complete with preferences and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in a particular study.
With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. They provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton 1963).
Typically, the scientific method starts with these steps—1) ask a question, 2) research existing sources, and 3) formulate a hypothesis.
The first step of the scientific method is to ask a question, describe a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geography and time frame. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. That said, happiness and hygiene are worthy topics to study. Sociologists do not rule out any topic, but would strive to frame these questions in better research terms.
That is why sociologists are careful to define their terms. In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?” When forming these basic research questions, sociologists develop an operational definition, that is, they define the concept in terms of the physical or concrete steps it takes to objectively measure it. The operational definition identifies an observable condition of the concept. By operationalizing a variable of the concept, all researchers can collect data in a systematic or replicable manner.
The operational definition must be valid, appropriate, and meaningful. And it must be reliable, meaning that results will be close to uniform when tested on more than one person. For example, “good drivers” might be defined in many ways: those who use their turn signals, those who don’t speed, or those who courteously allow others to merge. But these driving behaviors could be interpreted differently by different researchers and could be difficult to measure. Alternatively, “a driver who has never received a traffic violation” is a specific description that will lead researchers to obtain the same information, so it is an effective operational definition.
Research Existing Sources
The next step researchers undertake is to conduct background research through a literature review, which is a review of any existing similar or related studies. A visit to the library and a thorough online search will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted on the topic at hand and enables them to position their own research to build on prior knowledge. Researchers—including student researchers—are responsible for correctly citing existing sources they use in a study or that inform their work. While it is fine to borrow previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized.
To study hygiene and its value in a particular society, a researcher might sort through existing research and unearth studies about child-rearing, vanity, obsessive-compulsive behaviors, and cultural attitudes toward beauty. It’s important to sift through this information and determine what is relevant. Using existing sources educates researchers and helps refine and improve studies’ designs.
Formulate a Hypothesis
A hypothesis is an assumption about how two or more variables are related; it makes a conjectural statement about the relationship between those variables. In sociology, the hypothesis will often predict how one form of human behavior influences another. In research, independent variables are the cause of the change. The dependent variable is the effect, or thing that is changed.
Free AIOU Solved Assignment 1 Code 8604 Spring 2023
2 Describe different types of research categorized on the basis of methods used and the purpose of research.
Research is a logical and systematic search for new and useful information on a particular topic. Research is important both in scientific and nonscientific fields. In our life new problems, events, phenomena and processes occur every day. Practically, implementable solutions and suggestions are required for tackling new problems that arise. Scientists have to undertake research on them and find their causes, solutions, explanations and applications.
The research is broadly classified into two main classes: 1. Fundamental or basic research and 2. Applied research. Basic and applied researches are generally of two kinds: normal research and revolutionary research. In any particular field, normal research is performed in accordance with a set of rules, concepts and procedures called a paradigm, which is well accepted by the scientists working in that field. In addition, the basic and applied researches can be quantitative or qualitative or even both (mixed research).
- Fundamental or basic research:
Basic research is an investigation on basic principles and reasons for occurrence of a particular event or process or phenomenon. It is also called theoretical research. Study or investigation of some natural phenomenon or relating to pure science are termed as basic research. Basic researches sometimes may not lead to immediate use or application. It is not concerned with solving any practical problems of immediate interest. But it is original or basic in character. It provides a systematic and deep insight into a problem and facilitates extraction of scientific and logical explanation and conclusion on it. It helps build new frontiers of knowledge. The outcomes of basic research form the basis for many applied research.
- Seeks generalization
- Aims at basic processes
- Attempts to explain why things happen
- Tries to get all the facts
- Reports in technical language of the topic
- Applied research:
In an applied research one solves certain problems employing well known and accepted theories and principles. Most of the experimental research, case studies and inter-disciplinary research are essentially applied research. Applied research is helpful for basic research. A research, the outcome of which has immediate application is also termed as applied research. Such a research is of practical use to current activity.
- Studies individual or specific cases without the objective to generalize
- Aims at any variable which makes the desired difference
- Tries to say how things can be changed
- Tries to correct the facts which are problematic
- Reports in common language
Basic and applied research, further divided into three types of research bearing some characteristics feature as follows:
- It is numerical, non-descriptive, applies statistics or mathematics and uses numbers.
- It is an iterative process whereby evidence is evaluated.
