Causal-comparative Research

Dr. V.K. Maheshwari, Former Principal

K.L.D.A.V (P. G) College, Roorkee, India

Causal-comparative research is an attempt to identify a causative relationship between an independent variable and a dependent variable.The relationship between the independent variable and dependent variable is usually a suggested relationship (not proven) because you (the researcher) do not have complete control over the independent variable.

The Causal Comparative method seeks to establish causal relationships between events and circumstances. In other words, it finds out the causes of certain occurrences or non-occurrenceces. This is achieved by comparing the circumstances associated with observed effects and by noting  the factors present in the instances where a given effect occurs and where it does not occur. This method is based on Miill’s canon of agreement and disaggrement which states that caoses of given observed effect may be ascertained by noting elements which are invariably present when the result is present and which are invariably absent when the result is absent.

Causal-comparative research scrutinizes the relationship among variables in studies in which the independent variable has already occurred, thus making the study descriptive rather than experimental in nature. Because the independent variable (the variable for which the researcher wants to suggest causation) has already been completed (e.g., two reading methods used by a school ), the researcher has no control over it. That is, the researcher cannot assign subjects or teachers or determine the means of implementation or even verify proper implementation.

Sometimes the variable either cannot be manipulated (e.g., gender) or should not be manipulated (e.g., who smokes cigarettes or how many they smoke). Still, the relationship of the independent variable on one or more dependent variables is measured and implications of possible causation are used to draw conclusions about the results.

Also known as “ex post facto” research.  (Latin for “after the fact”) since both the effect and the alleged cause have already occurred and must be studied in retrospect  .In this type of research investigators attempt to determine the cause or consequences of differences that already exist between or among groups of individuals.

Used, particularly in the behavioral sciences. In education, because it is impossible, impracticable, or unthinkable to manipulate such variables as aptitude, intelligence, personality traits, cultural deprivation, teacher competence, and some variables that might present an unacceptable threat to human beings, this method will continue to be used.

Causal-Comparative Research Facts

• Causal-Comparative Research is not manipulated by the researcher.
• -Does not establish cause-effect relationships.
• -Generally includes more than two groups and at least one dependent variable.
• -Independent variable is causal-comparative studies is often referred to as the grouping variable.
• -The independent variable has occurred or is already formed.

The Nature of Causal-Comparative Research

A common design in educational research studies,  Causal-comparative research, seeks to identify associations among variables. Relationships can be identified in causal-comparative study, but causation cannot be fully established.

Attempts to determine cause and effect. It is not as powerful as experimental designs  Causal-comparative research attempts to determine the cause or consequences of differences that already exist between or among groups of individuals.

Alleged cause and effect have already occurred and are being examined after the fact. The basic causal-comparative approach is to begin with a noted difference between two groups and then to look for possible causes for, or consequences of, this difference.

Used when independent variables cannot or should not be examined using controlled experiments.  When an experiment would take a considerable length of time and be quite costly to conduct, a causal-comparative study is sometimes used as an alternative.

Main purpose  of causal-comparative research:

• Exploration of Effects
• Exploration of Causes
• Exploration of Consequences

Basic Characteristics of Causal-comparative research

In short it the basic Characteristics of Causal-comparative research can be concluded:

• -Causal comparative research attempts to determine reasons, or causes, for the existing condition
• Causal comparative studies are also called ex post facto because the investigator has no control over the exogenous variable. Whatever happened occurred before the researcher arrived.
• -Causal-comparative research is sometimes treated as a type of descriptive research since it describes conditions that already exist.
• -Causal-comparative studies attempt to identify cause-effect relationships; correlational studies do not
• -Causal-comparative studies involve comparison, correlational studies involve relationship.
• -Causal-comparative studies typically involve two (or more) groups and one independent variable, whereas correlational studies typically involve two or more variables and one group
• -Causal-comparative studies typically involve two (or more) groups and one independent variable, whereas correlational studies typically involve two or more variables and one group
• -In causal-comparative  the researcher attempts to determine the cause, or reason, for preexisting differences in groups of  individual.
• Involves comparison of two or more groups on a single endogenous variables.
• -Retrospective causal-comparative studies are far more common in educational research
• -The basic approach is sometimes referred to as retrospective causal-comparative research (since it starts with effects and investigates causes)
• -The basic approach is sometimes referred to as retrospective causal-comparative research (since it starts with effects and investigates causes)
• -The basic causal-comparative approach involves starting with an effect and seeking possible causes.
• The characteristic that differentiates these groups is the exogenous variable.
• -The variation as prospective causal-comparative research (since it starts with causes and investigates effects)
• We can never know with certainty that the two groups were exactly equal before the difference occurred.

