hypothesis in social work research

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Module 2 Chapter 1: The Nature of Social Work Research Questions

The search for empirical evidence typically begins with a question or hypothesis. The nature of the questions asked determine many features of the studies that lead to answers: the study approach, design, measurement, participant selection, data collection, data analysis, and reporting of results. Not just any type of question will do, however:

“When the question is poorly formulated, the design, analysis, sample size calculations, and presentation of results may not be optimal. The gap between research and clinical practice could be bridged by a clear, complete, and informative research question” (Mayo, Asano, & Barbic, 2013, 513).

The topic concerning the nature of social work research questions has two parts: what constitutes a research question, and what makes it a social work question. We begin this chapter by examining a general model for understanding where different types of questions fit into the larger picture of knowledge building explored in Module 1. We then look at research questions and social work questions separately. Finally, we reassemble them to identify strong social work research questions.

In this chapter, you will learn:

• 4 types of social work research for knowledge building,
• characteristics of research questions,
• characteristics of social work research questions.

Translational Science

The concept of translational science addresses the application of basic science discoveries and knowledge to routine professional practice. In medicine, the concept is sometimes described as “bench to trench,” meaning that it takes what is learned at the laboratory “bench” to practitioners’ work in the real-world, or “in the trenches.” This way of thinking is about applied science—research aimed at eventual applications to create or support change. Figure 1-1 assembles the various pieces of the translational science knowledge building enterprise:

Figure 1-1. Overview of translational science elements

Basic Research .   Federal policy defines basic research  as systematic study that is directed toward understanding the fundamental aspects of phenomena without specific applications in mind (adapted from 32 CFR 272.3). Basic research efforts are those designed to describe something or answer questions about its nature. Basic research in social and behavioral science addresses questions of at least two major types: epidemiology  and  etiology  questions.

Epidemiology questions. Questions about the nature of a population, problem, or social phenomenon are often answered through epidemiological methods. Epidemiology is the branch of science (common in public health) for understanding how a problem or phenomenon is distributed in a population. Epidemiologists also ask and address questions related to the nature of relationships between problems or phenomena—such as the relationship between opioid misuse and infectious disease epidemics (NAS, 2018). One feature offered by epidemiological research is a picture of trends over time. Consider, for example, epidemiology data from the Centers for Disease Control and Prevention (the CDC) regarding trends in suicide rates in the state of Ohio over a four-year period (see Figure 1-2, created from data presented by CDC WONDER database).

Figure 1-2. Graph reflecting Ohio trend in suicide rate, 2012-2016

Since the upward trend is of concern, social workers might pursue additional questions to examine possible causes of the observed increases, as well as what the increase might mean to the expanded need for supportive services to families and friends of these individuals. The epidemiological data can help tease out some of these more nuanced answers. For example, epidemiology also tells us that firearms were the recorded cause in 46.9% of known suicide deaths among individuals aged 15-24 years across the nation during 2016 (CDC, WONDER database). Not only do we now know the numbers of suicide deaths in this age group, we know something about a relevant factor that might be addressed through preventive intervention and policy responses.

Epidemiology also addresses questions about the size and characteristics of a population being impacted by a problem or the scope of a problem. For example, a social worker might have a question about the “shape” of a problem defined as sexual violence victimization. Data from the United States’ 2010-2012 National Intimate Partner and Sexual Violence Survey (NISVS) indicated that over 36% of woman (1 in 3) and 17% of men (1 in 6) have experienced sexual violence involving physical contact at some point in their lives; the numbers vary by state, from 29.5% to 47.5% for women and 10.4% to 29.3% for men (Smith et al., 2017).

In developing informed responses to a problem, it helps to know for whom it is a problem. Practitioners, program administrators, and policy decision makers may not be aware that the problem of sexual violence is so prevalent, or that men are victimized at worrisome rates, as well as women. It is also helpful to know how the problem of interest might interface with other problems. For example, the interface between perpetrating sexual assault and alcohol use was examined in a study of college men (Testa & Cleveland, 2017). The study investigators determined that frequently attending parties and bars was associated with a greater probability of perpetrating sexual assault. Thus, epidemiological research helps answer questions about the scope and magnitude of a problem, as well as how it relates to other issues or factors, which can then inform next steps in research to address the problem.

Etiology questions.  Etiology research tests theories and hypotheses about the origins and natural course of a problem or phenomenon. This includes answering questions about factors that influence the appearance or course of a problem—these may be factors that mediate or moderate the phenomenon’s development or progression (e.g., demographic characteristics, co-occurring problems, or other environmental processes). To continue with our intimate partner violence example, multiple theories are presented in the literature concerning the etiology of intimate partner violence perpetration—theories also exist concerning the etiology of being the target of intimate partner violence (Begun, 2003). Perpetration theories include:

• personality/character traits
• biological/hereditary/genetic predisposition
• social learning/behavior modeling
• social skills
• self-esteem
• cultural norms (Begun, 2003, p. 642).

Evidence supporting each of these theories exists, to some degree; each theory leads to the development of a different type of prevention or intervention response. The “best” interventions will be informed by theories with the strongest evidence or will integrate elements from multiple evidence-supported theories.

Etiology research is often about understanding the mechanisms underlying the phenomena of interest. The questions are “how” questions—how does this happen (or not)? For example, scientists asked the question: how do opioid medications (used to manage pain) act on neurons compared to opioids that naturally occur in the brain (Stoeber et al., 2018)? They discovered that opioid medications used to treat pain bind to receptors  inside n erve cells, which is a quite different mechanism than the conventional wisdom that they behave the same way that naturally occurring (endogenous) opioids do—binding only on the surface  of nerve cells. Understanding this mechanism opens new options for developing pain relievers that are less- or non-addicting than current opioid medicines like morphine and oxycodone. Once these mechanisms of change are understood, interventions can be developed, then tested through intervention research approaches.

Intervention Research.  Interventions are designed around identified needs: epidemiology research helps to support intervention design by identify the needs. Epidemiology research also helps identify theories concerning the causes and factors affecting social work problems. Intervention development is further supported by later theory-testing and etiology research. However, developing an intervention is not sufficient: interventions need to be tested and evaluated to ensure that they are (1) safe, (2) effective, and (3) cost-efficient to deliver. This is where  intervention research  comes into play. Consider the example of Motivational Interviewing (MI) approaches to addressing client ambivalence about engaging in a behavior change effort. Early research concerning MI addressed questions about its effectiveness. For example, a meta-analytic review reported that “MI should be considered as a treatment for adolescent substance abuse” because the evidence demonstrated small, but significant effect sizes, and that the treatment gains were retained over time (Jensen et al., 2011). Subsequently, when its safety and effectiveness were consistently demonstrated through this kind of evidence, investigators assessed MI as cost-efficient or cost-effective. For example, MI combined with providing feedback was demonstrated to be cost-effective in reducing drinking among college students who engaged in heavy drinking behavior (Cowell et al., 2012).

Intervention research not only is concerned with the outcomes of delivering an intervention, but may also address the mechanisms of change  through which an intervention has its effects—not only what changes happen, but how  they happen. For example, investigators are exploring  how  psychotherapy works, moving beyond demonstrating that  it works (Ardito & Rabellino, 2011; Kazdin, 2007; Wampold, 2015). One mechanism that has garnered attention is the role of therapeutic alliance—the relationships, bonds, and interactions that occur in the context of treatment—on treatment outcomes.

Therapeutic alliance is one common factor identified across numerous types of effective psychotherapeutic approaches (Wampold, 2015). Authors summarizing a number of studies about therapeutic alliance and its positive relationship to treatment outcomes concluded that the quality of therapeutic alliance may be a more powerful predictor of positive outcome than is the nature or type of intervention delivered (Ardito & Rabellino, 2011). However, it is important to determine the extent to which (a) therapeutic alliance enhances clients’ symptom improvement, (b) gradual improvements in symptoms lead to enhanced therapeutic alliance, or (c) the relationship between therapeutic alliance and symptom improvement are iterative—they go back and forth, influencing each other over time (Kazdin, 2007).

Implementation Science . Social work and other disciplines have produced a great deal of evidence about “what works” for intervening around a great number of social work problems. Unfortunately, many best practices with this kind of evidence support are slow to become common practices.  Implementation science  is about understanding facilitators and barriers to these evidence-supported interventions becoming adopted into routine practice: characteristics of the interventions themselves, conditions and processes operating in the organizations where interventions are implemented, and factors external to these organizations all influence practitioners’ adoption of evidence supported interventions.

Even under optimal internal organizational conditions, implementation can be undermined by changes in organizations’ external environments, such as fluctuations in funding, adjustments in contracting practices, new technology, new legislation, changes in clinical practice guidelines and recommendations, or other environmental shifts” (Birken, et al, 2017).

Research for/about Research . In addition, social work investigators engage in research that is specifically about scientific methodology. This is where advances in measurement, participant recruitment and retention, and data analysis emerge. The results of these kinds of research studies are used to improve the research in basic, intervention, and implementation research. Later in the course you will see some of these products in action as we learn about best practices in research and evaluation methodology. Here are a few examples related to measurement methods:

• Concept mapping to assess community needs of sexual minority youth (Davis, Saltzburg, & Locke, 2010)
• Field methodologies for measuring college student drinking in natural environments (Clapp et al., 2007)
• Intergenerational contact measurement (Jarrott, Weaver, Bowen, & Wang, 2018)
• Perceived Social Competence Scale-II (Anderson-Butcher et al., 2016)
• Safe-At-Home Instrument to measure readiness to change intimate partner violence behavior (Begun et al., 2003; 2008; Sielski, Begun, & Hamel, 2015)
• Teamwork Scale for Youth (Lower, Newman, & Anderson-Butcher, 2016)

And, here are a few examples related to involving participants in research studies:

• Conducting safe research with at risk populations (Kyriakakis, Waller, Kagotho, & Edmond, 2015)
• Recruitment strategies for non-treatment samples in addiction studies (Subbaraman et al., 2015)
• Variations in recruitment results across Internet platforms (Shao et al., 2015)

Take a moment to complete the following activity.

Research Questions

In this section, we take a closer look at research questions and their relationship to the types of research conducted by investigators. It may be easier to understand research questions by first ruling out what are not research questions. In that spirit, let’s begin with examples of questions where applying research methods will not help to find answers:

• Trauma informed education. The first issue with this example is obvious: it is not worded as a question. The second is critically important: this is a general topic, it is not a research question. This topic is too vague and broad making it impossible to determine what answers would look like or how to approach finding answers.
• How is my client feeling about what just happened? This type of question about an individual is best answered by asking clinical questions of that individual, within the context of the therapeutic relationship, not by consulting research literature or conducting a systematic research study.
• Will my community come together in protest of a police-involved shooting incident? This type of question may best be answered by waiting to see what the future brings. Research might offer a guess based on data from how other communities behaved in the past but cannot predict how groups in individual situations will behave. A better research question might be: What factors predict community protest in response to police-involved shooting incidents?
• Should I order salad or soup to go with my sandwich? This type of question is not of general interest, making it a poor choice as a research question. The question might be reframed as a general interest question: Is it healthier to provide salad or soup along with a sandwich? The answer to that researchable question might inform a personal decision.
• Why divorce is bad for children. There are two problems with this example. First, it is a statement, not a question, despite starting with the word “why.” Second, this question starts out with a biased assumption—that divorce is bad for children. Research questions should support unbiased investigation, leading to evidence and answers representative of what exists rather than what someone sets out wanting to prove is the case. A better research question might be: How does divorce affect children?

Tuning back to our first example of what is not a research question, consider several possible school social work research questions related to that general topic:

• To what extent do elementary school personnel feel prepared to engage in trauma informed education with their students?
• What are the barriers and facilitators of integrating trauma informed education in middle school?
• Does integrating trauma informed education result in lower rates of suicidal ideation among high school students?

Is there a relationship between parent satisfaction and the implementation of trauma informed education in their children’s schools?

Does implementing trauma informed education in middle schools affect the rate of student discipline referrals?

What is the difference between these research questions and the earlier “not research” questions? First, research questions are specific. This is an important distinction between identifying a topic of interest (e.g., trauma informed education) and asking a researchable question. For example, the question “How does divorce affect children?” is not a good research question because it remains too broad. Instead, investigators might focus their research questions on one or two specific effects of interest, such as emotional or mental health, academic performance, sibling relationships, aggression, gender role, or dating relationship outcomes.

Related to a question being “researchable” is its feasibility for study. Being able to research a question requires that appropriate data can be collected with integrity. For example, it may not be feasible to study what would happen if every child was raised by two parents, because (a) it is impossible to study every child and (2) this reality cannot ethically be manipulated to systematically explore it. No one can ethically conduct a study whereby children are randomly assigned by study investigators to the compared conditions of being raised by two parents versus being raised by one or no parents. Instead, we settle for observing what has occurred naturally in different families.

Second, “good” research questions are relevant to knowledge building. For this reason, the question about what to eat was not a good research question—it is not relevant to others’ knowledge development. Relevance is in the “eye of the beholder,” however. A social work researcher may not see the relevance of using a 4-item stimulus array versus a 6-item stimulus array in testing children’s memory, but this may be an important research question for a cognitive psychology researcher. It may, eventually, have implications for assessment measures used in social work practice.