- The results are often presented in tables and graphs.
- It is conclusive.
- It investigates the what, where and when of decision making.
- It is non-numerical, descriptive, applies reasoning and uses words.
- Its aim is to get the meaning, feeling and describe the situation.
- Qualitative data cannot be graphed.
- It is exploratory.
- It investigates the why and how of decision making.
Mixed research- research that involves the mixing of quantitative and qualitative methods or paradigm characteristics. Nature of data is mixture of variables, words and images.
Other types of research
Exploratory research might involve a literature search or conducting focus group interviews. The exploration of new phenomena in this way may help the researcher’s need for better understanding, may test the feasibility of a more extensive study, or determine the best methods to be used in a subsequent study. For these reasons, exploratory research is broad in focus and rarely provides definite answers to specific research issues.
The objective of exploratory research is to identify key issues and key variables.
The descriptive research is directed toward studying “what” and how many off this “what”. Thus, it is directed toward answering questions such as, “What is this?”.
- Its primary goal is to understand or to explain relationships.
- It uses correlations to study relationships between dimensions or characteristics off individuals, groups, situations, or events.
- Explanatory research explains (How the parts of a phenomenon are related to each other).
- Explanatory research asks the “Why” question.
Research carried out longitudinally involves data collection at multiple points in time. Longitudinal studies may take the form of:
- Trend study- looks at population characteristics over time, e.g. organizational absenteeism rates during the course of a year
- Cohort study- traces a sub-population over time, e.g. absenteeism rates for the sales department;
- Panel study- traces the same sample over time, e.g. graduate career tracks over the period 1990 – 2000 for the same starting cohort.
While longitudinal studies will often be more time consuming and expensive than cross-sectional studies, they are more likely to identify causal relationships between variables.
One-shot or cross-sectional studies are those in which data is gathered once, during a period of days, weeks or months. Many cross-sectional studies are exploratory or descriptive in purpose. They are designed to look at how things are now, without any sense of whether there is a history or trend at work.
- Fact findings to improve the quality of action in the social world
- Reports employing this type of research focus on the question ‘How can problem ‘X’ be solved or prevented ?’
- It aims at categorization of units in to groups
- To demonstrate differences
- To explain relationships
- To identify similarities and differences between units at all levels
- It aims at establishing cause and effect relationship among variable
- It aims at testing validity of a unit
- To establish and formulate the theory
Last of all, it is needless to say that scientific research helps us in many ways:
- A research problem refers to a difficulty which a researcher or a scientific community or an industry or a government organization or a society experiences. It may be a theoretical or a practical situation. It calls for a thorough understanding and possible solution.
- Research provides basis for many government policies. For example, research on the needs and desires of the people and on the availability of revenues to meet the needs helps a government to prepare a budget.
- It is the fountain of knowledge and provide guidelines for solving problems.
- Only through research inventions can be made; for example, new and novel phenomena and processes such as superconductivity and cloning have been discovered only through research.
- It is important in industry and business for higher gain and productivity and to improve the quality of products.
- Research leads to a new style of life and makes it delightful and glorious.
- It leads to the identification and characterization of new materials, new living things, new stars, etc.
- Mathematical and logical research on business and industry optimizes the problems in them.
- Social research helps find answers to social problems. They explain social phenomena and seek solution to social problems.
Free AIOU Solved Assignment 2 Code 8604 Spring 2023
3 Define casual comparative (Ex-Post Factor) research and discuss it in detail with example.
Causal-comparative research is an attempt to identify a causative relationship between an independent variable and a dependent variable.The relationship between the independent variable and dependent variable is usually a suggested relationship (not proven) because you (the researcher) do not have complete control over the independent variable.
The Causal Comparative method seeks to establish causal relationships between events and circumstances. In other words, it finds out the causes of certain occurrences or non-occurrenceces. This is achieved by comparing the circumstances associated with observed effects and by noting the factors present in the instances where a given effect occurs and where it does not occur. This method is based on Miill’s canon of agreement and disaggrement which states that caoses of given observed effect may be ascertained by noting elements which are invariably present when the result is present and which are invariably absent when the result is absent.