Three important aspects of Causal Comparative method are:

1-      Gathering of data on factors invariably present in cases where the given result occurs and discarding of those elements which are not universally present

2-      2-Gathering the data on factors invariably present in cases where the given effect does not occur

3- 3 Comparing the two sets of data, or in effect, substracting one from the other to get at the causes responsible for the occurance or otherwise of the effect.

Examples of variables investigated in Causal-Comparative Research

• -Ability variables (achievement)
• -Family-related variables (SES)
• -Organismic variables (age, ethnicity, sex)
• -Personality variables (self-concept)
• -School related variables (type of school, size of school)

Causal Comparative Research Procedure

Experimental, quasi-experimental, and causal-comparative research methods are frequently studied together because they all try to show cause and effect relationships among two or more variables. To conduct cause and effect research, one variable(s) is considered the causal or independent variable and

Causal comparative research attempts to attribute a change in the effect variable(s) when the causal variable(s) cannot be manipulated.

For example: if you wanted to study the effect of socioeconomic variables such as sex, race, ethnicity, or income on academic achievement, you might identify two existing groups of students: one group – high achievers; second group – low achievers. You then would study the differences of the two groups as related to socioeconomic variables that already occurred or exist as the reason for the difference in the achievement between the two groups. To establish a cause effect relationship in this type of research you have to build a strongly persuasive logical argument. Because it deals with variables that have already occurred or exist, causal-comparative research is also referred to as ex post facto research.

The most common statistical techniques used in causal comparative research are analysis of variance and t-tests wherein significant differences in the means of some measure (i.e. achievement) are compared between or among two or more groups.

Data Sources

• Raw scores such as test scores
• Measures such as grade point averages
• Judgements, and other assessments made of the subjects involved

Research Tools

• Standardized tests
• Surveys
• Structured interviews

Procedural Considerations

• The most important procedural consideration in doing causal comparative research is to identify two or more groups which are demonstrably different in an educationally important way such as high academic achievement versus low academic achievement. An attempt is then made to identify the cause which resulted in the differences in the effect (i.e. academic achievement). The cause (i.e. race, sex, income, etc.) has already had its effect and cannot be manipulated, changed or altered. In selecting subjects for causal- comparative research, it is most important that they be identical as possible except for the difference (i.e. independent variable – race, sex, income) which may have caused the demonstrated effect (i.e. dependent variable – academic achievement)
• Hypotheses are generally used
• Statistics are extensively used in experimental research and include measures of spread or dispersion such as:
• t-tests;
• Chi-Square;
• analysis of variance as well as measures of relationship such as
• : Pearson Product-Moment Coefficient;
• Spearman Rank Order Coefficient; Phi Correlation Coefficient; regression

SPECIAL PROCEDURAL CONSIDERATIONS

1. Statistics are extensively used in causal comparative research and include measures of relationship such as: Pearson Product-Moment Coefficient; Spearman Rank Order Coefficient; Phi Correlation Coefficient; Regression; as well as measures of spread or dispersion such as: t-tests; Chi-Square; Analysis of Variance.
2. REPORT PRESENTATION Reports tend to rely on both quantitative and qualitative presentations. Statistical data is almost always provided and supports the overall argument which is used to establish the cause and effect relationship.

Report Presentation

• Reports tend to rely on quantitative presentations
• Statistical data is almost always provided and supports the overall cause-effect argument.

CONDUCTING A CAUSAL-COMPARATIVE STUDY

• -Although the independent variable is not manipulated, there are control procedures that can be exercised to improve interpretation of results.

Design & Procedure

-The researcher selects two groups of participants, the experimental and control groups, but more accurately referred to as comparison groups.