Third, is the issue of bias built into research questions. Remembering that investigators are a product of their own developmental and social contexts, what they choose to study and how they choose to study it are socially constructed. An important aspect at the heart of social work research relates to a question’s cultural appropriateness and acceptability. To demonstrate this point, consider an era (during the 1950s to early 1970s) when research questions were asked about the negative effects on child development of single-parent, black family households compared to two-parent, white family households in America. This “majority comparison” frame of reference is not culturally appropriate or culturally competent. Today, in social work, we adopt a strengths perspective, and avoid making comparisons of groups against a majority model. For example, we might ask questions like: What are the facilitators and barriers of children’s positive development as identified by single parents of diverse racial/ethnic backgrounds? What strengths do African American parents bring to the experience of single-parenting and how does it shape their children’s development? What are the similar and different experiences of single-parenting experienced by families of different racial/ethnic composition?

Research Questions versus Research Hypotheses . You have now seen examples of “good” research questions. Take, for example, the last one we listed about trauma informed education:

Based on a review of literature, practice experience, previous research efforts, and the school’s interests, an investigator may be prepared to be even more specific about the research question (see Figure 1-3). Assume that these sources led the investigator to believe that implementing the trauma informed education approach will have the effect of reducing the rate of disciplinary referrals. The investigator may then propose to test the following hypothesis:

Implementing trauma informed education in middle schools will result in a reduction in the number of student discipline referrals.

The research hypothesis  is a clear statement that can be tested with quantitative data and will either be rejected or not, depending on the evidence. Research hypotheses are predictions about study results—what the investigator expects the results will show. The prediction, or hypothesis, is based on theory and/or other evidence. A study hypothesis is, by definition, quantifiable—the answer lies in numerical data, which is why we do not generally see hypotheses in qualitative, descriptive research reports.

Hypotheses are also specific to one question at a time. Thus, an investigator would need to state and test a second hypothesis to answer the question:

The stated hypothesis might be:

Parent satisfaction is higher in middle schools where trauma informed education is implemented.

Figure 1-3. Increasing specificity from research topic to question to hypothesis

Social Work Questions

It is difficult to find a simple way to characterize social work research. The National Institutes of Health (NIH) described social work research in the following way:

Historically, social work research has focused on studies of the individual, family, group, community, policy and/or organizational level, focusing across the lifespan on prevention, intervention, treatment, aftercare and rehabilitation of acute and chronic conditions, including the effects of policy on social work practice (OBSSR, 2003, p. 5) .

For all the breadth expressed in this statement, it reflects only how social work research relates to the health arena—it does not indicate many other domains and service delivery systems of social work influence:

• physical, mental, and behavioral health
• substance misuse/addiction and other addictive behaviors
• income/poverty
• criminal justice
• child and family welfare
• housing and food security/insecurity
• environmental social work
• intimate partner, family, and community violence
• and others.

In addition to breadth of topic, social work research is characterized by its biopsychosocial nature. This means that social work researchers not only pursue questions relating to biological, psychological, and social context factors, but also questions relating to their intersections and interactions. Related to this observation is that social work not only addresses questions related to the multiple social system levels, social work also addresses the ways multiple levels intersect and interact (i.e., those levels represented in the NIH statement about individuals, families, groups, communities, organizations, and policy).

It is worth noting that research need not be conducted by social workers to be relevant to social work–many disciplines and professions contribute to the knowledge base which informs social work practice (medicine, nursing, education, occupational therapy, psychology, sociology, criminal justice, political science, economics, and more). Authors of one social work research textbook summarize the relevance issue in the following statement:

“To social workers, a relevant research question is one whose answers will have an impact on policies, theories, or practices related to the social work profession” (Grinnell & Unrau, 2014, p. 46).

Social Work Research Questions and Specific Aims

The kinds of questions that help inform social work practice and policy are relevant to understanding social work problems, diverse populations, social phenomena, or interventions. Most social work research questions can be divided into two general categories: background questions  and foreground questions . The major distinction between these two categories relates to the specific aims that emerge in relation to the research questions.

Background Questions.  This type of question is answerable with a fact or set of facts. Background questions are generally simple in structure, and they direct a straightforward search for evidence. This type of question can usually be formulated using the classic 5 question words: who, what, when, where, or why. Here are a few examples of social work background questions related to the topic of fetal alcohol exposure:

• Who is at greatest risk of fetal alcohol exposure?
• What are the developmental consequences of fetal alcohol exposure?
• When in gestation is the risk of fetal alcohol exposure greatest?
• Where do women get information about the hazards of drinking during pregnancy?
• Why is fetal alcohol exposure (FAE) presented as a spectrum disorder, different from fetal alcohol syndrome (FAS)?

These kinds of questions direct a social worker to review literature about human development, human behavior, the distribution of the problem across populations, and factors that determine the nature of a specific social work problem like fetal exposure to alcohol. Where the necessary knowledge is lacking, investigators aim to explore or describe the phenomenon of interest. Many background questions can be answered by epidemiology or etiology evidence.

Foreground Questions.  This type of question is more complex than the typical background question. Foreground questions typically are concerned with making specific choices by comparing or evaluating options. These types of questions required more specialized evidence and may lead to searching different types of resources than would be helpful for answering background questions. Foreground questions are dealt with in greater detail in our second course, SWK 3402 which is about understanding social work interventions. A quick foreground question example related to the fetal exposure to alcohol topic might be:

Which is the best tool for screening pregnant women for alcohol use with the aim of reducing fetal exposure, the T-ACE, TWEAK, or AUDIT?

This type of question leads the social worker to search for evidence that compares different approaches. These kinds of evidence are usually found in comparative reviews, or require the practitioner to conduct a review of literature, locating individual efficacy and effectiveness studies. Where knowledge is found to be lacking, investigators aim to experiment with different approaches or interventions.

Three Question Types and Their Associated Research Aims

Important distinctions exist related to different types of background questions. Consider three general categories of questions that social workers might ask about populations, problems, and social phenomena: exploratory, descriptive, and explanatory. The different types of questions matter because the nature of the research questions determines the specific aims and most appropriate research approaches investigators apply in answering them.

Exploratory Research Questions. Social workers may find themselves facing a new, emerging problem where there is little previously developed knowledge available—so little, in fact, that it is premature to begin asking any more complex questions about causes or developing testable theories. Exploratory research questions open the door to beginning understanding and are basic; answers would help build the foundation of knowledge for asking more complex descriptive and explanatory questions. For example, in the early days of recognition that HIV/AIDS was emerging as a significant public health problem, it was premature to jump to questions about how to treat or prevent the problem. Not enough was known about the nature and scope of the problem, for whom it was a problem, how the problem was transmitted, factors associated with risk for exposure, what factors influenced the transition from HIV exposure to AIDS as a disease state, and what issues or problems might co-occur along with either HIV exposure or AIDS. In terms of a knowledge evolution process, a certain degree of exploration had to occur before intervention strategies for prevention and treatment could be developed, tested, and implemented.

In 1981, medical providers, public health officials, and the Centers for Disease Control and Prevention (CDC) began to circulate and publish observations about a disproportionate, unexpectedly high incidence rate of an unusual pneumonia and Kaposi’s sarcoma appearing in New York City and San Francisco/California among homosexual men (Curran, & Jaffe, 2011). As a result, a task force was formed and charged with conducting an epidemiologic investigation of this outbreak; “Within 6 months, it was clear that a new, highly concentrated epidemic of life threatening illness was occurring in the United States” (Curran & Jaffe, 2011, p. 65). The newly recognized disease was named for its symptoms: acquired immune deficiency syndrome, or AIDS. Exploratory research into the social networks of 90 living patients in 10 different cities indicated that 40 had a sexual contact link with another member of the 90-patient group (Auerbach, Darrow, Jaffe, & Curran, 1984). Additionally, cases were identified among persons who had received blood products related to their having hemophilia, persons engaged in needle sharing during substance use, women who had sexual contact with a patient, and infants born to exposed women. Combined, these pieces of information led to an understanding that the causal infectious factor (eventually named the human immunodeficiency virus, HIV) was transmitted by sexual contact, blood, and placental connection. This, in turn, led to knowledge building activities to develop both preventive and treatment strategies which could be implemented and studied. Social justice concerns relate to the slow rate at which sufficient resources were committed for evolving to the point of effective solutions for saving lives among those at risk or already affected by a heavily stigmatized problem.

The exploratory research approaches utilized in the early HIV/AIDS studies were both qualitative and quantitative in nature. Qualitative studies included in-depth interviews with identified patients—anthropological and public health interviews about many aspects of their living, work, and recreational environments, as well as many types of behavior. Quantitative studies included comparisons between homosexually active men with and without the diseases of concern. In addition, social network study methods combined qualitative and quantitative approaches. These examples of early exploratory research supported next steps in knowledge building to get us to where we are today. “Today, someone diagnosed with HIV and treated before the disease is far advanced can live nearly as long as someone who does not have HIV” (hiv.gov). While HIV infection cannot (yet) be “cured,” it can be controlled and managed as a chronic condition.

Descriptive Research Questions.  Social workers often ask for descriptions about specific populations, problems, processes, or phenomena. Descriptive research questions  might be expressed in terms of searching to create a profile of a group or population, create categories or types (typology) to describe elements of a population, document facts that confirm or contradict existing beliefs about a topic or issue, describe a process, or identify steps/stages in a sequential process (Grinnell & Unrau, 2014). Investigators may elect to approach the descriptive question using qualitative methods that result in a rich, deep description of certain individuals’ experiences or perceptions (Yegidis, Weinbach, & Meyers, 2018). Or, the descriptive question might lead investigators to apply quantitative methods, assigning numeric values, measuring variables that describe a population, process, or situation of interest. In descriptive research, investigators do not manipulate or experiment with the variables; investigators seek to describe what naturally occurs (Yegidis, Weinbach, & Meyers, 2018). As a result of studies answering descriptive questions, tentative theories and hypotheses may be generated.

Here are several examples of descriptive questions.

• How do incarcerated women feel about the option of medication-assisted treatment for substance use disorders?
• What barriers to engaging in substance misuse treatment do previously incarcerated persons experience during community reentry?
• How often do emerging adults engage in binge drinking in different drinking contexts (e.g., bars, parties, sporting events, at home)?
• What percent of incarcerated adults experience a substance use disorder?
• What is the magnitude of racial/ethnic disparities in access to treatment for substance use disorders?
• Who provides supervision or coordination of services for aging adults with intellectual or other developmental disabilities?
• What is the nature of the debt load among students in doctoral social work programs?

An example of descriptive research, derived from a descriptive question, is represented in an article where investigators addressed the question: How is the topic of media violence and aggression reported in print media (Martins et al., 2013)? This question led the investigators to conduct a qualitative content analysis, resulting in a description showing a shift in tone where earlier articles (prior to 2000) emphasized the link as a point of concern and later articles (since 2000) assumed a more neutral stance.

Correlational Research Questions.  One important type of descriptive question asks about relationships that might exist between variables—looking to see if variable x  and variable y  are associated or correlated with each other. This is an example of a correlational research question; it does not indicate whether “x” causes “y” or “y” causes “x”, only whether these two are related. Consider again the topic of exposure to violence in the media and its relationship to aggression. A descriptive question asked about the existence of a relationship between exposure to media violence ( variable x ) and children’s expression of aggression ( variable y ). Investigators reported one study of school-aged children, examining the relationship between exposure to three types of media violence (television, video games, and movies/videos) and three types of aggression (verbal, relational, and physical; Gentile, Coyne, & Walsh, 2011). The study investigators reported that media violence exposure was, indeed, correlated with all three types of aggressive behavior (and less prosocial behavior, too).

For a positive correlation (the blue line), as the value of the “x” variable increases, so does the value of the “y” variable (see Figure 1-4 for a general demonstration). An example might be as age or grade in school increases (“x”), so does the number of preadolescent, adolescent, and emerging adults who have used alcohol (“y”). For a negative correlation (the orange line), as the value of the “x” variable increases, the value of the “y” variable decreases. An example might be as the number of weeks individuals are in treatment for depression symptoms (“x”), the reported depression symptoms decreases (“y”). The neutral of non-correlation line (grey) means that the two variables, “x” and “y” do not have an association with each other. For example, number of years of teachers’ education (“x”) might be unrelated to the number of students dropping out of high school (“y”).

Figure 1-4. Depicting positive, negative, and neutral correlation lines

Descriptive correlational studies are sometimes called comparison studies because the descriptive question is answered by comparing groups that differ on one of the variables (low versus high media violence exposure) to see how they might differ on the other variable (aggressive behavior).

Explanatory Research Questions. To inform the design of evidence-informed interventions, social workers need answers to questions about the nature of the relationships between potentially influential factors or variables. An explanatory research question  might be mapped as: Does variable x  cause, lead to or prevent changes in variable y  (Grinnell & Unrau, 2014)? These types of questions often test theory related to etiology.