Causal-comparative research scrutinizes the relationship among variables in studies in which the independent variable has already occurred, thus making the study descriptive rather than experimental in nature. Because the independent variable (the variable for which the researcher wants to suggest causation) has already been completed (e.g., two reading methods used by a school ), the researcher has no control over it. That is, the researcher cannot assign subjects or teachers or determine the means of implementation or even verify proper implementation.
Sometimes the variable either cannot be manipulated (e.g., gender) or should not be manipulated (e.g., who smokes cigarettes or how many they smoke). Still, the relationship of the independent variable on one or more dependent variables is measured and implications of possible causation are used to draw conclusions about the results.
Also known as “ex post facto” research. (Latin for “after the fact”) since both the effect and the alleged cause have already occurred and must be studied in retrospect .In this type of research investigators attempt to determine the cause or consequences of differences that already exist between or among groups of individuals.
Used, particularly in the behavioral sciences. In education, because it is impossible, impracticable, or unthinkable to manipulate such variables as aptitude, intelligence, personality traits, cultural deprivation, teacher competence, and some variables that might present an unacceptable threat to human beings, this method will continue to be used.
Causal-Comparative Research Facts
- Causal-Comparative Research is not manipulated by the researcher.
- -Does not establish cause-effect relationships.
- -Generally includes more than two groups and at least one dependent variable.
- -Independent variable is causal-comparative studies is often referred to as the grouping variable.
- -The independent variable has occurred or is already formed.
The Nature of Causal-Comparative Research
A common design in educational research studies, Causal-comparative research, seeks to identify associations among variables. Relationships can be identified in causal-comparative study, but causation cannot be fully established.
Attempts to determine cause and effect. It is not as powerful as experimental designs Causal-comparative research attempts to determine the cause or consequences of differences that already exist between or among groups of individuals.
Alleged cause and effect have already occurred and are being examined after the fact. The basic causal-comparative approach is to begin with a noted difference between two groups and then to look for possible causes for, or consequences of, this difference.
Used when independent variables cannot or should not be examined using controlled experiments. When an experiment would take a considerable length of time and be quite costly to conduct, a causal-comparative study is sometimes used as an alternative.
Main purpose of causal-comparative research:
- Exploration of Effects
- Exploration of Causes
- Exploration of Consequences
Basic Characteristics of Causal-comparative research
In short it the basic Characteristics of Causal-comparative research can be concluded:
- –Causal comparative research attempts to determine reasons, or causes, for the existing condition
- Causal comparative studies are also called ex post facto because the investigator has no control over the exogenous variable. Whatever happened occurred before the researcher arrived.
- -Causal-comparative research is sometimes treated as a type of descriptive research since it describes conditions that already exist.
- -Causal-comparative studies attempt to identify cause-effect relationships; correlational studies do not
- -Causal-comparative studies involve comparison, correlational studies involve relationship.
- -Causal-comparative studies typically involve two (or more) groups and one independent variable, whereas correlational studies typically involve two or more variables and one group
- -Causal-comparative studies typically involve two (or more) groups and one independent variable, whereas correlational studies typically involve two or more variables and one group
- -In causal-comparative the researcher attempts to determine the cause, or reason, for preexisting differences in groups of individual.
- Involves comparison of two or more groups on a single endogenous variables.
- -Retrospective causal-comparative studies are far more common in educational research
- -The basic approach is sometimes referred to as retrospective causal-comparative research (since it starts with effects and investigates causes)
- -The basic approach is sometimes referred to as retrospective causal-comparative research (since it starts with effects and investigates causes)
- -The basic causal-comparative approach involves starting with an effect and seeking possible causes.
- The characteristic that differentiates these groups is the exogenous variable.
- -The variation as prospective causal-comparative research (since it starts with causes and investigates effects)
- We can never know with certainty that the two groups were exactly equal before the difference occurred.
Three important aspects of Causal Comparative method are:
1- Gathering of data on factors invariably present in cases where the given result occurs and discarding of those elements which are not universally present
2- 2-Gathering the data on factors invariably present in cases where the given effect does not occur
3- 3 Comparing the two sets of data, or in effect, substracting one from the other to get at the causes responsible for the occurance or otherwise of the effect.
