Prepared by : Mohammed Salahat

Supervised by

Dr. Aidah Abu Elsoud AlkaissiLinkoping University-Sweden

An-Najah National University- Palestine

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Quasi-experimentsQuasi-experiments, like true

experiments, involve:the manipulation of an independent variable,

that is, an intervention.

However, quasi-experimental designs lack randomization to treatment groups, which characterizes true experiments

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Quasi-Experimental Designs

Quasi-experiments are not as powerful as experiments in establishing causal connections between interventions and outcomes.

Figure 8-4 presents a symbolic representation of a pretest—posttest experimental design.

In this figure, R means random assignment to groups; O represents an observation (i.e., the collection of data on the

dependent variable); and X stands for exposure to an intervention.

Thus, the top line in this figure represents the experimental group that had subjects randomly assigned to it (R), had both a pretest (O1) and a posttest (O2), and has been exposed to an experimental intervention (X).

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Quasi-Experimental Designs

The second row represents the control group, which differs from the experimental group only by absence of the treatment (X).

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Symbolic representation of apretest–posttest (before–after) experimental design.

R O1 X O2R O1 O2

R=RandomizationO= Observation or measurementX= Treatment or intervaetion

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FIGURE 8.4 Symbolic representation of a

pretest–posttest (before–after) experimental design .

Nonequivalent Control Group Designs

The most frequently used quasi-experimental design is the nonequivalent control group pretest— posttest design, which involves an experimental treatment and two groups of subjects observed before and after its implementation.

Suppose, for example, we wished to study the effect of introducing primary nursing on staff morale in a large metropolitan hospital. العاصمةالمستشفى

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Nonequivalent Control Group DesignsBecause the new system of nursing care delivery

is being implemented throughout the hospital, randomization is not possible.

Therefore, we decide to collect comparison data from nurses in another similar hospital that is not instituting primary nursing.

Data on staff morale is collected in both hospitals before the change is made (the pretest) and again after the new system is implemented in the first hospital (the posttest)

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FIGURE 8.5 Nonequivalent controlgroup pretest–posttest design (quasiexperiment

group pretest–posttest design (quasiexperiment.

O1 X O2O1 O2

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Figure 8-5 depict”show” this study symbolically.

The top row is our experimental (primary nursing) hospital; the second row is the hospital using traditional nursing.

A comparison of this diagram with the one in Figure 8-4 shows that they are identical, except that subjects have not been randomly assigned to treatment groups in the second diagram.

The design in Figure 8-5 is the weaker of the two because it can no longer be assumed that the experimental and comparison groups are equivalent at the outset. ومنذ البداية.

Because there was no randomization, this study is quasi-experimental rather than experimental

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The design is nevertheless strong, because the pretest data allow us to determine whether the groups had similar morale initially.

If the comparison and experimental groups are similar on the pretest, we could be relatively confident that any posttest difference in self-reported morale was the result of the new system of nursing care.

If the morale of the two groups is very different initially, however, it will be difficult to interpret any posttest differences, although there are statistical procedures that can help.

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Note that in quasi-experiments, the term comparison group is usually used in lieu of control group to refer to the group بدلagainst which outcomes in the treatment group are evaluated.

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Now suppose we had been unable to collect pretest data.

This design, diagramed in Figure 8-6, has a flaw that is difficult to remedy.

We no longer have information about the initial equivalence of the two nursing staffs.

If we find that staff morale in the experimental hospital is lower than that in the control hospital at the posttest, can we conclude that the new method of delivering care caused a decline in staff morale?

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There could be alternative explanations for the posttest differences.

In particular, it might be that the morale of the employees in the two hospitals differed even at the outset.

Campbell and Stanley (1963) call the nonequivalent control group posttest-only design in Figure 8-6

preexperimental rather than quasi-experimental because of its fundamental weakness.

Thus, although quasi-experiments lack the controlling properties of true experiments, the hallmark سمة مميزة of quasi-experiments is the effort to introduce strategies to compensate for the absence of either randomization or control groups.

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FIGURE 8.6 Nonequivalent control group onlyposttest design (preexperimental)

X OO

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Example of a nonequivalent controlgroup pretest—posttest design:

Johnson et al (1999) evaluated the effect of a nurse-delivered smoking cessation intervention on smoking status and smoking self efficacy among patients hospitalized with cardiac disease.

Experimental subjects were admitted to one cardiac unit, and comparison subjects were admitted to another.

The researchers preferred this approach to randomization within units because information sharing among patients in the same unit could have contaminated treatment conditions.

By collecting pretest data, the researchers learned that the two groups were comparable with regard to demographic characteristics and pre intervention smoking histories.

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Strengths and Limitations ofQuasi-ExperimentsA great strength of quasi-experiments is that they

are practical.