Comparative research might provide information about a relationship between variables. For example, the difference in outcomes between persons experiencing a substance use disorder and have been incarcerated compared to others with the same problem but have not been incarcerated may be related to their employability and ability to generate a living-wage income for themselves and their families. However, to develop evidence-informed interventions, social workers need to know that variables are not only related, but that one variable actually plays a causal role in relation to the other. Imagine, for example, that evidence demonstrated a significant relationship between adolescent self-esteem and school performance. Social workers might spend a great deal of effort developing interventions to boost self-esteem in hopes of having a positive impact on school performance. However, what if self-esteem comes from strong school performance? The self-esteem intervention efforts will not likely have the desired effect on school performance. Just because research demonstrates a significant relationship between two variables does not mean that the research has demonstrated a  causal relationship between those variables. Investigators need to be cautious about the extent to which their study designs can support drawing conclusions about causality; anyone reviewing research reports also needs to be alert to where causal conclusions are properly and improperly drawn.

The questions that drive intervention and evaluation research studies are explanatory in nature: does the intervention ( x ) have a significant impact on outcomes of interest ( y )? Another type of explanatory question related to intervention research concerns the mechanisms of change. In other words, not only might social workers be interested to find out  what  outcomes or changes can be attributed to an intervention, they may also be interested to learn how  the intervention causes those changes or outcomes.

Chapter Summary

In this chapter, you learned about different aspects of the knowledge building process and where different types of research questions might fit into the big picture. No single research study covers the entire spectrum; each study contributes a piece of the puzzle as a whole. Research questions come in many different forms and several different types. What is important to recall as we move through the remainder of the course is that the decisions investigators make about research approaches, designs, and procedures all start with the nature of the question being asked. And, the questions being asked are influenced by multiple factors, including what is previously known and remains unknown, the culture and context of the questioners, and what theories they have about what is to be studied. That leads us to the next chapter.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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2.3: Propositions and Hypotheses

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• Anol Bhattacherjee
• University of South Florida via Global Text Project

Figure 2.2 shows how theoretical constructs such as intelligence, effort, academic achievement, and earning potential are related to each other in a nomological network. Each of these relationships is called a proposition. In seeking explanations to a given phenomenon or behavior, it is not adequate just to identify key concepts and constructs underlying the target phenomenon or behavior. We must also identify and state patterns of relationships between these constructs. Such patterns of relationships are called propositions. A proposition is a tentative and conjectural relationship between constructs that is stated in a declarative form. An example of a proposition is: “An increase in student intelligence causes an increase in their academic achievement.” This declarative statement does not have to be true, but must be empirically testable using data, so that we can judge whether it is true or false. Propositions are generally derived based on logic (deduction) or empirical observations (induction).

Because propositions are associations between abstract constructs, they cannot be tested directly. Instead, they are tested indirectly by examining the relationship between corresponding measures (variables) of those constructs. The empirical formulation of propositions, stated as relationships between variables, is called hypotheses (see Figure 2.1). Since IQ scores and grade point average are operational measures of intelligence and academic achievement respectively, the above proposition can be specified in form of the hypothesis: “An increase in students’ IQ score causes an increase in their grade point average.” Propositions are specified in the theoretical plane, while hypotheses are specified in the empirical plane. Hence, hypotheses are empirically testable using observed data, and may be rejected if not supported by empirical observations. Of course, the goal of hypothesis testing is to infer whether the corresponding proposition is valid.

Hypotheses can be strong or weak. “Students’ IQ scores are related to their academic achievement” is an example of a weak hypothesis, since it indicates neither the directionality of the hypothesis (i.e., whether the relationship is positive or negative), nor its causality (i.e., whether intelligence causes academic achievement or academic achievement causes intelligence). A stronger hypothesis is “students’ IQ scores are positively related to their academic achievement”, which indicates the directionality but not the causality. A still better hypothesis is “students’ IQ scores have positive effects on their academic achievement”, which specifies both the directionality and the causality (i.e., intelligence causes academic achievement, and not the reverse). The signs in Figure 2.2 indicate the directionality of the respective hypotheses.

Also note that scientific hypotheses should clearly specify independent and dependent variables. In the hypothesis, “students’ IQ scores have positive effects on their academic achievement,” it is clear that intelligence is the independent variable (the “cause”) and academic achievement is the dependent variable (the “effect”). Further, it is also clear that this hypothesis can be evaluated as either true (if higher intelligence leads to higher academic achievement) or false (if higher intelligence has no effect on or leads to lower academic achievement). Later on in this book, we will examine how to empirically test such cause-effect relationships. Statements such as “students are generally intelligent” or “all students can achieve academic success” are not scientific hypotheses because they do not specify independent and dependent variables, nor do they specify a directional relationship that can be evaluated as true or false.

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Chapter 3: Developing a Research Question

3.4 Hypotheses

When researchers do not have predictions about what they will find, they conduct research to answer a question or questions with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses. A hypothesis is a statement, sometimes but not always causal, describing a researcher’s expectations regarding anticipated finding. Often hypotheses are written to describe the expected relationship between two variables (though this is not a requirement). To develop a hypothesis, one needs to understand the differences between independent and dependent variables and between units of observation and units of analysis. Hypotheses are typically drawn from theories and usually describe how an independent variable is expected to affect some dependent variable or variables. Researchers following a deductive approach to their research will hypothesize about what they expect to find based on the theory or theories that frame their study. If the theory accurately reflects the phenomenon it is designed to explain, then the researcher’s hypotheses about what would be observed in the real world should bear out.

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and legalization of marijuana. Perhaps you have done some reading in your spare time, or in another course you have taken. Based on the theories you have read, you hypothesize that “age is negatively related to support for marijuana legalization.” What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their support for marijuana legalization decreases. Thus, as age moves in one direction (up), support for marijuana legalization moves in another direction (down). If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

Note that you will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a relationship has been shown to exist with absolute certainty and there is no chance that there are conditions under which the hypothesis would not bear out. Instead, researchers tend to say that their hypotheses have been supported (or not). This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining a relationship will be discovered. Researchers may also discuss a null hypothesis, one that predicts no relationship between the variables being studied. If a researcher rejects the null hypothesis, he or she is saying that the variables in question are somehow related to one another.

Quantitative and qualitative researchers tend to take different approaches when it comes to hypotheses. In quantitative research, the goal often is to empirically test hypotheses generated from theory. With a qualitative approach, on the other hand, a researcher may begin with some vague expectations about what he or she will find, but the aim is not to test one’s expectations against some empirical observations. Instead, theory development or construction is the goal. Qualitative researchers may develop theories from which hypotheses can be drawn and quantitative researchers may then test those hypotheses. Both types of research are crucial to understanding our social world, and both play an important role in the matter of hypothesis development and testing.  In the following section, we will look at qualitative and quantitative approaches to research, as well as mixed methods.

Text attributions This chapter has been adapted from Chapter 5.2 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor, and is licensed under a CC BY-NC-SA 3.0 License .

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Social Work Research Methods That Drive the Practice

Social workers advocate for the well-being of individuals, families and communities. But how do social workers know what interventions are needed to help an individual? How do they assess whether a treatment plan is working? What do social workers use to write evidence-based policy?

Social work involves research-informed practice and practice-informed research. At every level, social workers need to know objective facts about the populations they serve, the efficacy of their interventions and the likelihood that their policies will improve lives. A variety of social work research methods make that possible.

Data-Driven Work

Data is a collection of facts used for reference and analysis. In a field as broad as social work, data comes in many forms.

Quantitative vs. Qualitative

As with any research, social work research involves both quantitative and qualitative studies.

Quantitative Research

Answers to questions like these can help social workers know about the populations they serve — or hope to serve in the future.

• How many students currently receive reduced-price school lunches in the local school district?
• How many hours per week does a specific individual consume digital media?
• How frequently did community members access a specific medical service last year?

Quantitative data — facts that can be measured and expressed numerically — are crucial for social work.

Quantitative research has advantages for social scientists. Such research can be more generalizable to large populations, as it uses specific sampling methods and lends itself to large datasets. It can provide important descriptive statistics about a specific population. Furthermore, by operationalizing variables, it can help social workers easily compare similar datasets with one another.

Qualitative Research

Qualitative data — facts that cannot be measured or expressed in terms of mere numbers or counts — offer rich insights into individuals, groups and societies. It can be collected via interviews and observations.

• What attitudes do students have toward the reduced-price school lunch program?
• What strategies do individuals use to moderate their weekly digital media consumption?
• What factors made community members more or less likely to access a specific medical service last year?

Qualitative research can thereby provide a textured view of social contexts and systems that may not have been possible with quantitative methods. Plus, it may even suggest new lines of inquiry for social work research.

Mixed Methods Research

Combining quantitative and qualitative methods into a single study is known as mixed methods research. This form of research has gained popularity in the study of social sciences, according to a 2019 report in the academic journal Theory and Society. Since quantitative and qualitative methods answer different questions, merging them into a single study can balance the limitations of each and potentially produce more in-depth findings.

However, mixed methods research is not without its drawbacks. Combining research methods increases the complexity of a study and generally requires a higher level of expertise to collect, analyze and interpret the data. It also requires a greater level of effort, time and often money.

The Importance of Research Design

Data-driven practice plays an essential role in social work. Unlike philanthropists and altruistic volunteers, social workers are obligated to operate from a scientific knowledge base.

To know whether their programs are effective, social workers must conduct research to determine results, aggregate those results into comprehensible data, analyze and interpret their findings, and use evidence to justify next steps.

Employing the proper design ensures that any evidence obtained during research enables social workers to reliably answer their research questions.

Research Methods in Social Work

The various social work research methods have specific benefits and limitations determined by context. Common research methods include surveys, program evaluations, needs assessments, randomized controlled trials, descriptive studies and single-system designs.

Surveys involve a hypothesis and a series of questions in order to test that hypothesis. Social work researchers will send out a survey, receive responses, aggregate the results, analyze the data, and form conclusions based on trends.

Surveys are one of the most common research methods social workers use — and for good reason. They tend to be relatively simple and are usually affordable. However, surveys generally require large participant groups, and self-reports from survey respondents are not always reliable.

Program Evaluations

Social workers ally with all sorts of programs: after-school programs, government initiatives, nonprofit projects and private programs, for example.

Crucially, social workers must evaluate a program’s effectiveness in order to determine whether the program is meeting its goals and what improvements can be made to better serve the program’s target population.

Evidence-based programming helps everyone save money and time, and comparing programs with one another can help social workers make decisions about how to structure new initiatives. Evaluating programs becomes complicated, however, when programs have multiple goal metrics, some of which may be vague or difficult to assess (e.g., “we aim to promote the well-being of our community”).

Needs Assessments

Social workers use needs assessments to identify services and necessities that a population lacks access to.

Common social work populations that researchers may perform needs assessments on include:

• People in a specific income group
• Everyone in a specific geographic region
• A specific ethnic group
• People in a specific age group

In the field, a social worker may use a combination of methods (e.g., surveys and descriptive studies) to learn more about a specific population or program. Social workers look for gaps between the actual context and a population’s or individual’s “wants” or desires.

For example, a social worker could conduct a needs assessment with an individual with cancer trying to navigate the complex medical-industrial system. The social worker may ask the client questions about the number of hours they spend scheduling doctor’s appointments, commuting and managing their many medications. After learning more about the specific client needs, the social worker can identify opportunities for improvements in an updated care plan.

In policy and program development, social workers conduct needs assessments to determine where and how to effect change on a much larger scale. Integral to social work at all levels, needs assessments reveal crucial information about a population’s needs to researchers, policymakers and other stakeholders. Needs assessments may fall short, however, in revealing the root causes of those needs (e.g., structural racism).

Randomized Controlled Trials

Randomized controlled trials are studies in which a randomly selected group is subjected to a variable (e.g., a specific stimulus or treatment) and a control group is not. Social workers then measure and compare the results of the randomized group with the control group in order to glean insights about the effectiveness of a particular intervention or treatment.

Randomized controlled trials are easily reproducible and highly measurable. They’re useful when results are easily quantifiable. However, this method is less helpful when results are not easily quantifiable (i.e., when rich data such as narratives and on-the-ground observations are needed).

Descriptive Studies

Descriptive studies immerse the researcher in another context or culture to study specific participant practices or ways of living. Descriptive studies, including descriptive ethnographic studies, may overlap with and include other research methods:

• Informant interviews
• Census data
• Observation

By using descriptive studies, researchers may glean a richer, deeper understanding of a nuanced culture or group on-site. The main limitations of this research method are that it tends to be time-consuming and expensive.

Single-System Designs

Unlike most medical studies, which involve testing a drug or treatment on two groups — an experimental group that receives the drug/treatment and a control group that does not — single-system designs allow researchers to study just one group (e.g., an individual or family).

Single-system designs typically entail studying a single group over a long period of time and may involve assessing the group’s response to multiple variables.

For example, consider a study on how media consumption affects a person’s mood. One way to test a hypothesis that consuming media correlates with low mood would be to observe two groups: a control group (no media) and an experimental group (two hours of media per day). When employing a single-system design, however, researchers would observe a single participant as they watch two hours of media per day for one week and then four hours per day of media the next week.

These designs allow researchers to test multiple variables over a longer period of time. However, similar to descriptive studies, single-system designs can be fairly time-consuming and costly.