Examples of variables investigated in Causal-Comparative Research
- -Ability variables (achievement)
- -Family-related variables (SES)
- -Organismic variables (age, ethnicity, sex)
- -Personality variables (self-concept)
- -School related variables (type of school, size of school)
Causal Comparative Research Procedure
Experimental, quasi-experimental, and causal-comparative research methods are frequently studied together because they all try to show cause and effect relationships among two or more variables. To conduct cause and effect research, one variable(s) is considered the causal or independent variable and
Causal comparative research attempts to attribute a change in the effect variable(s) when the causal variable(s) cannot be manipulated.
For example: if you wanted to study the effect of socioeconomic variables such as sex, race, ethnicity, or income on academic achievement, you might identify two existing groups of students: one group – high achievers; second group – low achievers. You then would study the differences of the two groups as related to socioeconomic variables that already occurred or exist as the reason for the difference in the achievement between the two groups. To establish a cause effect relationship in this type of research you have to build a strongly persuasive logical argument. Because it deals with variables that have already occurred or exist, causal-comparative research is also referred to as ex post facto research.
The most common statistical techniques used in causal comparative research are analysis of variance and t-tests wherein significant differences in the means of some measure (i.e. achievement) are compared between or among two or more groups.
- Raw scores such as test scores
- Measures such as grade point averages
- Judgements, and other assessments made of the subjects involved
- Standardized tests
- Structured interviews
- The most important procedural consideration in doing causal comparative research is to identify two or more groups which are demonstrably different in an educationally important way such as high academic achievement versus low academic achievement. An attempt is then made to identify the cause which resulted in the differences in the effect (i.e. academic achievement). The cause (i.e. race, sex, income, etc.) has already had its effect and cannot be manipulated, changed or altered. In selecting subjects for causal- comparative research, it is most important that they be identical as possible except for the difference (i.e. independent variable – race, sex, income) which may have caused the demonstrated effect (i.e. dependent variable – academic achievement)
- Hypotheses are generally used
- Statistics are extensively used in experimental research and include measures of spread or dispersion such as:
- analysis of variance as well as measures of relationship such as
- : Pearson Product-Moment Coefficient;
- Spearman Rank Order Coefficient; Phi Correlation Coefficient; regression
AIOU Solved Assignment Code 8604 Spring 2023
4 What is an experiment and how you will conduct and experimental research? What will be the threats to internal and external validity and how you will minimize these threats?
The design of research is fraught with complicated and crucial decisions. Researchers must decide which research questions to address, which theoretical perspective will guide the research, how to measure key constructs reliably and accurately, who or what to sample and observe, how many people/places/things need to be sampled in order to achieve adequate statistical power, and which data analytic techniques will be employed. These issues are germane to research of all types (exploratory, explanatory, descriptive, evaluation research). However, the term “research design” typically does not refer to the issues discussed above.
The term “experimental research design” is centrally concerned with constructing research that is high in causal (or internal) validity. Causal validity concerns the accuracy of statements regarding cause and effect relationships. For example, does variable 1 cause variation in variable 2? Or does variable 2 cause variation in variable 1? Or does variable 3 cause variation in both variables 1 and 2? And what is the magnitude of the causal relationships among the variables? Thus, research design as used herein is a concern of explanatory and evaluation research but generally does not apply to exploratory or descriptive research.
The importance of making causal inferences in criminology is hard to overstate. A central issue in many criminological debates concerns whether correlates of offending are causally related to offending. The correlates of offending are well known: bad parenting, deviant friends, prior delinquency behavior, youthful age (i.e., adolescents and young adults), being male, deviant attitudes, personality traits such as impulsivity and psychopathy, and so forth. Criminologists largely agree on these correlates of offending. Yet, “correlation does not imply causation.” The field of criminology is filled with debates about which of these relationships are causal in nature. Perhaps the best known of these debates focuses on association between deviant peers on offending. Social learning theorists assert that having numerous, close relationships with those involved in deviance causes one’s own level of deviance to increase. On the other hand, social control theorists argue that this relationship is noncausal; instead, the positive relationship between having deviant peers is the result of “homophily” (the tendency of individuals to associate with similar others) – “birds of a feather flock together.” Likewise, there is disagreement over whether the relationship between prior offending and future offending is causal. Theorists such as Gottfredson and Hirschi (1990) argue that this relationship is spurious, as both prior and future offending are caused by low self‐control. Other theories, such as Sampson and Laub’s (1993) age‐graded theory of informal social control, assert that involvement in crime and contact with the criminal justice system increase the likelihood of future offending because these experiences diminish bonding to important sources of informal social control (e.g., marriage, employment).