-Groups may differ in two ways.

• -One group possesses a characteristic that the other does not.
• -Each group has the characteristic, but to differing degrees or amounts.

-Definition and selection of the comparison groups are very important parts of the causal-comparative procedure.

• -The independent variable differentiating the groups must be clearly and operationally defined, since each group represents a different population.
• -In causal-comparative research the random sample is selected from two already existing populations, not from a single population as in experimental research.
• -As in experimental studies, the goal is to have groups that are as similar as possible on all relevant variables except the independent variable.

-The more similar the two groups are on such variables, the more homogeneous they are on everything but the independent variable.

CONTROL PROCEDURES

-Lack of randomization, manipulation, and control are all sources of weakness in a causal-comparative study.

-Random assignment is probably the single best way to try to ensure equality of the groups.

-A problem is the possibility that the groups are different on some other important variable (e.g. gender, experience, or age) besides the identified independent variable.

MATCHING

• -Matching is another control technique.
• -If a researcher has identified a variable likely to influence performance on the dependent variable, the researcher may control for that variable by pair-wise matching of participants.
• -For each participant in one group, the researcher finds a participant in the other group with the same or very similar score on the control variable.
• -If a participant in either group does not have a suitable match, the participant is eliminated from the study.
• -The resulting matched groups are identical or very similar with respect to the identified extraneous variable.
• -The problem becomes serious when the researcher attempts to simultaneously match participants on two or more variables.

COMPARING HOMOGENEOUS GROUPS OR SUBGROUPS

• -To control extraneous variables, compare groups that are homogeneous with respect to the extraneous variable.
• -This procedure may lower the number of participants and limits the generalizability of the findings.
• -A similar but more satisfactory approach is to form subgroups within each group that represent all levels of the control variable.
• -Each group might be divided into high, average, and low IQ subgroups.
• -The existence of comparable subgroups in each group controls for IQ.
• -In addition to controlling for the variable, this approach also permits the researcher to determine whether the independent variable affects the dependent variable differently at different levels of the control variable.
• -The best approach is to build the control variable right into the research design and analyze the results in a statistical technique called factorial analysis of variance.
• -A factorial analysis allows the researcher to determine the effect of the independent variable and the control variable on the dependent variable both separately and in combination.
• -It permits determination of whether there is interaction between the independent variable and the control variable such that the independent variable operates differently at different levels of the control variable.

ANALYSIS OF COVARIANCE

• -Is used to adjust initial group differences on variables used in causal-comparative and experimental research studies.
• -Analysis of covariance adjusts scores on a dependent variable for initial differences on some other variable related to performance on the dependent.
• -Suppose we were doing a study to compare two methods, X and Y, of teaching fifth graders to solve math problems.
• -Covariate analysis statistically adjusts the scores of method Y to remove the initial advantage so that the results at the end of the study can be fairly compared as if the two groups started equally.

DATA ANALYSIS AND INTERPRETATION

• -Analysis of data involves a variety of descriptive and inferential statistics.

-The most commonly used descriptive statistics are

(a)  the mean, which indicates the average performance of a group on some measure of a variable, and

(b)  the standard deviation, which indicates how spread out a set of scores is around the mean, that is, whether the scores are relatively homogeneous or heterogeneous around the mean.

-The most commonly used inferential statistics are

(a) the t test, used to determine whether the means of two groups are statistically different from one another;

(b) analysis of variance, used to determine if there is significant difference among the means of three or more groups; and

(c)  chi square, used to compare group frequencies, or to see if an event occurs more frequently in one group than another.

-Lack of randomization, manipulation, and control factors make it difficult to establish cause-effect relationships with any degree of confidence.

• -However, reversed causality is more plausible and should be investigated.
• -It is equally plausible that achievement affects self-concept, as it is that self-concept affects achievement.

-The way to determine the correct order of causality-which variable caused which- is to determine which one occurred first.

• -The possibility of a third, common explanation in causal-comparative research is plausible in many situations.
• -One way to control for a potential common cause is to equate groups on that variable.
• -To investigate or control for alternative hypotheses , the researcher must be aware of them and must present evidence that they are not in fact the true explanation for the behavioral differences being investigated.