Learn More About Social Work Research Methods

Social workers have the opportunity to improve the social environment by advocating for the vulnerable — including children, older adults and people with disabilities — and facilitating and developing resources and programs.

Learn more about how you can earn your  Master of Social Work online at Virginia Commonwealth University . The highest-ranking school of social work in Virginia, VCU has a wide range of courses online. That means students can earn their degrees with the flexibility of learning at home. Learn more about how you can take your career in social work further with VCU.

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Social workers' strategies for treatment hypothesis testing

The authors thank the respondents who have contributed so generously from their time and the Social Welfare Department of the Municipality of Jerusalem for their collaboration. Correspondence should be addressed to Dr. Rujla Osmo.

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Rujla Osmo, Aaron Rosen, Social workers' strategies for treatment hypothesis testing, Social Work Research , Volume 26, Issue 1, March 2002, Pages 9–18, https://doi.org/10.1093/swr/26.1.9

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This study describes how social workers test their treatment hypotheses. The authors looked at the information search strategies and the intended use of this information (biased or unbiased). Information search strategies were defined as confirming or disconfirming the treatment hypotheses. These dependent variables were also examined in relation to confidence in the hypotheses, the number of alternative hypotheses, and whether the alternative hypotheses were complementary or contradictory to the original hypothesis. Analyses indicated marked variations in information search strategies and intended use of that information and a preference for confirmatory strategies and unbiased use of information. Among the conclusions suggested by the findings is that a critical approach to decision making can be enhanced by encouraging workers to give explicit rationales for their clinical decisions.

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13 4.2 Causality

Learning objectives.

• Define and provide an example of idiographic and nomothetic causal explanations
• Describe the role of causality in quantitative research as compared to qualitative research
• Identify, define, and describe each of the main criteria for nomothetic causal explanations
• Describe the difference between and provide examples of independent, dependent, and control variables
• Define hypothesis, be able to state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses

Most social scientific studies attempt to provide some kind of causal explanation.  In other words, it is about cause and effect. A study on an intervention to prevent child abuse is trying to draw a connection between the intervention and changes in child abuse. Causality refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief.  It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

Think back to our chapter on paradigms, which were analytic lenses comprised of assumptions about the world. You’ll remember the positivist paradigm as the one that believes in objectivity and social constructionist paradigm as the one that believes in subjectivity. Both paradigms are correct, though incomplete, viewpoints on the social world and social science.

A researcher operating in the social constructionist paradigm would view truth as subjective. In causality, that means that in order to try to understand what caused what, we would need to report what people tell us. Well, that seems pretty straightforward, right? Well, what if two different people saw the same event from the exact same viewpoint and came up with two totally different explanations about what caused what? A social constructionist might say that both people are correct. There is not one singular truth that is true for everyone, but many truths created and shared by people.

When social constructionists engage in science, they are trying to establish one type of causality—idiographic causality.  The word idiographic comes from the root word “idio” which means peculiar to one, personal, and distinct. An idiographic causal explanation means that you will attempt to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants. Idiographic causal explanations are intended to explain one particular context or phenomenon.  These explanations are bound with the narratives people create about their lives and experience, and are embedded in a cultural, historical, and environmental context. Idiographic causal explanations are so powerful because they convey a deep understanding of a phenomenon and its context. From a social constructionist perspective, the truth is messy. Idiographic research involves finding patterns and themes in the causal themes established by your research participants.

If that doesn’t sound like what you normally think of as “science,” you’re not alone. Although the ideas behind idiographic research are quite old in philosophy, they were only applied to the sciences at the start of the last century. If we think of famous Western scientists like Newton or Darwin, they never saw truth as subjective. They operated with the understanding there were objectively true laws of science that were applicable in all situations. In their time, another paradigm–the positivist paradigm–was dominant and continues its dominance today. When positivists try to establish causality, they are like Newton and Darwin, trying to come up with a broad, sweeping explanation that is universally true for all people. This is the hallmark of a nomothetic causal explanation .  The word nomothetic is derived from the root word “nomo” which means related to a law or legislative, and “thetic” which means something that establishes.  Put the root words together and it means something that is establishing a law, or in our case, a universal explanation.

Nomothetic causal explanations are incredibly powerful. They allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important. We clearly do not have time to ask everyone their opinion on a topic, nor do we have the ability to look at every interaction in the social world. We need a type of causal explanation that helps us predict and estimate truth in all situations.

If these still seem like obscure philosophy terms, let’s consider an example. Imagine you are working for a community-based non-profit agency serving people with disabilities. You are putting together a report to help lobby the state government for additional funding for community support programs, and you need to support your argument for additional funding at your agency. If you looked at nomothetic research, you might learn how previous studies have shown that, in general, community-based programs like yours are linked with better health and employment outcomes for people with disabilities. Nomothetic research seeks to explain that community-based programs are better for everyone with disabilities. If you looked at idiographic research, you would get stories and experiences of people in community-based programs. These individual stories are full of detail about the lived experience of being in a community-based program. Using idiographic research, you can understand what it’s like to be a person with a disability and then communicate that to the state government. For example, a person might say “I feel at home when I’m at this agency because they treat me like a family member” or “this is the agency that helped me get my first paycheck.”

Neither kind of causal explanation is better than the other. A decision to conduct idiographic research means that you will attempt to explain or describe your phenomenon exhaustively, attending to cultural context and subjective interpretations. A decision to conduct nomothetic research, on the other hand, means that you will try to explain what is true for everyone and predict what will be true in the future. In short, idiographic explanations have greater depth, and nomothetic explanations have greater breadth. More importantly, social workers understand the value of both approaches to understanding the social world. A social worker helping a client with substance abuse issues seeks idiographic knowledge when they ask about that client’s life story, investigate their unique physical environment, or probe how they understand their addiction. At the same time, a social worker also uses nomothetic knowledge to guide their interventions. Nomothetic research may help guide them to minimize risk factors and maximize protective factors or use an evidence-based therapy, relying on knowledge about what in general helps people with substance abuse issues.

Nomothetic causal explanations

If you are trying to generalize about causality, or create a nomothetic causal explanation, then the rest of these statements are likely to be true: you will use quantitative methods, reason deductively, and engage in explanatory research. How can we make that prediction? Let’s take it part by part.

Because nomothetic causal explanations try to generalize, they must be able to reduce phenomena to a universal language, mathematics. Mathematics allows us to precisely measure, in universal terms, phenomena in the social world. Because explanatory researchers want a clean “x causes y” explanation, they need to use the universal language of mathematics to achieve their goal. That’s why nomothetic causal explanations use quantitative methods.  It’s helpful to note that not all quantitative studies are explanatory. For example, a descriptive study could reveal the number of people without homes in your county, though it won’t tell you why they are homeless. But nearly all explanatory studies are quantitative.

What we’ve been talking about here is an association between variables. When one variable precedes or predicts another, we have what researchers call independent and dependent variables. Two variables can be associated without having a causal relationship.  However, when certain conditions are met (which we describe later in this chapter), the independent variable is considered as a “ cause ” of the dependent variable.  For our example on spanking and aggressive behavior, spanking would be the independent variable and aggressive behavior addiction would be the dependent variable.  In causal explanations, the  independent variable is the cause, and the dependent variable is the effect.  Dependent variables depend on independent variables. If all of that gets confusing, just remember this graphical depiction:

The strength of the association between the independent variable and dependent variable is another important factor to take into consideration when attempting to make causal claims when your research approach is nomothetic.  In this context, strength refers to statistical significance . When the  association between two variables is shown to be statistically significant, we can have greater confidence that the data from our sample reflect a true association between those variables in the target population. Statistical significance is usually represented in statistics as the p- value .  Generally a p -value of .05 or less indicates the association between the two variables is statistically significant.

A hypothesis is a statement describing a researcher’s expectation regarding the research findings. Hypotheses in quantitative research are nomothetic causal explanations that the researcher expects to demonstrate. Hypotheses are written to describe the expected association between the independent and dependent variables. Your prediction should be taken from a theory or model of the social world. For example, you may hypothesize that treating clinical clients with warmth and positive regard is likely to help them achieve their therapeutic goals. That hypothesis would be using the humanistic theories of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. If Rogers’ theory of unconditional positive regard is accurate, your hypothesis should be true.

Let’s consider a couple of examples. In research on sexual harassment (Uggen & Blackstone, 2004), one might hypothesize, based on feminist theories of sexual harassment, that more females than males will experience specific sexually harassing behaviors. What is the causal explanation being predicted here? Which is the independent and which is the dependent variable? In this case, we hypothesized that a person’s gender (independent variable) would predict their likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that an association will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the association between age and support for legalization of marijuana. Perhaps you’ve taken a sociology class and, based on the theories you’ve read, you hypothesize that age is negatively related to support for marijuana legalization. In fact, there are empirical data that support this hypothesis. Gallup has conducted research on this very question since the 1960s (Carroll, 2005). What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus, as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). So, positive associations involve two variables going in the same direction and negative associations involve two variables going in opposite directions. If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

It’s important to note that once a study starts, it is unethical to change your hypothesis to match the data that you found. For example, what happens if you conduct a study to test the hypothesis from Figure 4.3 on support for marijuana legalization, but you find no association between age and support for legalization? It means that your hypothesis was wrong, but that’s still valuable information. It would challenge what the existing literature says on your topic, demonstrating that more research needs to be done to figure out the factors that impact support for marijuana legalization. Don’t be embarrassed by negative results, and definitely don’t change your hypothesis to make it appear correct all along!

Establishing causality in nomothetic research

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite. You’ve only established one of the criteria for causality. The main criteria for causality have to do with covariation, plausibility, temporality, and spuriousness. In our example from Figure 4.3, we have established only one criteria—covariation. When variables covary , they vary together. Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization and they vary together in a patterned way–when age increases, support for legalization decreases.

Just because there might be some correlation between two variables does not mean that a causal explanation between the two is really plausible. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. It makes sense that people from previous generations would have different attitudes towards marijuana than younger generations. People who grew up in the time of Reefer Madness or the hippies may hold different views than those raised in an era of legalized medicinal and recreational use of marijuana.

Once we’ve established that there is a plausible association between the two variables, we also need to establish that the cause happened before the effect, the criterion of temporality . A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization.

Finally, scientists must establish nonspuriousness. A spurious association is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. For example, we could point to the fact that older cohorts are less likely to have used marijuana. Maybe it is actually use of marijuana that leads people to be more open to legalization, not their age. This is often referred to as the third variable problem, where a seemingly true causal explanation is actually caused by a third variable not in the hypothesis. In this example, the association between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious associations. They are an important form of critique of scientific work. As a result, they will often measure these third variables in their study, so they can control for their effects. These are called control variables , and they refer to variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.” Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.” Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. The association between age and support for marijuana legalization is still statistically significant and is the most important association here.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive association between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993).

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011). In each of these examples, it is the presence of a third variable that explains the apparent association between the two original variables.

In sum, the following criteria must be met for a correlation to be considered causal:

• The two variables must vary together.
• The association must be plausible.
• The cause must precede the effect in time.
• The association must be nonspurious (not due to a third variable).

Once these criteria are met, there is a nomothetic causal explanation, one that is objectively true. However, this is difficult for researchers to achieve. You will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a association has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not be true. Instead, researchers tend to say that their hypotheses have been supported (or not). This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining an association will be discovered. Researchers may also discuss a null hypothesis. The null hypothesis is one that predicts no association between the variables being studied. If a researcher fails to accept the null hypothesis, she is saying that the variables in question are likely to be related to one another.

Idiographic causal explanations

If you not trying to generalize, but instead are trying to establish an idiographic causal explanation, then you are likely going to use qualitative methods, reason inductively, and engage in exploratory or descriptive research. We can understand these assumptions by walking through them, one by one.

Researchers seeking idiographic causal explanation are not trying to generalize, so they have no need to reduce phenomena to mathematics. In fact, using the language of mathematics to reduce the social world down is a bad thing, as it robs the causality of its meaning and context. Idiographic causal explanations are bound within people’s stories and interpretations. Usually, these are expressed through words. Not all qualitative studies analyze words, as some can use interpretations of visual or performance art, but the vast majority of social science studies do.

But wait, we predicted that an idiographic causal explanation would use descriptive or exploratory research. How can we build causality if we are just describing or exploring a topic? Wouldn’t we need to do explanatory research to build any kind of causal explanation?  To clarify, explanatory research attempts to establish nomothetic causal explanations—an independent variable is demonstrated to cause changes a dependent variable. Exploratory and descriptive qualitative research are actually descriptions of the causal explanations established by the participants in your study. Instead of saying “x causes y,” your participants will describe their experiences with “x,” which they will tell you was caused by and influenced a variety of other factors, depending on time, environment, and subjective experience. As stated before, idiographic causal explanations are messy. The job of a social science researcher is to accurately identify patterns in what participants describe.

Let’s consider an example. What would you say if you were asked why you decided to become a social worker?  If we interviewed many social workers about their decisions to become social workers, we might begin to notice patterns. We might find out that many social workers begin their careers based on a variety of factors, such as: personal experience with a disability or social injustice, positive experiences with social workers, or a desire to help others. No one factor is the “most important factor,” like with nomothetic causal explanations. Instead, a complex web of factors, contingent on context, emerge in the dataset when you interpret what people have said.

