Research Designs are platforms to use to explore new knowledge in order to better understand phenomena, clarify explanations and identify causative factors. Although there are no real rules for choosing a design; one must realize the consequences for choosing one design over the other. One should choose the design that best attempts to address the conceptual issues presented.

Some questions that would help you decide which quantitative design is most appropriate for your study include:

1. How much do you know about the variables of interest?
2. Are you manipulating the levels of the independent variable?
3. How many independent variables are being tested?
4. How many levels do each independent variable have, and are these levels experimental or control?
5. How many groups of subjects are being tested?
6. How will subjects be selected?
7. Can subjects be randomly assigned to groups?
8. Is pretest data being collected?
9. Are you interested in examining differences or similarities between groups?
10. How often will observations of responses be made?
11. What is the temporal (time) sequence of interventions and measurements?

• Manipulation. What is actively directed or managed by the researcher.  Usually refers to giving a treatment or not giving a treatment (or giving one of many levels of treatment) to a group of subjects.  Assigning groups to various levels of the independent variable(s).
• Control. The extent to which the researcher can manage extraneous sources that might affect a study and lead to incorrect scientific conclusions.
• Random selection. Randomly drawing research subjects from the target population.
• Random assignment. Allocating subjects to treatment and control conditions in a nonsystematic way, using a method that is known to be random.
• Probability. (as related to research findings) The likelihood that research findings are low in uncertainty and error; that the findings are trustworthy and believable.
• Bias. The difference between the true and the observed.  Undesirable influences on outcomes in research.
• Causality. Determining the cause-and-effect relationship(s) that exist between variables.

The purpose of an experimental design is to provide a structure for evaluating the cause-and-effect relationship between a set of independent and dependent variables.

Some of the elements of an experimental design:

1. Manipulation - the researcher manipulates the levels of the independent variable. This usually means that you are looking at the effect of some treatment on one group of subjects (the treatment group), and comparing that to another group of subjects who do not receive the treatment (the control group). The type of treatment here is the independent variable that gets "manipulated". The idea of manipulation is that the researcher "manipulates" by assigning some subjects to the treatment group, and the other subjects to the control group. The researcher does not have to use a control group, the design may incorporate two or more "treatments", or various levels of the same treatment that are compared. It is the effect of this "manipulation" that is measured to determine the result of the experimental treatment. Another variation on the control group is the "attention control group".  This group would get some "neutral" experimental attention/treatment, but not the treatment variable being studied.  This allows the researcher to look at three groups (experimental, attention control, and "silent" control) and better control for the Hawthorne effect.
2. Control - the researcher incorporates elements of control so that the evidence supporting a causal relationship can be interpreted with confidence. Using a control group is only one aspect of control. Control is acquired through manipulation, randomization, the use of control groups, and methods to handle extraneous variables. (more on control below).
3. Randomization - subjects are randomly assigned to at least two comparison groups.
• With randomization, extraneous variables are evenly distributed between or among groups; even variables you have not thought about yet may be controled through randomization.
• Randomization (or random assignment) is not the same thing as random sampling or random selection.

1. Selective control - the use of randomization (see above). For instance: if gender is an extraneous variable, then by randomly assigning subjects to groups, the number of males and females in each group should be evenly distributed, and the variable gender should not effect the outcome for one group versus the other.
2. Physical control - control of an extraneous variable by making it a constant.  For instance: if gender is an extraneous variable, then only study females. This way the variable gender will not effect the outcome of one group versus the other. The disadvantage of this approach is that it limits your ability to generalize your results. If you only study females, you do not know how this treatment will effect males.
3. Statistical control - include the extraneous variables in the design. For instance: add gender as another independent variable in your study. This can be a very powerful way to control the effect of an extraneous variable like gender. You will actually analyze the effect of the variable, and know how gender effects the outcome of your study.

• In order to postulate a cause and effect relationship, the following conditions must be met:

1. the causal variable and the effect variable must be associated with each other
2. the cause must precede the effect
3. the relationship/association must not be explainable by another (extraneous) variable

• The validity of the conclusion of cause and effect depends on the control of extraneous variables.

• can test cause and effect relationships
• provides better control of the experiment and minimizes threats to validity.

• for many studies, all extraneous variables cannot be identified or controlled
• not all variables can be experimentally manipulated
• some experimental procedures may not be practical or ethical in a clinical setting
• the Hawthorne effect may be strong in an experimental situation, and not so in an ex post facto study.

DePoy gives three criteria that need to be met to determine that a true experimental design is appropriate:

1.     there is sufficient development of theory to warrant the proposition of causality
2.     you have asked a causal quantitative question
3.     conditions exist, and legal and ethical issues allow for the random selection of subjects (random sampling), random assignment to groups, use of a  pretest/posttest, and manipulation of the levels of the independent variable(s), including a control group

It is not always possible to implement a design that meets the three criteria of a true experimental study (manipulation, control and randomization). Quasi-experimental designs differ from experimental designs because either there is no control group or randomization cannot occur.