Debates concerning causal inference are not confined to theory. The effectiveness, or causal effect, of many criminal justice – based interventions on measures of offending are hotly debated. Evaluations of criminal justice interventions (e.g., reentry programs, drug court, and domestic violence programs) often find that program participants have less recidivism than nonparticipants. Yet, most evaluations have difficulty proving that the observed differences were actually caused by program participation.
Simply put, research design is a central concern in criminology because carefully designed research that is implemented with high fidelity can establish causal validity/causal inferences.
Criteria for Establishing Causal Inferences
The three classic criteria necessary to support a causal inference, according to the philosopher John Stuart Mill, are: (1) association (correlation), (2) temporal order, and (3) nonspuriousness. The criterion of association requires that there is a systematic relationship between the cause and effect variables. This criterion is by far the easiest to determine. The second criterion of temporal order is a bit more complicated. The temporal order criterion requires that the cause, or more precisely variation in the cause variable, must occur before the observed variation in the effect variable. The third criterion of nonspuriousness is by far the most difficult to achieve. This criterion requires that the observed relationship between the cause and the effect variables must not be due to other omitted or unmeasured third variables. Using the relationship between delinquent peers and offending as an example, this criterion requires that this relationship cannot be due to homophily or any other potential explanation. Because there are usually many, many potentially relevant third variables and many of these third variables are unobserved, the criterion of nonspuriousness can be quite difficult to achieve.
Types of Experiments
Shadish, Cook, and Campbell (2002) define an experiment as “a study in which an intervention is deliberately introduced to observe its effects” (p. 12). Shadish and colleagues distinguish two broad types of experiments: randomized experiments and quasi experiments. The central difference between these two types of techniques is the use of random assignment to the levels of the hypothesized cause variable.
The hallmark of all randomized experiments is the use of random assignment to experimental conditions. In randomized experiments, research subjects are randomly assigned to different levels of the hypothesized cause variable (i.e., experimental conditions) by the researchers. Random assignment can be achieved in many different ways, such as by flipping a coin, using a table of random numbers, or using numbers randomly generated by a computer. The method of randomization is largely arbitrary, but the use of some form of randomization is the crucial element of a randomized experiment.
Randomized experiments come in many forms or designs. The most common form of a randomized experiment involves randomly assigning research subjects, all of whom have been screened for eligibility, to either the treatment group that receives the experimental intervention of interest or the control group that does not; the control group, instead, typically receives no treatment, standard care, or a placebo. Randomized experiments involving the use of a no‐treatment control group are often referred to as “randomized controlled trials.” Randomized controlled trials are considered by many to be the gold standard of evaluation research for their high causal validity. There are many variations on this basic design. One common variation involves multiple treatment groups that receive varying doses of the experimental intervention. Another common variation involves “blinding” – procedures designed to prevent research subjects, treatment providers, and/or researchers from knowing which experimental condition a research subject was assigned. Double‐blind randomized control trials typically attempt to prevent research subjects and researchers from learning which research subjects were assigned to the control group, until after all data have been collected. Blinding is intended to prevent various kinds of bias from contaminating the research results. Randomized experiments are increasingly common in criminology (see, e.g., Farrington & Welsh, 2005), but double‐blind randomized experiments are extremely rare.
Quasi experiments do not use randomization to assign research subjects to experimental conditions; instead, some other method of assignment is utilized. Often research subjects voluntarily choose to participate or not to participate in the treatment of interest. Thus, the actions and wishes of the research subjects typically affect assignment.
Quasi experiments utilize a wide variety of designs. The two most common involve one‐group and two‐group designs. The simplest and least rigorous quasi‐experimental research design involves one group of research subjects who participated in some treatment of interest. These research subjects are observed before and after the administration of treatment of interest. And the observed changes in the outcome of interest are causally attributed to participation in the treatment. Another widely used quasi‐experimental design involves the use of two groups. Typically, two‐group quasi experiments involve a comparison group that does not receive the treatment of interest and a treatment group that does receive the treatment. These groups are compared, often while controlling for any observed differences, and the remaining differences are causally attributed to the treatment.