Types of Causal-Comparative Research Designs

There are two types of causal-comparative research designs:

Retrospective causal-comparative research

Retrospective causal-comparative research requires that a researcher begins investigating a particular question when the effects have already occurred and the researcher attempts to determine whether one variable may have influenced another variable.

Prospective causal-comparative research

Prospective causal-comparative research occurs when a researcher initiates a study a study begin with the causes and is determined to investigate the effects of a condition. By far, retrospective causal-comparative research designs are much more common than prospective causal-comparative designs….

Basic approach of causal- comparative research

The researcher observe that 2 groups differ on some variable (teaching style) and then attempt to find the reason for (or the results of) this difference. …

- Causal-comparative studies attempt to identify cause-effect relationships.

2- Causal-comparative studies typically Involve two (or more) groups and one independent variable

3- Causal-comparative studies involve comparision.

4-The basic causal-comparative approach involves starting with an effect and seeking possible causes ( retrospective).

5-Retospective causal – comparative studies are far more common in educational research.

Steps for conducting a Causal-comparative research

STEP ONE- Select a topic

For  determining the problem it is necessary for the researcher to focus on the problem that he or she needs to study. They not only need to find out a problem, they also need to determine, analyse and define the problem which they will be dealing with.

Topic studies with Causal-comparative designs typically catch a researcher’s attention based on experiences or situations that have occurred in the real world.

The first step in formulating a problem in causal-comparative research is usually to identify and define the particular phenomena of interest, and then to consider possible causes for, or consequences of, these phenomena.

There are no limits to the kinds of instruments that can be used in a causal-comparative study.

The basic causal-comparative design involves selecting two groups that differ on a particular variable of interest and then comparing them on another variable or variables.

STEP TWO -Review of literature

. Literature Review Before trying to predict the causal relationships, the researcher needs to study all the related or similar literature and relevant studies, which may help in further analysis, prediction and conclusion of the causal relationship between the variables under study.

Reviewing published literature on a specific topic of interest is specially important when conducting  Caucal-comparative research as such a review can assist a researcher in determining which extraneous variable  may exist in the situations that they are considering studying.

STEP THREE- Develop a Research hypothesis

The third step of the  research is to propose the possible solutions or alternatives that might have led to the effect. They need to list out the assumptions which will be the basis of the hypothesis and procedure of the research. Hypothesis developed for Causal-comparative research  to identify the independent and dependent variable Causal-comparative hypothesis should describe the expected impact of the independent variable on the dependent variable.

STEP FOUR-Select participants

The important thing in selecting a sample for a causal-comparative study is to define carefully the characteristic to be studied and then to select groups that differ in this characteristic.

In causal-comparative research participants are already organized in groups. The researcher selects two groups of participants the experimental and control groups but more accurately referred to as  comparison groups because one group does not possess a characteristics or experience possessed by the second group or the two groups differ in the amount the characteristics that they share. The independent variable they share. The independent variable differentiating the groups must be clearly and operationally defined, since each group represent a different variable.

STEP FIVE- Select instruments to measure  variables and collecting data

As all the types of qualitative research Causal-comparative research requires that researcher selects instruments that are reliable and allow researchers  to draw valid conclusions( Link to reliability and validity portion of site ) . They also need to select the scale or construct instrument for collecting the required information / data. After a researcher has selected a reliable and valid instrument, data for the study can be selected.

Causal Comparative: Data Collection

■ You select two groups that differ on the (exogenous) variable of interest.

■ Next, compare the two groups by looking at an endogenous variable that you think might be influenced by the exogenous variable.

■ Define clearly and operationally the exogenous variable.

■ Be sure the groups are similar on all other important variables.

Causal Comparative: Equating groups

■ Use subject matching

■ Use change scores; i.e., each subject as own control

■ Compare homogeneous groups

■ Use analysis of covariance

STEP SIX- Analyze and interpret results

Finally, the researcher needs to analyse, evaluate and interpret the information collected. It is on basis of this step only, the researcher selects the best possible alternative of causes which might have led the effect to occur

Typically in Causal-comparative studies data is reported as a mean or frequency for each group. Inferential statistics is than used to determine whether the mean “ for the groups are significantly differ from each other. Since Causal-comparative research can not definitively determine that one variable  has caused something to occur  Reacher should instead report the findings of Causal-comparative studies as a possible effect or possible cause of an event or occurrence.