Finding patterns in data, as you’ll remember from Chapter 2, is what inductive reasoning is all about. A qualitative researcher collects data, usually words, and notices patterns. Those patterns inform the theories we use in social work. In many ways, the idiographic causal explanations created in qualitative research are like the social theories we reviewed in Chapter 2  and other theories you use in your practice and theory courses. Theories are explanations about how different concepts are associated with each other how that network of associations works in the real world. While you can think of theories like Systems Theory as Theory (with a capital “T”), inductive causality is like theory with a small “t.” It may apply only to the participants, environment, and moment in time in which the data were gathered. Nevertheless, it contributes important information to the body of knowledge on the topic studied.

Unlike nomothetic causal explanations, there are no formal criteria (e.g., covariation) for establishing causality in idiographic causal explanations. In fact, some criteria like temporality and nonspuriousness may be violated. For example, if an adolescent client says, “It’s hard for me to tell whether my depression began before my drinking, but both got worse when I was expelled from my first high school,” they are recognizing that oftentimes it’s not so simple that one thing causes another. Sometimes, there is a reciprocal association where one variable (depression) impacts another (alcohol abuse), which then feeds back into the first variable (depression) and also into other variables (school). Other criteria, such as covariation and plausibility still make sense, as the associations you highlight as part of your idiographic causal explanation should still be plausibly true and it elements should vary together.

Similarly, idiographic causal explanations differ in terms of hypotheses. If you recall from the last section, hypotheses in nomothetic causal explanations are testable predictions based on previous theory. In idiographic research, instead of predicting that “x will decrease y,” researchers will use previous literature to figure out what concepts might be important to participants and how they believe participants might respond during the study. Based on an analysis of the literature a researcher may formulate a few tentative hypotheses about what they expect to find in their qualitative study. Unlike nomothetic hypotheses, these are likely to change during the research process. As the researcher learns more from their participants, they might introduce new concepts that participants talk about. Because the participants are the experts in idiographic causal explanation, a researcher should be open to emerging topics and shift their research questions and hypotheses accordingly.

Complementary approaches to causality

Over time, as more qualitative studies are done and patterns emerge across different studies and locations, more sophisticated theories emerge that explain phenomena across multiple contexts. In this way, qualitative researchers use idiographic causal explanations for theory building or the creation of new theories based on inductive reasoning. Quantitative researchers, on the other hand, use nomothetic causal explanations for theory testing , wherein a hypothesis is created from existing theory (big T or small t) and tested mathematically (i.e., deductive reasoning).  Once a theory is developed from qualitative data, a quantitative researcher can seek to test that theory. In this way, qualitatively-derived theory can inspire a hypothesis for a quantitative research project.

Two different baskets

Idiographic and nomothetic causal explanations form the “two baskets” of research design elements pictured in Figure 4.4 below. Later on, they will also determine the sampling approach, measures, and data analysis in your study.

In most cases, mixing components from one basket with the other would not make sense. If you are using quantitative methods with an idiographic question, you wouldn’t get the deep understanding you need to answer an idiographic question. Knowing, for example, that someone scores 20/35 on a numerical index of depression symptoms does not tell you what depression means to that person. Similarly, qualitative methods are not often used to deductive reasoning because qualitative methods usually seek to understand a participant’s perspective, rather than test what existing theory says about a concept.

However, these are not hard-and-fast rules. There are plenty of qualitative studies that attempt to test a theory. There are fewer social constructionist studies with quantitative methods, though studies will sometimes include quantitative information about participants. Researchers in the critical paradigm can fit into either bucket, depending on their research question, as they focus on the liberation of people from oppressive internal (subjective) or external (objective) forces.

We will explore later on in this chapter how researchers can use both buckets simultaneously in mixed methods research. For now, it’s important that you understand the logic that connects the ideas in each bucket. Not only is this fundamental to how knowledge is created and tested in social work, it speaks to the very assumptions and foundations upon which all theories of the social world are built!

Key Takeaways

• Idiographic research focuses on subjectivity, context, and meaning.
• Nomothetic research focuses on objectivity, prediction, and generalizing.
• In qualitative studies, the goal is generally to understand the multitude of causes that account for the specific instance the researcher is investigating.
• In quantitative studies, the goal may be to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance.
• For nomothetic causal explanations, an association must be plausible and nonspurious, and the cause must precede the effect in time.
• In a nomothetic causal explanations, the independent variable causes changes in a dependent variable.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about an association between two or more variables.
• Qualitative research may create theories that can be tested quantitatively.
• The choice of idiographic or nomothetic causal explanation requires a consideration of methods, paradigm, and reasoning.
• Depending on whether you seek a nomothetic or idiographic causal explanation, you are likely to employ specific research design components.
• Causality-the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief
• Control variables- potential “third variables” effects are controlled for mathematically in the data analysis process to highlight the relationship between the independent and dependent variable
• Covariation- the degree to which two variables vary together
• Dependent variable- a variable that depends on changes in the independent variable
• Generalize- to make claims about a larger population based on an examination of a smaller sample
• Hypothesis- a statement describing a researcher’s expectation regarding what she anticipates finding
• Idiographic research- attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants
• Independent variable- causes a change in the dependent variable
• Nomothetic research- provides a more general, sweeping explanation that is universally true for all people
• Plausibility- in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense
• Spurious relationship- an association between two variables appears to be causal but can in fact be explained by some third variable
• Statistical significance- confidence researchers have in a mathematical relationship
• Temporality- whatever cause you identify must happen before the effect
• Theory building- the creation of new theories based on inductive reasoning
• Theory testing- when a hypothesis is created from existing theory and tested mathematically

Image attributions

Mikado by 3dman_eu CC-0

Weather TV Forecast by mohamed_hassan CC-0

Figures 4.2 and 4.3 were copied from Blackstone, A. (2012) Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. Retrieved from: https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ Shared under CC-BY-NC-SA 3.0 License

Beatrice Birra Storytelling at African Art Museum by Anthony Cross public domain

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.3 Inductive and deductive reasoning

Learning objectives.

• Describe the inductive approach to research, and provide examples of inductive research
• Describe the deductive approach to research, and provide examples of deductive research
• Describe the ways that inductive and deductive approaches may be complementary

Theories structure and inform social work research. So, too, does research structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students new to these topics when they consider the relationships between theory and research in inductive and deductive approaches to research. In both cases, theory is crucial. But the relationship between theory and research differs for each approach.

Inductive and deductive approaches to research are quite different, but they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive approaches and some examples

In an inductive approach to research, a researcher begins by collecting data that is relevant to her topic of interest. Once a substantial amount of data have been collected, the researcher will then take a breather from data collection, stepping back to get a bird’s eye view of their data. At this stage, the researcher looks for patterns in the data, working to develop a theory that could explain those patterns. Thus, when researchers take an inductive approach, they start with a set of observations and then they move from those particular experiences to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 6.1 outlines the steps involved with an inductive approach to research.

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating study in which the researchers took an inductive approach is Katherine Allen, Christine Kaestle, and Abbie Goldberg’s (2011) study of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation, that menstruation makes boys feel somewhat separated from girls, and that as they enter young adulthood and form romantic relationships, young men develop more mature attitudes about menstruation. Note how this study began with the data—men’s narratives of learning about menstruation—and tried to develop a theory.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011) analyzed empirical data to better understand how best to meet the needs of young people who are homeless. The authors analyzed data from focus groups with 20 young people at a homeless shelter. From these data they developed a set of recommendations for those interested in applied interventions that serve homeless youth. The researchers also developed hypotheses for people who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test the hypotheses that they developed from their analysis, their study ends where most deductive investigations begin: with a theory and a hypothesis derived from that theory.

Deductive approaches and some examples

Researchers taking a deductive approach take the steps described earlier for inductive research and reverse their order. They start with a social theory that they find compelling and then test its implications with data. That is, they move from a more general level to a more specific one. A deductive approach to research is the one that people typically associate with scientific investigation. The researcher studies what others have done, reads existing theories of whatever phenomenon she is studying, and then tests hypotheses that emerge from those theories. Figure 2.2 outlines the steps involved with a deductive approach to research.

While not all researchers follow a deductive approach, as you have seen in the preceding discussion, many do, and there are a number of excellent recent examples of deductive research. We’ll take a look at a couple of those next.

In a study of United States law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009) hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from their reading of prior research and theories on the topic. They tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis. One might associate this research with critical theory.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011) studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and even heat, would be associated with emotional and behavioral problems in children. One might associate this research with systems theory. The researchers found support for their hypothesis, demonstrating that policymakers should probably be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011).

Complementary approaches

While inductive and deductive approaches to research seem quite different, they can actually be rather complementary. In some cases, researchers will plan for their study to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to only conduct either inductive or deductive research, but then discovers along the way that the other approach is needed to help illuminate findings.

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s Investigating the Social World (2006). As Schutt describes, researchers Lawrence Sherman and Richard Berk (1984) conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence). Specifically, Sherman and Berk hypothesized that deterrence theory would provide a better explanation of the effects of arresting accused batterers than labeling theory . Deterrence theory predicts that arresting an accused spouse batterer will reduce future incidents of violence. Conversely, labeling theory predicts that arresting accused spouse batterers will increase future incidents. Figure 2.3 summarizes the two competing theories and the predictions that Sherman and Berk set out to test.

Sherman and Berk found, after conducting an experiment with the help of local police in one city, that arrest did in fact deter future incidents of violence, thus supporting their hypothesis that deterrence theory would better predict the effect of arrest. After conducting this research, they and other researchers did what is called replication and went on to conduct similar experiments in six additional cities (Berk, Campbell, Klap, & Western, 1992; Pate & Hamilton, 1992; Sherman & Smith, 1992). Results from these follow-up studies were mixed. In some cases, arrest deterred future incidents of violence. In other cases, it did not. This left the researchers with new data that they needed to explain. The researchers therefore took an inductive approach in an effort to make sense of their latest empirical observations. The new studies revealed that arrest seemed to have a deterrent effect for those who were married and employed, but that it led to increased offenses for those who were unmarried and unemployed. Researchers thus turned to control theory, which predicts that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation.

What the Sherman and Berk research, along with the follow-up studies, shows us is that we might start with a deductive approach to research, but then, if confronted by new data that we must make sense of, we may move to an inductive approach.

Key Takeaways

• The inductive approach begins with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
• The deductive approach begins with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test those hypotheses.
• Inductive and deductive approaches to research can be employed together for a more complete understanding of the topic that a researcher is studying.
• Though researchers don’t always set out to use both inductive and deductive strategies in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.
• Deductive approach- study what others have done, reads existing theories of whatever phenomenon she is studying, and then tests hypotheses that emerge from those theories
• Inductive approach- start with a set of observations and then move from particular experiences to a more general set of propositions about those experiences

Image Attributions

All figures in this section are copied from Blackstone, A. (2012) Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. Retrieved from: https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ Shared under CC-BY-NC-SA 3.0 License

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

2.1 Approaches to Sociological Research

Learning objectives.

By the end of this section, you should be able to:

• Define and describe the scientific method.
• Explain how the scientific method is used in sociological research.
• Describe the function and importance of an interpretive framework.
• Describe the differences in accuracy, reliability and validity in a research study.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behavior is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behavior as people move through that world. Using sociological methods and systematic research within the framework of the scientific method and a scholarly interpretive perspective, sociologists have discovered social patterns in the workplace that have transformed industries, in families that have enlightened family members, and in education that have aided structural changes in classrooms.

Sociologists often begin the research process by asking a question about how or why things happen in this world. It might be a unique question about a new trend or an old question about a common aspect of life. Once the question is formed, the sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a scientific approach or an interpretive framework. The following sections describe these approaches to knowledge.

The Scientific Method

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 social 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 six prescribed steps that have been established over centuries of scientific scholarship.

Sociological research does not reduce knowledge to right or wrong facts. Results of studies tend to provide people with insights they did not have before—explanations of human behaviors and social practices and access to knowledge of other cultures, rituals and beliefs, or trends and attitudes.

In general, sociologists tackle questions about the role of social characteristics in outcomes or results. 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 often look between the cracks to discover obstacles to meeting basic human needs. They might also 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, social 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 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 does not 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 collecting and analyzing data in research studies.

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 has 6 steps which are described below.

Step 1: Ask a Question or Find a Research Topic

The first step of the scientific method is to ask a question, select a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geographic location 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. Sociologists strive to frame questions that examine well-defined patterns and relationships.

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?”

Step 2: Review the Literature/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, a thorough online search, and a survey of academic journals will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted, identify gaps in understanding of the topic, and 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 crime, a researcher might also sort through existing data from the court system, police database, prison information, interviews with criminals, guards, wardens, etc. It’s important to examine this information in addition to existing research to determine how these resources might be used to fill holes in existing knowledge. Reviewing existing sources educates researchers and helps refine and improve a research study design.