In the real world, it may be difficult, if not impossible, to conduct true experiments.

Nursing research usually occurs in real-life settings, where it is difficult to deliver an innovative treatment عالج مبتكرة randomly to some people but not to others.

Quasi-experimental designs introduce some research control when full experimental rigor is not possible.

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Strengths and Limitations ofQuasi-ExperimentsResearchers using quasi-experimental designs

need, however, to be acquainted مطلعwith their weaknesses, and take these weaknesses into account in interpreting results.

When a quasi-experimental design is used, there may be several rival hypotheses فرضيات competing with the experimental منافسmanipulation as explanations for the results.

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NONEXPERIMENTALRESEARCHMany research problems cannot be addressed with an

experimental or quasi-experimental design.

For example, suppose we were interested in studying the effect of widowhood” he state of having lost one's spouse to death” on health status.

Our independent variable is widowhood versus nonwidowhood.

Clearly, we cannot manipulate widowhood; people lose their spouses by a process that is neither random nor subject to research control.

Thus, we would have to proceed by taking two groups (widows and nonwidows) as they naturally occur and comparing them in terms of health status.

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Reasons for UndertakingNonexperimental ResearchMost studies involving human subjects,

including nursing studies, are nonexperimental.

One reason for using a nonexperimental design is that a vast واسعة number of human characteristics are inherently not subject to experimental manipulation (e.g., blood type, personality, health beliefs, medical diagnosis); the effects of these characteristics on other phenomena cannot be studied experimentally.

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Reasons for UndertakingNonexperimental ResearchA second issue is that in nursing research, as in other

fields, there are many variables that could technically be manipulated but could not be manipulated ethically.

If manipulating the independent variable could cause physical or mental harm to subjects, then the variable should not be controlled experimentally.

For example, if we were studying the effect of prenatal care on infant mortality, it would be unethical to provide such care to one group of pregnant women while deliberately depriving a second group.

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Reasons for UndertakingNonexperimental ResearchWe would need to locate a naturally occurring group of

pregnant women who had not received prenatal care.

Their birth outcomes could then be compared with those of women who had received appropriate care.

The problem, however, is that the two groups of women are likely to differ in terms of many other characteristics, such as age, education, nutrition, and health, any of which individually or in combination could affect infant mortality, independent of the absence or presence of prenatal care.

This is precisely why experimental designs are so strong in demonstrating cause-and-effect relationships.

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Reasons for UndertakingNonexperimental Research Third, there are many research situations in which it is simply not

practical to conduct a true experiment.

constraint might involve insufficient time, lack of administrative approval, excessive inconvenience to patients or staff, or lack of adequate funds.

Fourth, there are some research questions for which an experimental design is not appropriate.

This is especially true for descriptive studies, which seek to document the characteristics, prevalence, intensity, or full nature of phenomena.

qualitative studies are nonexperimental.

Manipulation is neither attempted nor considered desirable; the emphasis is on the normal experiences of humans.

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Reasons for UndertakingNonexperimental ResearchFinally, nonexperimental research is usually

needed before an experimental study can be planned.

Experimental interventions are developed on the basis of nonexperimental research documenting the scope of a problem and describing critical relationships between relevant variables.

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Retrospective DesignsStudies with a retrospective design are ones in which a

phenomenon existing in the present is linked to phenomena that occurred in the past, before the study was initiated.

That is, the researcher is interested in a present outcome and attempts to determine antecedent factors that caused it.

Most of the early epidemiologic studies of the link between cigarette smoking and lung cancer were retrospective.

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Retrospective Designs

In such a study, the researcher begins with groups of people with and without lung cancer (the dependent variable).

The researcher then looks for differences between the two groups in antecedent behaviors or conditions.

Retrospective studies are often cross-sectional, with data on both the dependent and independent variables collected once, simultaneously.

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Retrospective Designs

Researchers can sometimes strengthen a retrospective design by taking certain steps.

For example, one type of retrospective design, referred to as a case—control design, involves the comparison of cases (subjects with a certain illness or condition, such as lung cancer victims) with controls (e.g., people without lung cancer).

In conducting a strong case—control study, researchers find the cases and obtain from them (or about them, if records are available) information about the history of the presumed cause.

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Retrospective Designs

Then the researchers must find controls without the disease or condition who are as similar as possible to the cases with regard to key extraneous variables (e.g., age, gender) and also obtain historical information about the presumed cause. .يفترض السبب

If controls are well chosen, the only difference between them and the cases is exposure to the presumed cause.

Researchers sometimes use matching or other techniques to control for extraneous variables.