Similarly, Jacobs et al. (1992: 81) also proposed that the following steps are involved in conducting an ex-post facto-research:

First Step: The first step should be to state the problem.

Second Step: Following this is the determination of the group to be investigated. Two groups of the population that differ with regard to the variable, should be selected in a proportional manner for the test sample.

Third step: The next step refers to the process of collection of data. Techniques like questionnaires, interviews, literature search etc. are used to collect the relevant information.

Fourth Step: The last step is the interpretation of the findings and the results. Based on the conclusions the hypothesis is either accepted or rejected. It must be remembered that eventhough the ex-post facto research is a valid method for collecting information regarding an event that had already occurred, this type of research has shortcomings, and that only partial control is possible.

Validity of the research

The researcher needs to validate the significance of their research. They need to be cautious regarding the extent to which their findings would be valid and significant and helpful in interpreting and drawing inferences from the obtained results.

Threats to Internal Validity in Causal-Comparative Research

Two weaknesses in causal-comparative research are lack of randomization and inability to manipulate an independent variable.

A major threat to the internal validity of a causal-comparative study is the possibility of a subject selection bias. The chief procedures that a researcher can use to reduce this threat include matching subjects on a related variable or creating homogeneous subgroups, and the technique of statistical matching.

Other threats to internal validity in causal-comparative studies include location, instrumentation, and loss of subjects. In addition, type 3 studies are subject to implementation, history, maturation, attitude of subjects, regression, and testing threats.

In short the Threats to Internal Validity in Causal-Comparative Research can be summerised as:

• Creating or finding homogeneous subgroups would be another way to control for an extraneous variable
• One way to control for an extraneous variable is to match subjects from the comparison groups on that variable
• Subject Characteristics
• The possibility exists that the groups are not equivalent on one or more important variables
• The third way to control for an extraneous variable is to use the technique of statistical matching

Other Threats

• Attitude
• Data collector bias
• History
• Instrument decay
• Instrumentation
• Location
• Loss of subjects
• Maturation
• Pre-test/treatment interaction effect
• Regression

Evaluating Threats to Internal Validity in Causal-Comparative Studies

Involves three sets of steps as shown below:

– Step 1: What specific factors are known to affect the variable on which groups are being compared or may be logically be expected to affect this variable?

– Step 2: What is the likelihood of the comparison groups differing on each of these factors?

– Step 3: Evaluate the threats on the basis of how likely they are to have an effect and plan to control for them.

Data Analysis

1- In a Causal-Comparative Study, the first step is to construct frequency polygons.

2-Means and SD are usually calculated if the variable involved    are quantitative…

3- The most commonly  used inference test is a’ t’ test for differences between  means

Analysis of data also involve a variety of descriptive and inferential statistics

The mean-which indicates the average performance of a group

The most commonly used Descriptive statistics are or some measures of a variable.

The Standard Deviation, which indicates how spread out  a set of score is around the mean, that is whether the scores are relatively homogenous or heterogenous around the mean.

The most commonly used inferential statistics are;

The t test used to determine whether the means of two groups are statistically different from one another.

Analysis of variance, used to determine if there is significant difference among the means of three or more groups

Chi square, used to compare group frequencies or to see if an event occurs

Limitations of use

1-There must be a “pre existing” independent variable, like years of study, gender, age, etc

2-There must be active variable- variable which the research can manipulate ,like the length and number of study session.

3-Lack of randomization, manipulation and control factors make it difficult to establish cause-effect relationships with any degree of confidence.

Causal Comparative: Conclusions

■ Researchers often infer cause and effect relationships based on such studies.

■ Conditions necessary, but not necessarily sufficient, to infer a causal relationship:

• A statistical relationship exists that is unlikely attributable to chance variation

• You have reason to believe the supposed exogenous variable preceded the endogenous.

• You can, with some degree of certainty, rule out other possible explanations.