Randomized experiments and quasi experiments are capable of clearly establishing the first two criteria for causal inferences (association and temporal order); yet, they differ sharply in their ability to establish nonspuriousness. Randomized experiments are able to convincing establish nonspuriousness because of their use of random assignment. Random assignment ensures that research subjects will be equal in expectation on all variables – both observed and unobserved variables – prior to the administration of the experimental intervention. The phrase “equal in expectation” does not mean that the research subjects assigned to each of the experimental conditions will be perfectly equal on all variables. Instead, equal in expectation means that if we could repeat this assignment process an infinite number of times, the population means on all variables would be equal for each of the experimental conditions. Therefore, any differences between research subjects assigned to the various experimental conditions are due to chance. Because there are no systematic differences between the experimental groups on any variable besides the experimental condition, randomized experiments are able to rule out all potential third variables as alternative explanations for the observed differences on the outcome variable(s) of interest.
Quasi experiments have much greater difficulty in establishing nonspuriousness. In quasi experiments the actions and/or wishes of those involved in the research affect which experimental condition they eventually receive. This is highly problematic, as research subjects who choose to participate in a particular level of the experimental condition often differ from other research subjects on observed and/or unobserved variables. If research subjects in various levels of the experimental condition (e.g., program participants vs. nonparticipants) differ only on observed variables, then it would be easy to control for these observed differences by using statistical techniques such as multiple regression. However, in the absence of random assignment, how does one establish convincingly that participants and nonparticipants differ only on observed variables? It stands to reason that if the groups differ on observed variables, then they also differ on unobserved variables as well. Further, even if participants and nonparticipants are equal on observed variables, this does not mean that these groups are also equal on important unobserved variables. This is the crucial issue, because it is these unobserved differences that cause selection bias. Selection bias refers to inaccuracies in the estimated relationship between variables that is caused by omitted or unmeasured variables. Because quasi experiments do not establish that research subjects are equal in expectation on all variables, especially unobserved variables, prior to the administration of the experimental intervention, selection bias is a persistent problem in quasi‐experimental research designs .
In the language of research methods, in randomized experiments, the assignment of research subjects to experimental conditions is exogenous . Exogenous in this context means outside or external to everyone involved in the experiment including the research subjects, treatment providers, and researchers – only randomization affects experimental assignment. Research subjects have no influence on which level of the experimental condition they will be assigned. However, in quasi experiments, the actions and wishes of those involved in the research including research subjects, their families, treatment providers, criminal justice officials, and researchers among many others may affect assignment; and therefore, assignment in quasi experiments is endogenous – meaning that the assignment process is affected by factors internal to the experiment. Endogeneity is highly problematic because accurate estimation of causal relationships requires the cause variable to be at least partially exogenous.
Threats to Causal Validity
The use of randomized experimental research designs ensures that the research subjects in each of the experimental conditions are equal in expectation before the administration of the experimental treatment. However, the use of randomized experimental designs does not ensure that the experiment will remain bias‐free after randomization. Randomized experiments must be carefully planned and implemented to avoid various biases affecting their results postrandomization. In particular, there are three primary threats (i.e., sources of bias) that must be guarded against for randomized experiments to achieve high levels of causal validity. The first potential threat is contamination . Contamination occurs in situations where research subjects assigned to different levels of the experiment (e.g., participants and nonparticipants) come into direct contact or interact in other ways. Contamination occurs when nonparticipants end up receiving the treatment via interactions with participants. For example, if nonparticipants learn ideas/techniques discussed in the experimental treatment, then this knowledge may attenuate the size of the treatment effect because in essence nonparticipants received some of the experiment treatment vicariously. Cross‐overs are a second potential threat to the causal validity of randomized experimental designs. Cross‐overs refers to research subjects assigned to one condition who end up in some other experimental condition. For example, if some nonparticipants end up receiving the treatment because of an error or deliberate actions, then these individuals have “crossed‐over.” Cross‐overs, particularly as their numbers rise, may attenuate the magnitude of the treatment effect and thereby negatively affect the experiment’s causal validity. The third potential threat to randomized experimental research designs is attrition . Attrition is the loss of research subjects due to factors such as being unable to locate the subjects for follow‐up interviews/assessments, subjects declining to participate, death of research subjects, and so forth. Attrition becomes an increasingly potent problem as the length of the tracking period grows. Attrition is problematic in two ways. First, general attrition (i.e., attrition across experimental conditions) undermines external validity, the ability to generalize research findings beyond the sample. Second and more problematic in terms of causal validity is differential attrition (i.e., attrition rates differ markedly between experimental conditions), as differential attrition has the potential to undo the equating of groups accomplished via random assignment.