To the degree that researchers can demonstrate comparability between cases and controls with regard to extraneous traits, inferences regarding the presumed cause of the disease are enhanced. يتم تحسين استنتاجات بشأن القضيةالمفترضة للمرض

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Example of a retrospective study:

Heitkemper (2001) used a retrospective design in their study of factors contributing to the onset of irritable bowel syndrome (IBS).

They compared samples of women with and without IBS in terms of their history of sexual and physical abuse, and found that abusive experiences were more prevalent among women with IBS.

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Prospective Nonexperimental DesignsA nonexperimental study with a prospective design

(sometimes called a prospective cohort design) starts with a presumed cause and then goes forward in time to the presumed effect.

For example, we might want to test the hypothesis that the incidence of rubella during pregnancy (the independent variable) is related to infant abnormalities (the dependent variable).

To test this hypothesis prospectively, we would begin with a sample of pregnant women, including some who contracted rubella during their pregnancy and others who did not.

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Prospective Nonexperimental DesignsThe subsequent occurrence of congenital

anomalies would be assessed for all subjects, and we would examine whether women with rubella were more likely than other women to bear malformed infants.

Prospective designs are often longitudinal, but may also be cross-sectional (from the subjects’ point of view) if reliable information about the independent variable is available in records or existing data sources.

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Prospective Nonexperimental DesignsNot all longitudinal studies are prospective,

because sometimes the independent variable has occurred long before the initial wave of data collection.

And not all prospective studies are longitudinal in the classic sense.

For example, an experimental study that collects data at 2, 4, and 6 hours after an intervention would be considered prospective but not longitudinal (i.e., data are not collected over an extended period of time.)

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Prospective Nonexperimental DesignsProspective studies are more costly than

retrospective studies.

For one thing, a substantial follow-up period may be necessary before the dependent variable manifests itself, as is the case in prospective studies of cigarette smoking and lung cancer.

Also, prospective designs may require large samples, particularly if the dependent variable of interest is rare, as in the example of malformations associated with maternal rubella.

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Prospective Nonexperimental DesignsProspective studies are more costly than

retrospective studies.

For one thing, a substantial follow-up period may be necessary before the dependent variable manifests itself, as is the case in prospective studies of cigarette smoking and lung cancer.

Also, prospective designs may require large samples, particularly if the dependent variable of interest is rare, as in the example of malformations associated with maternal rubella.

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Prospective Nonexperimental DesignsAnother issue is that in a good prospective

study, researchers take steps to confirm that all subjects are free from the effect (e.g., the disease) at the time the independent variable is measured, and this may in some cases be difficult or expensive to do.

For example, in prospective smoking/lung cancer studies, lung cancer may be present initially but not yet diagnosed.

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Prospective Nonexperimental DesignsDespite these issues, prospective studies are

considerably stronger than retrospective studies.

In particular, any ambiguity about whether the presumed cause occurred before the effect is resolved in prospective research if the researcher has confirmed the initial absence of the effect.

In addition, samples are more likely to be representative, and investigators may be in a position to impose controls to rule out competing explanations for the results. قد تكون في وضع يمكنها من.فرض ضوابط الستبعاد التفسيرات المتنافسة على النتائج

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Prospective Nonexperimental DesignsSome prospective studies are exploratory.

That is, the researcher measures a wide range of possible “causes” at one point in time, and then examines an outcome of interest at a later point (e.g., length of stay in hospital).

Such studies are usually stronger than retrospective studies if it can be determined that the outcome was not present initially because time sequences are clear.

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Prospective Nonexperimental DesignsHowever, they are not as powerful as prospective

studies that involve specific a priori hypotheses and the comparison of cohorts known to differ on a presumed cause. مقارنة بين األفواج المعروف أن تختلف

Researchers doing exploratory retrospective or prospective studies are sometimes accused of going on “fishing expeditions” that can lead to erroneous conclusions because of spurious relationships in a particular sample of subjects.

" حمالت الصيد" التي يمكن أن تؤدي إلى استنتاجات خاطئة.بسبب عالقات زائفة أو الفقهي في عينة خاصة

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Example of a prospective study nonexperimental

Brook (2000) conducted a prospective cohort study to examine clinical and cost outcomes of early versus late tracheostomy in patients who require prolonged mechanical ventilation.

Early tracheostomy was found to be associated with shorter lengths of hospital stay and lower hospital costs.

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Descriptive Research

The second broad class of nonexperimental studies is descriptive research.

The purpose of descriptive studies is to observe, describe, and document aspects of a situation as it naturally occurs and sometimes to serve as a starting point for hypothesis generation or theory development.

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Descriptive Correlational StudiesAlthough researchers often focus on understanding the

causes of behaviors, conditions, and situations, sometimes they can do little more than describe relationships without comprehending causal pathways. .أحيانا يمكن أن تفعل أكثر من وصف العالقات دون فهم مسارات سببية

Many research problems are cast ألقى in noncausal terms.