Types of quasi experimental designs:

1. Nonequivalent control group design:

• One of the most frequently used designs.
• It is the same as experimental except control group subjects are not randomly assigned.
• Advantages -
• in spite of the lack of randomization, the use of a control group and posttest increases the strength of the design
• good pretest data allows for improvement of analysis results
• Disadvantages -
• Threats to internal and external validity, i.e., selection - threat as subjects are not randomly selected.
• There are limits to extent one can infer causality.

• Similar to after only experimental group design but randomization is not used to assign subjects to groups.
• This design assumes groups are equivalent after the independent variable is introduced.
• This type of design can be used to study effects of natural or man made catastrophes such as hurricanes, floods or plane crashes.
• Use of good demographic data to further describe the subjects in each group would strengthen this type of study design.
• Advantage - Design is simple and quick.
• Disadvantage - Cannot be used to assess causality. Reduces internal validity.

• This is useful for determining trends where it is not possible to have a control group.
• No randomization occurs when only one group is available.
• Time series design significantly increases the strength of the study.
• Advantages -
• Repeated pretreatment observations help to control for maturation.
• Repeated post treatment measures allow one to determine if change is maintained over time.
• Disadvantage - History may threaten internal validity.

These designs are used in situations where manipulation of an independent variable, control or randomization are not involved. These designs are focused to describe and measure independent and dependent variables. They are sometimes called descriptive research designs. Nonexperimental research does not prove causality. The goal is to describe phenomena and explore and explain relationships between variables.

Types of non experimental designs:

1. Descriptive/Exploratory Survey Studies-

• This is used to examine differences or similarities between variables.
• This is a comparatively weak quantitative design, often used when little is known about a topic or to initially explore a research question.
• There is no random assignment or control.
• The design can be used with both quantitative and qualitative methods.  Rember that because of the differences between qualitative and quantitative research in philosophy, and the order in which steps are taken, the two studies would be very different.
• Survey designs gather information from a segment of the population.
• Random selection of subjects for the survey can increase the ability to generalize information. (Random selection is not the same as random assignment - assignment occurs before the experimental condition is experienced.)
• Instruments commonly used for this type design include interviews (telephone and in person), questionnaires (mailed or administered in person).

• A correlation is the examination the strength of the relationship(s) between 2 or more variables.
• If subjects were assigned to treatment and control groups, that group assignment and manipulation of the independent variable would turn this into an experimental or quasi-experimental study (depending on the level of control of extraneous variables).
• If there is no manipulation of the independent variable and there is no assignment of groups this is a nonnexperimental design.
• Data can be collected through the use of a questionnaire, interviews, or it can be measured with any of a variety of instruments.
• Advantage of nonexperimental correlational designs is they are straightforward, usually inexpensive and quick. They may be used as preliminary research for future studies.
• Disadvantage is only the relationship between variables can be determined; not causation

• A form of correlational study where the event of interest has already occurred.
• There is no assignment of subjects to groups - the researcher is studying the groups after they have already been formed.
• This design is used to describe existing relationships between variables and to determine if there is a correlation between variables. Remember, correlation does not indicate causation.

• an exploration of relationships among independent variables to make predictions about the dependent variable
• advantage - helps make intelligent decisions
• disadvantage - a relationship does not ensure cause-and-effect (see section on cause-and -effect)

• examine data across different groups at one point in time
• advantage - less time consuming or expensive
• disadvantage - less control of extraneous variables

• collect data from one group over different points in time
• advantage - a subject serves as his/her own control, which greatly controls extraneous variables
• disadvantage - costly and time consuming - may take decades to finish study, depending on the study

• In order to postulate a cause-and-effect relationship, the following conditions must be met:
1. the causal variable and the effect variable must be associated with each other
2. the cause must precede the effect
3. the relationship/association must not be explainable by another (extraneous) variable
• The validity of the conclusion of cause and effect depends on the control of extraneous variables. For the most part, nonexperimental studies do not control extraneous variables. Therefore, even though a statistically significant relationship may be shown, there are not grounds to postulate a cause and effect relationship unless conditions 1 through 3 are met. This is one of the most common mistakes made in research.

• A research method that provides a detailed analysis of a single individual, group, institution or problem of interest. In the last 2 decades, the case study method has evolved into a well respected set of design strategies. They are used in health and social science inquiry.
• Case studies may yield either quantitative or qualitative data. The key lies in how the research question or query is framed.
• Criteria for determining when to chose this design -
1. If interested in examining phenomenon in it's current context
2. If want to contribute to the support and development of theory
3. If want to explore in-depth an atypical, different case or when it is impossible to conduct a design group.
• Cannot generalize from case study findings to a population!
• Case study designs includes single and multiple case designs.
• They may be descriptive, exploratory, explanatory or causal.
• Data is content analyzed and presented in narrative form.