Step 3: Formulate a Hypothesis

A hypothesis is an explanation for a phenomenon based on a conjecture about the relationship between the phenomenon and one or more causal factors. In sociology, the hypothesis will often predict how one form of human behavior influences another. For example, a hypothesis might be in the form of an “if, then statement.” Let’s relate this to our topic of crime: If unemployment increases, then the crime rate will increase.

In scientific research, we formulate hypotheses to include an independent variables (IV) , which are the cause of the change, and a dependent variable (DV) , which is the effect , or thing that is changed. In the example above, unemployment is the independent variable and the crime rate is the dependent variable.

In a sociological study, the researcher would establish one form of human behavior as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)?

Taking an example from Table 12.1, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Note, however, this hypothesis can also work the other way around. A sociologist might predict that increasing a child’s sense of self-esteem (the independent variable) will increase or improve habits of hygiene (now the dependent variable). Identifying the independent and dependent variables is very important. As the hygiene example shows, simply identifying related two topics or variables is not enough. Their prospective relationship must be part of the hypothesis.

Step 4: Design and Conduct a Study

Researchers design studies to maximize 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 or what will happen in one situation will happen in another. Cooking is a science. When you follow a recipe and measure ingredients with a cooking tool, such as a measuring cup, the same results is obtained as long as the cook follows the same recipe and uses the same type of tool. The measuring cup introduces accuracy into the process. If a person uses a less accurate tool, such as their hand, to measure ingredients rather than a cup, the same result may not be replicated. Accurate tools and methods increase reliability.

Researchers also strive for validity , which refers to how well the study measures what it was designed to measure. To produce reliable and valid results, sociologists develop an operational definition , that is, they define each concept, or variable, 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 the concept, all researchers can collect data in a systematic or replicable manner. Moreover, researchers can determine whether the experiment or method validly represent the phenomenon they intended to study.

A study asking how tutoring improves grades, for instance, might define “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” However, one researcher might define a “good” grade as a C or better, while another uses a B+ as a starting point for “good.” For the results to be replicated and gain acceptance within the broader scientific community, researchers would have to use a standard operational definition. These definitions set limits and establish cut-off points that ensure consistency and replicability in a study.

We will explore research methods in greater detail in the next section of this chapter.

Step 5: Draw Conclusions

After constructing the research design, sociologists collect, tabulate or categorize, and analyze data to formulate conclusions. If the analysis supports the hypothesis, researchers can discuss the implications of the results for the theory or policy solution that they were addressing. If the analysis does not support the hypothesis, researchers may consider repeating the experiment or think of ways to improve their procedure.

However, even when results contradict a sociologist’s prediction of a study’s outcome, these results still contribute to sociological understanding. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns. In a study of education, a researcher might predict that high school dropouts have a hard time finding rewarding careers. While many assume that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results may substantiate or contradict it.

Sociologists carefully keep in mind how operational definitions and research designs impact the results as they draw conclusions. Consider the concept of “increase of crime,” which might be defined as the percent increase in crime from last week to this week, as in the study of Swedish crime discussed above. Yet the data used to evaluate “increase of crime” might be limited by many factors: who commits the crime, where the crimes are committed, or what type of crime is committed. If the data is gathered for “crimes committed in Houston, Texas in zip code 77021,” then it may not be generalizable to crimes committed in rural areas outside of major cities like Houston. If data is collected about vandalism, it may not be generalizable to assault.

Step 6: Report Results

Researchers report their results at conferences and in academic journals. These results are then subjected to the scrutiny of other sociologists in the field. Before the conclusions of a study become widely accepted, the studies are often repeated in the same or different environments. In this way, sociological theories and knowledge develops as the relationships between social phenomenon are established in broader contexts and different circumstances.

Interpretive Framework

While many sociologists rely on empirical data and the scientific method as a research approach, others operate from an interpretive framework . While systematic, this approach doesn’t follow the hypothesis-testing model that seeks to find generalizable results. Instead, an interpretive framework, sometimes referred to as an interpretive perspective , seeks to understand social worlds from the point of view of participants, which leads to in-depth knowledge or understanding about the human experience.

Interpretive research is generally more descriptive or narrative in its findings. Rather than formulating a hypothesis and method for testing it, an interpretive researcher will develop approaches to explore the topic at hand that may involve a significant amount of direct observation or interaction with subjects including storytelling. This type of researcher learns through the process and sometimes adjusts the research methods or processes midway to optimize findings as they evolve.

Critical Sociology

Critical sociology focuses on deconstruction of existing sociological research and theory. Informed by the work of Karl Marx, scholars known collectively as the Frankfurt School proposed that social science, as much as any academic pursuit, is embedded in the system of power constituted by the set of class, caste, race, gender, and other relationships that exist in the society. Consequently, it cannot be treated as purely objective. Critical sociologists view theories, methods, and the conclusions as serving one of two purposes: they can either legitimate and rationalize systems of social power and oppression or liberate humans from inequality and restriction on human freedom. Deconstruction can involve data collection, but the analysis of this data is not empirical or positivist.

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• / Risk assessment
• / The STEPWISE Approach
• / Step One: Hypothesise

Step One: Hypothesise

The word hypothesis has its origins in ancient Greek and means ' a proposed explanation for a phenomenon' (Wikipedia - online dictionary). In modern day usage, a hypothesis is a provisional idea or explanation which has to be evaluated or tested. The idea needs to be either confirmed or disproved. The hypothesis should be 'falsifiable', which means it is possible for it to be shown to be false, usually by observation. Even if confirmed, the hypothesis is not necessarily proven, but remains provisional.

Hypothesising is a core activity within social work assessment. Holland (2004) states:

"The cornerstone of analysis in assessment work might be seen as the process of building hypotheses for understanding a family situation and developing these until they include a plan for the way forward ."

This process of building, testing out and discarding hypotheses starts at the earliest point of contact. As soon as a referral is received into a social work team the practitioner will begin consciously or unconsciously to form some hypotheses of what is happening within the family. They would certainly check out some of their hypotheses during an initial conversation with the referrer and may even ditch one or more of them at this stage. The formation of various hypotheses and the decision taken about the steps needed to investigate the matter further will be influenced by a range of factors, for example: practice wisdom, personal values, and formal knowledge.

Munro highlights the fact that " The single most important factor in minimizing errors (in child protection practice) is to admit that you may be wrong" (Munro 2008: 125).

In risk assessment Raynes in Calder and others (2003) suggests that workers often remain narrowly focused on proving or disproving whether the original risk or perception about a family remains and fail to consider the broader picture, or alternative hypotheses about what is happening and why. Practitioners should therefore consider all the possibilities about what is happening and address each hypothesis, only discarding it when there is clear evidence to do so.

Stepwise requires that this is considered as part of a structured approach and that forming, testing out and discarding hypotheses needs to be a clear and recorded part of any assessment process.

The practitioner should record the possible hypotheses to which they are working and this needs to be done in a way that shows a) it's only a hypothesis not a conclusion, and b) that it's a reasonable hypothesis based on information to hand at that time (including research info) in order to avoid any later suggestion of bias/premature judgement. Planning the nature and source of information to be collected, should enable practitioners and managers to test out all possible hypotheses in the analysis stage, to prove or disprove the likelihood of one of them being the case in this situation. This will require use of the analysis models underpinning this framework.

In essence, at this step, practitioners should be asking:  "What are we worried about? What is the possible danger or harm to the child?" If our hypotheses are correct, what needs to happen?"

Where hypotheses relate to actual or likely abuse of a child, the child protection procedures must be followed, and the assessment planned as part of a strategy discussion or meeting.

Quick links

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• Child protection investigation
• Medical examinations
• Initial child protection case conference
• Conference agenda
• Involvement of parents and carers - at conference
• Involvement of children at conference
• Core Assessment
• Looked After Children
• Missing Children
• Children Moving Away from Guernsey
• Child Death Review
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Hypotheses: meaning, types and sources | social research.

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After reading this article you will learn about:- 1. Meaning of Hypotheses 2. Types of Hypotheses 3. Sources.

Meaning of Hypotheses:

Once the problem to be answered in the course of research is finally instituted, the researcher may, if feasible proceed to formulate tentative solutions or answers to it. These proposed solutions or explanations are called hypotheses which the researcher is obliged to test on the basis of fact already known or which can be made known.

If such answers are not formulated, even implicitly, the researcher cannot effectively go ahead with the investigation of his problem because, in the absence of direction which hypotheses typically provide, the researcher would not know what facts to look for and what relation or order to search for amongst them.

The hypotheses guide the researcher through a bewildering Jungle of facts to see and select only those that are relevant to the problem or difficulty he proposes to solve. Collection of facts merely for the sake of collecting them will yield no fruits.

To be fruitful, one should collect such facts as are for or against some point of view or proposition. Such a point of view or proposition is the hypothesis. The task of the inquiry or research is to test its accord with facts.

Lundberg aptly observes, “The only difference between gathering data without a hypothesis and gathering them with one, is that in the latter case, we deliberately recognize the limitations of our senses and attempt to reduce their fallibility by limiting our field of investigation so as to prevent greater concentration for attention on particular aspects which past experience leads us to believe are irrelevant as insignificant for our purpose.”

Simply stated, an hypothesis helps us see and appreciate:

(1) The kind of data that need be collected in order to answer the research question and

(2) The way in which they should be organized most efficiently and meaningfully.

Webster’s New International Dictionary of English Language, 1956, defines the term “hypothesis” as “proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined.”

Cohen and Nagel bring out the value of hypothesis thus:

“We cannot take a single step forward in any inquiry unless we begin with a suggested explanation or solution of the difficulty which originated it. Such tentative explanations are suggested to us by something in the subject-matter and by our previous knowledge. When they are formulated as propositions, they are called hypotheses.”

Once the scientist knows what his question (problem) is, he can make a guess, or a number of guesses as to its possible answers. According to Werkmeister, “The guesses he makes are the hypotheses which either solve the problems or guide him in further investigation.”

It is clear now that a hypothesis is a provisional formulation; a tentative solution of the problem posed by the scientist. ‘The scientist starts by assuming that the solution is true without, of course, personally believing in its truthfulness.

Based on this assumption, the scientist anticipates that certain logical consequences will be observed on the plane of observable events or objects. Whether these anticipations or expectations really materialize is the test of the hypothesis, its proof or disproof.

If the hypothesis is proved, the problem of which it was a tentative solution is answered. If it is not proved, i.e., falsified owing to non-support of proof, alternative hypotheses may be formulated by the researcher. An hypothesis thus stands somewhere at the midpoint of research; from here, one can look back to the problem as also look forward to data.

The hypothesis may be stated in the form of a principle, that is, the tentative explanation or solution to the questions how? Or why? May be presented in the form of a principle that X varies with Y. The inquiry established that an empirical referent of X varies with the empirical referent of Y in a concrete observable situation (i.e., the hypothesis is proved) then the question is answered.

Hypotheses, however, may take other forms, such as intelligent guesses, conditions, propositions deduced from theories, observations and findings of other scholars etc.

Proceeding on the basis of hypotheses has been the slow and hard way of science. While some scientific conclusions and premises seem to have arisen in the mind of the investigator as if by flashes of insight, in a majority of cases the process of discovery has been a slower one.

“The scientific imagination devises a possible solution, a hypothesis and the investigator proceeds to test it. He makes intellectual keys and then tries to see whether they fit the lock. If the hypothesis does not fit, it is rejected and another is made. The scientific workshop is full of discarded keys.”

Cohen and Nagel’s statement that one cannot take a single step forward in any inquiry without a hypothesis may well be a correct statement of the value of hypothesis in scientific investigation generally, but it hardly does justice to an important function of scientific research, i.e., the “formulation hypotheses.”

Hypotheses are not given to us readymade. Of course in fields with a highly developed theoretic structure it is reasonable to expect that most empirical studies will have at least some sharp hypotheses to be tested.

This is so especially in social sciences where there has not yet evolved a highly developed theoretic system in many areas of its subject-matter which can afford fruitful bases for hypothesis-formulation.

As such, attempts to force research into this mould are either deceitful or stultifying and hypotheses are likely to be no more than hunches as to where to look for sharper hypotheses in which case the study may be described as an intelligent fishing trip.

As a result, in the social sciences at least, a considerable quantum of research endeavour is directed understandably toward ‘making’ hypotheses rather than at testing them.

A very important type of research has as its goal, the formulation of significant hypotheses relating to a particular problem. Hence, we will do well to bear in mind that research can begin with well formulated hypotheses or it may come out with hypotheses as its end product.

Let us recapitulate the role of hypotheses for research in the words of Chaddock who summarizes it thus:

“(A hypothesis) in the scientific sense is … an explanation held after careful canvass of known facts, in full knowledge of other explanations that have been offered and with a mind open to change of view, if the facts disclosed by the inquiry warrant a different explanation. Another hypothesis as an explanation is proposed including investigation all available and pertinent data either to prove or disprove the hypothesis…. (A hypothesis) gives point to the inquiry and if founded on sufficient previous knowledge, guides the line of investigation. Without it much useless data maybe collected in the hope that nothing essential will be omitted or important data may be omitted which could have been easily included if the purpose of inquiry had been more clearly defined” and thus hypotheses are likely to be no more than hunches as to where to look for pertinent data.