Quasi experiments face a host of issues that threaten the causal validity of findings derived from these designs. The particular threats depend on the specific design features of the quasi experiment. Quasi experiments using one‐group designs face the most serious threats to the causal validity of their findings. These threats include maturation (i.e., changes due to aging), regression to the mean (i.e., the tendency of research subjects who scored unusually high and low scores in initial assessments to regress toward less extreme scores in later assessments), testing (i.e., the tendency of research subjects to respond differently in later assessments because they have been sensitized to the behaviors under investigation), and “history” (i.e., external events, besides the intervention, that cause changes in the behaviors under investigation). All of these threats are competing explanations for the results obtained from one‐group quasi‐experimental research designs. Given the number of threats challenging the causal validity of one‐group designs, these designs are the weakest type of experiments.
Two‐group quasi‐experimental designs generally have fewer and different primary threats to their causal validity in comparison to one‐group designs. Briefly, two‐group quasi‐experimental designs have all of the same threats as randomized experiments and the additional threat of selection bias. As discussed above, the nonrandom assignment of research subjects to experimental conditions leaves open the possibility that research subjects assigned to various levels of the experimental condition differed on observed and unobserved variables before the administration of the experimental treatment. As a result, the variable capturing treatment assignment is potentially endogenous, which is highly problematic because accurate estimation of the treatment effect requires the treatment assignment variable to be exogenous.
Exogeneity in Nonrandomized Experiments
The use of randomized experiments is not the only means of achieving exogeneity. There are several other research designs/research methods of achieving at least partial exogeneity. These designs/methods are more frequently utilized in fields outside of criminology and are making inroads in criminology. These quasi‐experimental designs/methods include natural experiments, regression discontinuity, and instrumental variable estimation. These techniques allow researchers to accurately estimate causal relationships and draw causal inferences in certain situations.
Natural experiments are one means of establishing exogenous variation in a cause variable when researcher‐led random assignment is not feasible. A natural experiment is study in which external factors such as natural events, serendipity, or policy changes “assign” research subjects to various experimental conditions of interest. Because the assignment process is external to the research subjects under observation, the assignment process is exogenous, or at least arguably exogenous. This exogenous variation allows researchers to accurately estimate causal relationships.
Natural experiments seem to be increasingly common in the social sciences (see Dunning, 2012). As an example of a natural experiment, Kirk (2009) wished to learn the causal effect of relocating previously incarcerated offenders from their old neighborhoods of residence to new less criminogenic neighborhoods. This is an important theoretical and practical issue because we know that many parolees return to the same criminogenic neighborhoods and social networks that contributed to their involvement in offending in the first place, and therefore we shouldn’t be surprised that recidivism is often alarmingly high. While it is not impossible to conduct a randomized experiment on this issue, it would be difficult for a variety of reasons. However, a recent natural event, Hurricane Katrina, forced many parolees who resided in high‐crime areas of New Orleans hard hit by the storm to move to other neighborhoods. In essence, Hurricane Katrina exogenously assigned some parolees to new neighborhoods, which made it possible to estimate the causal effect of relocation on recidivism. Kirk found that parolees who moved to a new area were substantially less likely to be reincarcerated within three years of release in comparison to parolees who did not move.
Another research design capable of establishing exogenous variation is the regression discontinuity design (see Murnane and Willett, 2011). The key element of this design is the use of some “forcing variable” that establishes a cut point (or threshold) that assigns research subjects below the cut point to one experimental condition and those above the cut point to another condition. The cut point is used as an exogenous source of variation; research subjects just below and just above the cut point are compared to estimate the causal relationship between the variables of interest. As an example of a regression discontinuity design, Berk and Rauma (1983) assessed the causal effect of providing financial assistance in the form of unemployment insurance to recently released former prison inmates. In order to qualify for the financial assistance former prison inmates had to have made at least $1,500 in the year prior to release; this criterion was used as the forcing variable used to assign former inmates to either the control (no financial assistance) or the treatment (financial assistance) conditions. Berk and Rauma found that financial assistance caused a 13% reduction in recidivism.