Comparison of Causal-comparative method and Experimental method

-Neither method provides researchers with true experimental data

• -Causal comparative studies help to identify variables worthy of experimental investigation
• -Causal-comparative and experimental research both attempt to establish cause-effect relationships and both involve comparisons.
• -Ethical considerations often prevent manipulation of a variable that could be manipulated but should not be-If the nature of the independent variable is such that it may cause physical or mental harm to participants, the ethics of research dictate that it should not be manipulated
• -Experimental research the independent variable is manipulated by the researcher, whereas in causal-comparative research, the groups are already formed and already different on the independent variable
• -Experimental studies are costly in more ways than one and should only be conducted when there is good reason to believe the effort will be fruitful
• -Experimental study the researcher selects a random sample and then randomly divides the sample into two or more groups-Groups are assigned to the treatments and the study is carried out
• -Independent variables in causal-comparative cannot be manipulated, should not be manipulated, or simply are not manipulated but could be
• -Individuals are not randomly assigned to treatment groups because they already were selected into groups before the research began
• -Not possible to manipulate organismic variables such as age or gender
• -Students with high anxiety could be compared  to students with low anxiety on attention span, or the difference in achievement between first graders who attended preschool and first graders who did not could be examined.

Despite many key advantages, causal comparative research does have some serious limitations that should also be kept in mind

-Both the independent and dependent variables would have already occurred, it would not be possible to determine which came first.-It would be possible that some third variable, such as parental attitude might be the main influence on self-concept and achievement.

• -Causal-comparative studies do permit investigation of variables that cannot or should not be investigated experimentally, facilitate decision making, provide guidance for experimental studies, and are less costly on all dimensions.
• -Caution must be applied in interpreting results
• -Caution must be exercised in attributing cause-effect relationships based on causal-comparative research.
• -In causal-comparative research the researcher cannot assign participants to treatment groups because they are already in those groups.
• -Only in experimental research does the researcher randomly assign participants to treatment groups.
• -Only in experimental research is the degree of control sufficient to establish cause-effect relationships.
• -Since the independent variable has already occurred, the same kinds of controls cannot be exercised as in an experimental study
• -The alleged cause of an observed effect may in fact be the effect itself, or there may be a third variable
• -This conclusion would not be warranted because it is not possible to establish whether self-concept precedes achievement or vice versa.

Difference and Similarities in between Causal and Correlational Research

Causal-comparative research involves comparing (thus the “comparative” aspect) two groups in order to explain existing differences between them on some variable or variables of interest. Correlational research, on the other hand, does not look at differences between groups. Rather, it looks for relationships within a single group. This is a big difference…one is only entitled to conclude that a relationship of some sort exists, not that variable A caused some variation in variable B.In sum, causal-comparative research does allow one to make reasonable inferences about causation; correlational research does not.

Although some consider causal and correlational research as similar in nature, there exists a clear difference between these two types of research. Causal research is aimed at identifying the causal relationships among variables. Correlational research, on the other hand, is aimed at identifying whether an association exists or not.

Causal-comparative and correlational designs are similar as:

• Neither is experimental
• Neither involves manipulation of a treatment variable
• Relationships are studied in both
• Correlational:  focus on magnitude and direction of relationship
• Causal-Comparative:  focus on difference between two groups
• The basic similarity between causal-comparative and correlational studies is that both seek to explore relationships among variables.
• When relationships are identified through causal-comparative research (or in correlational research), they often are studied at a later time by means of experimental research.
• Both lack manipulation
• Both require caution in interpreting results
• Causation is difficult to infer
• Both can support subsequent experimental research

The key difference between causal and correlational research is that while causal research can predict causality, correlational research cannot. Through this article let us examine the differences between causal and correlational research further.

Difference in meaning

The correlational research attempts to identify associations among variables.  The key difference between correlational research and causal research is that correlational research cannot predict causality, although it can identify associations. Another difference that can be highlighted between the two research methods is that in correlational research, the researcher does not attempt to manipulate the variables. He merely observes.