An hypothesis is therefore held with the definite purpose of including in the investigating all available and pertinent data either to prove or disprove the hypothesis.

Types of Hypotheses :

There are many kinds of hypotheses the social researcher has to be working with. One type of hypotheses asserts that something is the case in a given instance; that a particular object, person or situation has a particular characteristic.

Another type of hypotheses deals with the frequency of occurrences or of association among variables; this type of hypotheses may state that X is associated with y a certain (Y) proportion of times, e.g., that urbanism tends to be accompanied by mental disease or that something is greater or lesser than some thing else in a specific setting.

Yet another type of hypotheses assert that a particular characteristic is one of the factors which determine another characteristic, i.e., S is the producer of Y (product). Hypotheses of this type are known as causal hypotheses.

Hypotheses can be classified in a variety of ways. But classification of hypotheses on the basis of their levels of abstraction is regarded as especially fruitful. Goode arid Hatt have identified three differential levels of abstraction reached by hypotheses. We shall here be starting from the lowest level of abstraction and go over to the higher ones.

(a) At the lowest level of abstraction are the hypotheses which state existence of certain empirical uniformities. Many types of such empirical uniformities are common in social research, for instance, it may be hypothesized with reference to India that in the cities men will get married between the age of 22 and 24 years.

Or, the hypotheses of this type may state that certain behaviour pattern may be expected in a specified community. Thus, hypotheses of this type frequently seem to invite scientific verification of what are called “common sense propositions,” indeed without much justification.

It has often been said by way of a criticism of such hypotheses that these are not useful in as much as they merely state what everyone seems to know already. Such an objection may however be overruled by pointing out that what everyone knows is not often put in precise terms nor is it adequately integrated into the framework of science.

Secondly, what everyone knows may well be mistaken. To put common sense ideas into precisely defined concepts and subject the proposition to test is an important task of science.

This is particularly applicable to social sciences which are at present in their earlier stage of development. Not only social science but all sciences have found such commonsense knowledge a fruitful item of study. It was commonsense knowledge in the olden days that sun revolved round the earth. But this and many other beliefs based on commonsense have been exploded by patient, plodding, empirical checking of facts.

The monumental work, The American Soldier by Stouffer and associates was criticized in certain quarters, for it was according to them mere elaboration of the obvious. But to this study goes the credit of exploding some of the commonsense propositions and shocking many people who had never thought that what was so obvious a commonsense could be totally wrong or unfounded in fact.

(b) At a relatively higher level of abstraction are hypotheses concerned with complex ‘ideal types.’ These hypotheses aim at testing whether logically derived relationship between empirical uniformities obtain. This level of hypothesizing moves beyond the level of anticipating a simple empirical uniformity by visualizing a complex referent in society.

Such hypotheses are indeed purposeful distortions of empirical exactness and owing to their remoteness from empirical reality, these constructs are termed ‘ideal types.’ The function of such hypotheses is to create tools and formulate problems for further research in complex areas of investigation.

An example of one such hypothesis may be cited. Analyses of minority groups brought to light empirical uniformities in the behaviour of members of a wide variety of minorities. It was subsequently hypothesized that these uniformities pointed to an ‘ideal type’.

First called by H. A. Miller the ‘oppression psychosis,’ this ideal-typical construction was subsequently modified as the ‘Marginal man’ by E. Stone Quist and associates. Empirical evidence marshaled later substantiated the hypothesis, and so the concept of marginality (marginal man) has very much come to stay as a theoretic construct in social sciences, and as part of sociological theory.

(c) We now come to the class of hypotheses at the highest level of abstraction. This category of hypotheses is concerned with the relation obtaining amongst analytic variables. Such hypotheses are statements about, how one property affects other, e.g., a statement of relationship between education and social mobility or between wealth and fertility.

It is easy to see that this level of hypothesizing is not only more abstract compared to others; it is also the most sophisticated and vastly flexible mode of formulation.

This does not mean, however, that this type of hypotheses is ‘superior’ or ‘better’ than the other types. Each type of hypotheses has its own importance depending in turn upon the nature of investigation and the level of development the subject has achieved.

The sophisticated hypotheses of analytical variables owe much of their existence to the building-blocks contributed by the hypotheses existed at the lower orders of abstraction.

Sources of Hypotheses :

Hypotheses may be developed from a variety of sources. We examine here, some of the major ones.

(1) The history of sciences provides an eloquent testimony to the fact that personal and idiosyncratic experiences of the scientist contribute a great deal to type and form of questions he may ask, as also to the kinds of tentative answers to these questions (hypotheses) that he might provide. Some scientists may perceive an interesting pattern in what may merely, seem a jumble of facts to the common man.

The history of science is full of instances of discoveries made just because the ‘right’ person happened to make the ‘right’ observation owing to his characteristic life-history and exposure to a unique mosaic of events. Personal life-histories are a factor in determining the kinds of a person’s perception and conception and this factor may in turn direct him to certain hypotheses quite readily.

An illustration of such individual perspectives in social sciences may be seen in the work of Thorstein Veblen whom Merton describes as a sociologist with a keen eye for the unusual and paradoxical.

A product of an isolated Norwegian community, Veblen lived at a time when the capitalistic system was barely subjected to any criticism. His own community background was replete with derivational experiences attributable to the capitalist system.

Veblen being an outsider, was able to look at the capitalist economic system more objectively and with dispassionate detachment. Veblen was thus strategically positioned to attack the fundamental concepts and postulates of classical economics.

He was an alien who could bring a different experience to bear upon the economic world. Consequently, he made penetrating analyses of society and economy which have ever since profoundly influenced social science.

(2) Analogies are often a fountainhead of valuable hypotheses. Students of sociology and political science in the course of their studies would have come across analogies wherein society and state are compared to a biological organism, the natural law to the social law, thermodynamics to social dynamics, etc. such analogies, notwithstanding the fact that analogies as a class suffer from serious limitations, do provide certain fruitful insight which formulated as hypotheses stimulate and guide inquiries.

One of the recent orientations to hypotheses formulation is provided by cybernetics, the communication models now so well entrenched in the social science testify to the importance of analogies as a source of fruitful hypotheses. The hypothesis that similar human types or activities may be found occupying the same territory was derived from plant ecology.

When the hypothesis was borne out by observations in society, the concept of segregation as it is called in plant ecology was admitted into sociology. It has now become an important idea in sociological theory. Such examples may be multiplied.

In sum, analogy may be very suggestive but care needs to be taken not to accept models from other disciplines without a careful scrutiny of the concepts in terms of their applicability to the new frame of reference in which they are proposed to be deployed.

(3) Hypotheses may rest also on the findings of other studies. The researcher on the basis of the findings of other studies may hypothesize that similar relationship between specified variables will hold good in the present study too. This is a common way of researchers who design their study with a view of replicating another study conducted in a different concrete context or setting.

It was said that many a study in social science is exploratory in character, i.e., they start without explicit hypotheses, the findings of such studies may be formulated as hypotheses for more structured investigations directed at testing certain hypotheses.

(4) An hypothesis may stem from a body of theory which may afford by way of logical deduction, the prediction that if certain conditions are present, certain results will follow. Theory represents what is known; logical deductions from this constitute the hypotheses which must be true if the theory was true.

Dubin aptly remarks, “Hypothesis is the feature of the theoretical model closest to the ‘things observable’ that the theory is trying to model.” Merton illustrates this function of theory with his customary felicity. Basing his deductions on Durham’s theoretic orientation, Merton shows how hypotheses may be derived as deductions from theoretic system.

(1) Social cohesion provides psychic support to group members subjected to acute stresses and anxieties.

(2) Suicide rates are functions of unrelieved anxieties to which persons are subjected.

(3) Catholics have greater social cohesion than protestants.

(4) Therefore, lower suicide rates should be expected among Catholics than among protestants.

If theories purport to model the empirical world, then there must be a linkage between the two. This linkage is to be found in the hypotheses that mirror the propositions of the theoretical model.

It may thus appear that the points of departure vis-a-vis hypotheses-construction are in two opposite directions:

(a) Conclusions based on concrete or empirical observations lead through the process of induction to more abstract hypotheses and

(b) The theoretical model through the process of logical deduction affords more concrete hypotheses.

It may be well to bear in mind, however, that although these two approaches to hypotheses formulation seem diametrically opposed to each other, the two points of departure, i.e., empirical, observations and the theoretical structure, represent the poles of a continuum and hypotheses lie somewhere in the middle of this continuum.

Both these approaches to hypotheses-construction have proved their worth. The Chicago School in American sociology represents a strong empirical orientation whereas the Mertonian and Parsonian approach is typified by a stress on theoretic models as initial bases for hypotheses-construction. Hence hypotheses can be deductively derived from theoretic models.

(5) It is worthy of note that value-orientation of the culture in which a science develops may furnish many of its basic hypotheses.

That certain hypotheses and not others capture the attention of scientists or occur to them in particular societies or culture may well be attributed to the cultural emphases. Goode and Hatt contend that the American emphasis upon personal happiness had had considerable effect upon social science in that country.

The phenomenon of personal happiness has been studied in great detail. In every branch of social science, the problem of personal happiness came to occupy a position meriting central focus. Happiness has been correlated with income, education, occupation, social class, and so on. It is evident that the culture emphasis on happiness has been productive of a very wide range of hypotheses for the American social science.

Folk-wisdom prevalent in a culture may also serve as source of hypotheses. The sum and substance of the discussion is aptly reflected in Larrabee’s remark that the ideal source of fruitful and relevant hypotheses is a fusion of two elements: past experience and imagination in the disciplined mind of the scientist.

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Practice  Questions  – Write short note on Importance and Sources of Hypothesis in Sociological Research. [ UPSC 2008]

Approach –  Introduction, What makes Hypothesis relevant in a sociological research?, What are the sources which aids us to derive hypothesis?, Conclusion

INTRODUCTION

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

We know that research begins with a problem or a felt need or difficulty. The purpose of research is to find a solution to the difficulty. It is desirable that the researcher should propose a set of suggested solutions or explanations of the  difficulty which the research proposes to solve. Such tentative solutions formulated as a proposition are called hypotheses. The suggested solutions formulated as hypotheses may or may not be the real solutions to the problem. Whether they are or not is the task of research to test and establish.

DEFINTITIONS

• Lundberg- A Hypothesis is a tentative generalisation, the validity of which remains to be tested. In its most elementary stages, the hypothesis may be any hunch, guess imaginative idea or Intuition whatsoever which becomes the basis of action or Investigation.
• Bogardus- A Hypothesis is a proposition to be tested.
• Goode and Hatt- It is a proposition which can be put to test to determinants validity.
• P. V. Yaung- The idea of ​a temporary but central importance that becomes the basis of useful research is called a working hypothesis.

TYPES OF HYPOTHESIS

i)  Explanatory Hypothesis : The purpose of this hypothesis is to explain a certain fact. All hypotheses are in a way explanatory for a hypothesis is advanced only when we try to explain the observed fact. A large number of hypotheses are advanced to explain the individual facts in life. A theft, a murder, an accident are examples.

ii) Descriptive Hypothesis:  Some times a researcher comes across a complex phenomenon. He/ she does not understand the relations among the observed facts. But how to account for these facts? The answer is a descriptive hypothesis. A hypothesis is descriptive when it is based upon the points of resemblance of some thing. It describes the cause and effect relationship of a phenomenon e.g., the current unemployment rate of a state exceeds 25% of the work force. Similarly, the consumers of local made products constitute asignificant market segment.

iii) Analogical Hypothesis : When we formulate a hypothesis on the basis of similarities (analogy), it is called an analogical hypothesis e.g., families with higher earnings invest more surplus income on long term investments.

iv) Working hypothesis : Some times certain facts cannot be explained adequately by existing hypotheses, and no new hypothesis comes up. Thus, the investigation is held up. In this situation, a researcher formulates a hypothesis which enables to continue investigation. Such a hypothesis, though inadequate and formulated for the purpose of further investigation only, is called a working hypothesis. It is simply accepted as a starting point in the process of investigation.

v) Null Hypothesis:  It is an important concept that is used widely in the sampling theory. It forms the basis of many tests of significance. Under this type, the hypothesis is stated negatively. It is null because it may be nullified, if the evidence of a random sample is unfavourable to the hypothesis. It is a hypothesis being tested (H0). If the calculated value of the test is less than the permissible value, Null hypothesis is accepted, otherwise it is rejected. The rejection of a null hypothesis implies that the difference could not have arisen due to chance or sampling fluctuations.

USES OF HYPOTHESIS

i) It is a starting point for many a research work. ii) It helps in deciding the direction in which to proceed. iii) It helps in selecting and collecting pertinent facts. iv) It is an aid to explanation. v) It helps in drawing specific conclusions. vi) It helps in testing theories. vii) It works as a basis for future knowledge.

ROLE  OF HYPOTHESIS

In any scientific investigation, the role of hypothesis is indispensable as it always guides and gives direction to scientific research. Research remains unfocused without a hypothesis. Without it, the scientist is not in position to decide as to what to observe and how to observe. He may at best beat around the bush. In the words of Northrop, “The function of hypothesis is to direct our search for order among facts, the suggestions formulated in any hypothesis may be solution to the problem, whether they are, is the task of the enquiry”.