Outside of criminology, the most popular means of estimating causal relationships without the use of a random experiment is instrumental variable estimation. The logic of instrumental variable estimation begins by noting that if some part of the variation in some endogenous variable of interest could be established as exogenous, then this part of the variation in the variable of interest could be used to accurately estimate its causal effect on the outcome of interest. An “instrumental variable” can be used to identify exogenous variation. An instrumental variable is one that satisfies two assumptions: (1) it is uncorrelated with the error term of the regression of the outcome variable of interest on the endogenous independent variable of interest, and (2) it is correlated with the endogenous variable of interest. The first assumption means that the instrumental variable can have no effect on the outcome variable except via its indirect effect on the outcome through the endogenous independent variable. This is a strong assumption because the instrumental variable must only be related to the outcome variable through the endogenous independent (causal) variable and the instrumental variable cannot be correlated with other factors that affect the outcome variable. If an instrumental variable meeting these criteria can be found, then estimating the causal relationship between the endogenous variable and the outcome of interest is straightforward and can be accomplished using several statistical techniques, of which two‐stage least stages is most popular.
Instrumental variable estimation has rarely been applied in criminology. Apel, Bushway, Paternoster, Brame, and Sweeten (2008) is one example of instrumental variable estimation in criminology. Apel and colleagues examine the relationship between hours worked by youth and delinquency. Prior research typically finds that youth who work more hours are more likely to be involved in delinquency; however, number of hours worked is likely to be endogenously related to delinquency, as youth who work more hours are likely to be different from other youth on a host of factors that are also related to delinquency. Apel and colleagues use variation in state child labor laws as an instrumental variable to identify exogenous variation in the number of hours worked. Contrary to prior research, these authors find that the number of hours worked by youth reduces delinquency.
AIOU Solved Assignment Code 8604 Autumn 2023
5 Write notes on following:
- a) Survey Studies
Survey Research is defined as the process of conducting research using surveys that researchers send to survey respondents. The data collected from surveys is then statistically analyzed to draw meaningful research conclusions. In the 23st century, every organization’s eager to understand what their customers think about their products or services and make better business decisions. Researchers can conduct research in multiple ways, but surveys are proven to be one of the most effective and trustworthy research methods. An online survey is a method for extracting information about a significant business matter from an individual or a group of individuals. It consists of structured survey questions that motivate the participants to respond, Creditable survey research can give these businesses access to a vast information bank. Organizations in media, other companies, and even governments rely on survey research to obtain accurate data. The traditional definition of survey research is a quantitative method for collecting information from a pool of respondents by asking multiple survey questions. This research type includes the recruitment of individuals, collection, and analysis of data. It’s useful for researchers who aim at communicating new features or trends to their respondents. Generally, it’s the primary step towards obtaining quick information about mainstream topics and conducting more rigorous and detailed quantitative research methods like surveys/polls or qualitative research methods like focus groups/on-call interviews can follow. There are many situations where researchers can conduct research using a blend of both qualitative and quantitative strategies.
- b) Interrelationship Studies
Interrelationships are the connections and interactions between people, groups of people, or parts of a system within the system or outside the system. They can often explain events such as success or failure of a business venture. A system is a set of distinct parts that interact with each other to form a distinct whole. A business or other type of organization is a system with parts such as organizational structure, management structure, resources, information, and employees. Something outside the system is part of the system’s external environment.
Implication for Business
Understanding how interrelationships work is an important part of strategy formation and is a primary component of some theories of management, including systems theory and the concept of a learning organization. Let’s take a closer look at each of these.
A key step in formulating any strategy is to identify the strengths, weaknesses, opportunities, and threats facing an organization – often referred to as SWOT analysis. Many times there is no simple explanation of a weakness or strength, but rather they arise out of interaction of various components of your organization as well as the outside environment. For example, imagine you are a book publisher who sees a weakness due to declining market share. The weakness is actually related to the external threat of electronic books, which also provides an opportunity for the company to enter the electronic book market.
This is a management technique that treats a business as a system. In open-system theory, you view the business as a system that interacts with its outside environment through inputs (such as raw materials), throughputs (such as the manufacturing process), and outputs (the finished products) that are released back into the business’ external environment. Understanding the interrelationships of inputs, throughputs, and outputs is crucial to effective management under the systems theory technique.