• In terms of objective :Causal research aims at identifying causality among variables. This highlights that it allows the researcher to find the cause of a certain variable
• In terms of Prediction: In causal research, the researcher usually measures the impact each variable has before predicting the causality. It is very important to pay attention to the variables because, in most cases, the lack of control over variables can lead to false predictions. This is why most researchers manipulate the research environment.  In the social sciences especially, it is very difficult to conduct causal research because the environment can consist of many variables that influence the causality that can go unnoticed. Now let us move on to correlational research.
• In terms Definitions of Causal and Correlational Research: In Causal research aims at identifying causality among variables . In Correlational research attempts to identify associations among variables.
• In terms of Nature: In causal research, the researcher identifies the cause and effect . In correlational research, the researcher identifies an association.
• In terms of Manipulation: In causal research, the researcher manipulates the environment. In correlational research, the researcher does not manipulate the environment.
• In terms of Causality: In Causal research can identify causality. In  Correlational research cannot identify causality among variables
• In terms of  Subjects Subjects are notassigned to groups. Usually, there is only one group of subjects However, subjects are Randomly selected for participation. In Causal research subjects are not randomly assigned to control and experimental groups because it is logistically But, there are control & experimental groups in this type of design….just no random assignment.If possible, they should be randomly selected for participation.
• In terms of Variables: An important difference between causal-comparative and correlational research is that causal-comparative studies involve two or more groups and one independent variable, while correlational studies involve two or more variables and one group.   In Correlational research Two variables (X and Y) are measured and the strength and direction of the relationship is determined. In Causal research: Subjects are in pre-formed groups. But, unlike correlational and differential research, an independent variable ismanipulated and the groups are measured& compared on a dependent variable .
• In terms of  Statistics In Correlational research: Pearson product-moment, correlation (Pearson’s r. In  Causal research: Chi-square, t-test, ANOVA 
• In terms Conclusions : In Correlational research: Variable X co-varies with variable Y (i.e., there is a relationship between the two variables.)Cause and effect cannot be proven.In Causal research: While we may be able to draw some causal conclusions, we can’t do it with as much confidence as if we had used a true experimental design.

Strengths and Limitations of Causal-comparative Research

No research can be perfect in itself. All methods have their strengths as well as weaknesses. The same is applicable in the case of ex-post factor research too. The strengths of the ex-post facto research are: It is considered as a very relevant method in those behavioural researches where the variables can not be manipulated or altered.

Causal-Comparative Research has its limitations which should be recognized:

1. The independent variables cannot be manipulated. Subjects cannot be randomly, or otherwise, assigned to treatment groups.

2. Causes are often multiple and complex rather than single and simple.

For these reasons scientists are reluctant to use the expression cause and effect in  studies in which the variables have not been carefully manipulated.

They prefer to observe that when variable A appears, variable B is consistently associated, possibly for reasons not completely understood or explained.

Strengths of Causal-comparative Research

Causal-compative Research It is less time consuming as well as economical. It gives a chance to the researcher to analyse on basis of his personal opinion and then come out with the best possible conclusion. The weaknesses as well as the limitations of the ex-post facto research are: As discussed earlier, in Causal-compative Research   research, the researcher can not manipulate the independent variables. The researcher can not randomly assign the subjects to different groups. The researcher may not be able to provide a reasonable explanation for the relationship between the independent and dependent variables under study.

While predicting the causal relationships between the variables, the researcher falls prey to the bias called the post hoc fallacy. The concept of post hoc fallacy says that, it is a tendency of human to arrive at conclusions or predictions when two factors go together, one is the cause and the other is the effect. Because delinquency and parenthood go together, we may come to a conclusion that delinquency is the effect and the parenthood is the cause, whereas in reality the peer group to which the child belongs may be the actual reason.

It can therefore be concluded that the ex-post facto research holds a very good position in the field of behavioural sciences. It is the only method which is retrospective in nature, that is, with the help of this method one can trace the history in order to analyse the cause/ reason/action from an effect/behaviour/ event that has already occurred. Although it is a very significant method, yet it has certain limitations as well . The researcher can not manipulate the cause in order to see the alterations on its effect. This again marks a question on the validity of the findings of the research. Equally the researcher can not randomly assign the subjects in to groups and has no control over the variables. Yet, it is one of the very useful methods as it has several implications in the field