We ask, for example, whether men are less likely than women to bond with their newborn infants, not whether a particular configuration of sex chromosomes caused differences in parental attachment. ونسأل ، على سبيلالمثال ، إذا كان الرجال هم أقل عرضة من النساء اللسندات مع أطفالهن حديثي الوالدة ، وليس ما إذا كان تكوين معين من الصبغيات .الجنس سبب االختالفات في الحجز الوالدية

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Descriptive Correlational StudiesUnlike other types of correlational research—

such as the cigarette smoking and lung cancer investigations— the aim of descriptive correlational research is to describe the relationship among variables rather than to infer cause-and-effect relationships. بدال من أن.نستنتج السبب والنتيجة العالقات

Descriptive correlational studies are usually cross-sectional.

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Example of a descriptive correlationalstudy

Morin(2002) described the relationship between body image perceptions of postpartum African-American women on the one hand, and their weight (based on the body mass index) on the other.

Irrespective of body mass category, women usually considered themselves larger than they were.

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Univariate Descriptive Studies

Some descriptive studies are undertaken to describe the frequency of occurrence of a behavior or condition rather than to study relationships.

For example, an investigator may wish to describe the health care and nutritional practices of pregnant teenagers.

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Univariate Descriptive Studies

Univariate descriptive studies are not necessarily focused on only one variable. For example, a researcher might be interested in women’s experiences during menopause.

The study might describe the frequency of various symptoms, the average age at menopause, the percentage of women seeking formal health care, and the percentage of women using medications to alleviate symptoms.

There are multiple variables in this study, but the primary purpose is to describe the status of each and not to relate them to one another.

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Prevalence studiesTwo types of descriptive study from the field of

epidemiology are especially worth noting.

Prevalence studies are done to determine the prevalence rate of some condition (e.g., a disease or a behavior, such as smoking) at a particular point in time.

Prevalence studies rely on cross sectional designs in which data are obtained from the population at risk of the condition.

The researcher takes a “snapshot” of the population at risk to determine the extent to which the condition of interest is present.

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The formula for a pointprevalence rate (PR) is:Number of cases with the condition or disease at a

given point in time/ Number in the population at risk of being a case x K

K is the number of people for whom we want to have the rate established (e.g., per 100 or per 1000 population).

When data are obtained from a sample (as would usually be the case), the denominator is the size of the sample, and the numerator is the number of cases with the condition, as identified in the study.

عندما يتم الحصول على البيانات من عينة )كما هي الحال عادة( ، والقاسم هو حجم العينة ، والبسط هو عدد الحاالت مع شرط ، على .النحو المحدد في الدراسة

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If we sampled 500 adults aged 21 years and older living in a community, administered a measure of depression, and found that 80 people met the criteria for clinical depression, then the estimated point prevalence rate of clinical depression per 100 adults in that community would be 16 per 100.

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Incidence studies are used to of developing new cases measure the frequency

Longitudinal designs are needed to determine incidence because the researcher must first establish who is at risk of becoming a new case—that is, who is free of the condition at the outset.

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The formula for an incidencerate (IR) is:

Number of new cases with the condition or disease over a given time period / Number at risk of becoming a new case (free of the condition at the outset) X K

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If we continued with our previous example, suppose in October, 2001 we found that 80 in a sample of 500 people were clinically depressed (PR 16 per 100).

To determine the 1-year incidence rate, we would reassess the sample in October, 2002.

Suppose that, of the 420 previously deemed not to be clinically depressed in 2001, 21 were now found to meet the criteria for depression.

In this case, the estimated 1-year incidence rate would be 5 per 100) 21/420X100=5

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Prevalence and incidence rates can be calculated for subgroups of the population (e.g., for men versus women).

When this is done, it is possible to calculate another important descriptive index.

Relative risk is an estimate of risk of “caseness” in one group compared with another.

Relative risk is computed by dividing the rate for one group by the rate for another.

Suppose we found that the 1-year incidence rate for depression was 6 per 100 women and 4 per 100 men.

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Women’s relative risk for developing depression over the 1-year period would be 1.5, that is, women would be estimated to be 1.5

times more likely to develop depression than men.

Relative risk is an important index in determining the contribution of risk factors to a disease or condition (e.g., by comparing the relative risk for lung cancer for smokers versus nonsmokers).

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Example of an incidence and prevalence study:Whittington (2000) conducted a national study

involving 116 acute care facilities in 34 states to determine the incidence and prevalence of pressure ulcers.

Prevalence for 17,650 patients in medical-surgical or intensive care units was measured during a 24-hour period at each facility.

Incidence was measured over the average length of hospital stay for each facility.

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