First ,  it is an operating tool of theory. It can be deduced from other hypotheses and theories. If it is correctly drawn and scientifically formulated, it enables the researcher to proceed on correct line of study. Due to this progress, the investigator becomes capable of drawing proper conclusions. In the words of Goode and Hatt, “without hypothesis the research is unfocussed, a random empirical wandering. The results cannot be studied as facts with clear meaning. Hypothesis is a necessary link between theory and investigation which leads to discovery and addition to knowledge.

Secondly,  the hypothesis acts as a pointer to enquiry. Scientific research has to proceed in certain definite lines and through hypothesis the researcher becomes capable of knowing specifically what he has to find out by determining the direction provided by the hypothesis. Hypotheses acts like a pole star or a compass to a sailor with the help of which he is able to head in the proper direction.

Thirdly , the hypothesis enables us to select relevant and pertinent facts and makes our task easier. Once, the direction and points are identified, the researcher is in a position to eliminate the irrelevant facts and concentrate only on the relevant facts. Highlighting the role of hypothesis in providing pertinent facts, P.V. Young has stated, “The use of hypothesis prevents a blind research and indiscriminate gathering of masses of data which may later prove irrelevant to the problem under study”. For example, if the researcher is interested in examining the relationship between broken home and juvenile delinquency, he can easily proceed in the proper direction and collect pertinent information succeeded only when he has succeed in formulating a useful hypothesis.

Fourthly , the hypothesis provides guidance by way of providing the direction, pointing to enquiry, enabling to select pertinent facts and helping to draw specific conclusions. It saves the researcher from the botheration of ‘trial and error’ which causes loss of money, energy and time.

Finally,  the hypothesis plays a significant role in facilitating advancement of knowledge beyond one’s value and opinions. In real terms, the science is incomplete without hypotheses.

STAGES OF HYPOTHESIS TESTING

• EXPERIMENTATION   : Research study focuses its study which is manageable and approachable to it and where it can test its hypothesis. The study gradually becomes more focused on its variables and influences on variables so that hypothesis may be tested. In this process, hypothesis can be disproved.
• REHEARSAL TESTING :   The researcher should conduct a pre testing or rehearsal before going for field work or data collection.
• FIELD RESEARCH :  To test and investigate hypothesis, field work with predetermined research methodology tools is conducted in which interviews, observations with stakeholders, questionnaires, surveys etc are used to follow. The documentation study may also happens at this stage.
• PRIMARY & SECONDARY DATA/INFORMATION ANALYSIS :  The primary or secondary data and information’s available prior to hypothesis testing may be used to ascertain validity of hypothesis itself.

Formulating a hypothesis can take place at the very beginning of a research project, or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis. Whenever a hypothesis is formulated, the most important thing is to be precise about what one’s variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

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May 8, 2024

‘Beyond Books’ Discussion Explores Library-Based Social Work

On May 6, 2024, the McSilver Institute convened a virtual conversation with leading experts in library-based social work. Libraries are anchors in many communities, increasingly going beyond their traditional roles to serve the needs of neighborhoods in unique ways. This two-part conversation examined how libraries across the country and here in New York have brought social workers into their branches, how that policy trend has developed in different places, and what the future of social workers in libraries may look like.

NYU McSilver’s Director of Evaluation Ashley Fuss moderated two dynamic panels featuring eight speakers experienced in the field. A full recording of the 90-minute program is available below, as well as short bios for all participating speakers. Additional resources about library-based social work are also included on this page to provided an overview of the topic, including slides presented by Dr. Margaret Ann Paauw during the event.

If you’d like to receive more updates on this and other offerings from the McSilver Institute, make sure to sign up for occasional email updates.

Video Recording

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Panelist Bios

Select a speaker to see their bio:

Jean Badalamenti, MSW

Jean launched a Peer Navigator Program in 2017 which connects customers without homes with library staff who have lived experience, and who provide emotional support and referrals to services in the community. Jean also manages DCPL’s library at the DC jail in partnership with the DC Department of Corrections. Jean was a founding member of the Public Library Association (PLA) Social Work Task Force, and served as its co-chair until 2020. She was a contributing advisor on the PLA publication A Trauma-Informed Framework for Supporting Patrons: The PLA Workbook of Best Practices. Jean holds a Masters’ Degree in Social Work from Howard University, and has worked in health and human services in DC for over 25 years.

Robyn Berger-Gaston, LCSW

Now as an agency division director, Robyn oversees a wide array of programs including services for seniors, social emotional learning, crisis response and community-based counseling programs. She has developed new programs for Family Service League including the community action crisis team, suicide response team and several Family Place Libraries throughout Long Island. In her role as a crisis responder, Robyn has worked directly with families and communities impacted by suicide and trains other professions in this work. She has received extensive training from the International Critical Incident Stress Foundation and is an approved instructor for Group Crisis Intervention. Robyn is a past president of the Gerontology Professionals of Long Island and has served as an adjunct faculty at St. Joseph’s College, Human Services department.

Julianna Black

Ashley Fuss, LMSW, PhD

Dr. Fuss has been working in the research, evaluation, and data analytics space for the last 10 years across various academic, public sector, and private sector settings. She has expertise in both quantitative and qualitative research methodologies and has significant experience working with organizations to design and implement evaluation and research protocols to determine program impact and effectiveness.

She received her MSW degree from Fordham University with a concentration in research, and her PhD degree from University of Pennsylvania. Her dissertation work focused on behavioral health prevention for youth using machine learning techniques.

Elissa Hardy, LCSW, MSW, MELP

Elissa has been an adjunct instructor at the University of Denver’s Graduate School of Social Work for 12 years, and developed and teaches the Policy Considerations for Environmental Justice in the US course. Elissa holds a master’s degree in Social Work from the University of Nebraska at Omaha, and a master’s degree in Environmental Law and Policy from Vermont Law School. She lives in Denver, Colorado with her two rescue pups, loves to travel, and spend time with nature and the important people in her life.

Peter Allen Lee, PhD, MSW

He has taught in the MSW Program and is involved in research and community development activities. Dr. Lee is the co-creator of Social Workers in the Library (with SJPL Librarian Deborah Estreicher). He is also involved with The Salvation Army and other non-profit community organizations in the San Francisco Bay Area developing sports, family, and youth programs. Dr. Lee has also been involved in the partnership among the School, College of Health and Human Sciences, and SJSU with the Viet Nam National University in Ha Noi to create graduate-level curriculum instrumental towards developing the social work profession in Viet Nam.

He has worked with CommUniverCity San Jose as the Associate Director, and Director of the SJSU “UP” Pre-College program (formerly Upward Bound). He is also a committee member for the California Social Work Hall of Distinction.

Peggy Morton, DSW, MSW

Dr. Morton has developed and taught Service-Learning courses to the wider University undergraduate community. She has had extensive experience as a field faculty member, field instructor, and faculty advisor in both the undergraduate and graduate social work programs and mentors DSW candidates. Currently, Dr. Morton serves as field instructor to interns placed in the NY Public Library system, a field placement that she created and continues to develop. She also served from 2013-2019 as the School’s Assistant Dean for Field Learning and Community Partnerships. She is a College Coach at Breakthrough NY and serves as an Advisory Board member at Partners for Campus-Community Engagement.

She earned her MSW and DSW from Hunter College School of Social Work (CUNY), and her BA from the University of Colorado.

Margaret Ann Paauw, PhD, LCSW

Leah Topek-Walker, LCSW-R

She currently serves as faculty in the Stony Brook School of Social Welfare Practicum Department, and supervises the library social work program. The library social work program is committed to providing the community with micro and mezzo interventions to address equity and access to care, and to concurrently providing social work students with dynamic learning opportunities. Leah is dedicated to working on issues of liberation, social justice and creating systemic change that empowers communities. Leah is working on her doctorate in social work, and serves on the Long Island Legislative Committee for Our Unhoused Neighbors, Social Workers for Justice and Patchogue-Medford Friends of the Library.

Additional Resources

• SLIDES Insights from Research on Library Social work  — Slides presented by Margaret Ann Paauw, PhD, LCSW (Presented at the start of this event; see the recording above)
• “What is Library Social Work?” (2021) from the National Asosciation of Social Workers (NASW) — In a 50-minute recorded conversation, this NASW-NYS Chapter Chat outlines the value of collaboration between libraries and social work practitioners.
• “Why your local library might be hiring a social worker,” NPR (2022) — Surveying libraries across the country, this article explorers some of the benefits and challenges of library-based social work.
• A Trauma-Informed Framework for Supporting Patrons: The PLA Workbook of Best Practices (2022) from the Public Library Association (PLA) — This workbook outlines trauma-informed best practices needed to address the growing roles libraries play to support their communities.
• “The Changing Role of Libraries: How Social Workers Can Help,” Families in Society: The Journal of Contemporary Social Services (2019) — In this journal article, authors Elizabeth A. Wahler, Mary A. Provence, John Helling, and Michael A. Williams explore the overlap in services provided by librarians and social workers.
• “Cultivating Protective Libraries: An Introduction to Public Library Social Work,” (2022) hosted by the NIH’s National Library of Medicine — Hosted by social worker Patrick Lloyd, this recorded class introduces library social work, its history, and emerging best practices.
• “Helping Homeless New Yorkers by the Books,” Bloomberg CityLab (2017) — This article profiles social work offerrings provided by the Brooklyn Public Library in New York City.
• “Social Workers and Librarians— A Case for Why We are BFFs” (2018) from the American Library Association’s Intersections blog — Amy Schofield describes Richland Library’s major insights after bringing social work into the South Carolina library.
• “Five Ways Public Libraries Go Far Beyond Books” (2024) from the Urban Libraries Council (ULC) — Including a recording of the ULC’s vital town hall on the importance of libraries, this blog post examines the various resources libraries offer as partners and providers for key social services.

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School of Social Work College of Social Science

Michigan state university research supports michigan bipartisan bill reform in juvenile justice system.

May 8, 2024 - Brandon Drain

On December 12, 2023, Lieutenant Governor Garlin Gilchrist signed a first-of-its-kind, bipartisan legislation transforming Michigan’s juvenile justice system and investing in diversion and re-entry services to better position Michigan’s youth for successful adulthoods.  

This reform goes into effect on October 1, 2024, and includes several changes. Such changes include requiring courts to adopt evidence-based practices -- like administering screening tools and risk and needs assessments. These changes should lead to, “more desirable outcomes, increased opportunities for alternatives to detention with more funding for community based-programming, and almost a complete elimination of juvenile court fines and fees,” said Ashlee Barnes-Lee, assistant professor at Michigan State University’s School of Social Work.  

Barnes-Lee is an interdisciplinary, action researcher whose research focuses on juvenile legal system reform, with a specific emphasis on promoting racial equity and strength-based approaches to assessing and treating justice-involved youth.   

“In my community-driven research, I partner with juvenile court administrators who are interested in co-developing strategies to reduce racial and ethnic disparities and improve outcomes for the youth they serve,” said Barnes-Lee. “This new legislation broadens my opportunity to partner with courts looking to be trained in juvenile risk and needs assessment, analysis of existing data, as well as those interested in evaluating their programs and services.”  

“This bill signing accelerates the implementation phase of a statewide collaboration that began with Lt. Governor Gilchrist’s leadership and the hard work of partners on the task force, and Michigan courts are ready for this challenge,” said Michigan Supreme Court Chief Justice Elizabeth Clement.   

One of the ways Barnes-Lee wants to better serve justice-involved youth is by implementing more strengths-based, Juvenile Risk-Need Assessments (JRNAs) for treatment and rehabilitation.   

This approach aims to strengthen the widely implemented Risk-Needs-Responsivity (RNR) model – which has a deficits-focus, as it postulates that targeting youths’ risk factors when developing court treatment plans is the most effective way to reduce likelihood of future delinquency. While risk factor detection is important and effective, many scholars, including Barnes-Lee, have criticized its overall efficacy for not placing a stronger emphasis on youths’ strengths, assets, or protective factors.   

Criticism of deficit-focused models has led to more research in strength-based models.   

“Strengths-based approaches to treatment and rehabilitation for justice-involved youth is important because it destigmatizes youth, increases optimism among juvenile probation officers, and could theoretically lead to more accurate predictions of future delinquency,” said Barnes-Lee. “Strengths-based approaches may also be particularly beneficial for youth of color, and other historically marginalized youth, who are perceived more negatively, and are overrepresented in the juvenile legal system.”  

In 2020, Barnes-Lee published two manuscripts detailing the development of a strengths-based tool called Protective Factors for Reducing Juvenile Reoffending (PFRJR). This tool was adopted by a Michigan juvenile court and has been benefiting youth on probation in that county for almost 10 years.   

The bill reform gives Barnes-Lee, and other Michigan researchers, the opportunity to partner with juvenile court administrators to provide evidence-based practices to better serve justice-involved youth.   

“Countless youth and families have unfortunately been harmed by our juvenile legal system. I believe it’s important for us to focus on both prevention and reform. I am proud of the work that Michigan lawmakers and community advocates are doing to advance justice and equity in our state. Although there is much work to be done, we are moving in the right direction.”