Research design and types

A research Design is a procedural plan that is adopted by the researcher to answer questions validly, objectively, accurately, and economically. According to Philliber, Schwab, & Samsloss, 1980, the Research design is considered as a “blueprint” for research, dealing with at least four problems:

  1. Which questions to study,
  2. Which data are relevant,
  3. What data to collect, and
  4. How to analyse the results.


A research design is the arrangement of conditions for the collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in the procedure (According to Seltiz, Deutsch, and cook, 1962).

Through research design researcher decides to communicate to others on the decision regarding the study design that proposes to use, how the information has been collected, analyzed, and how the findings would be addressed.


A research design is a plan, structure, and strategy of investigation, so conceived as to obtain answers to research questions or problems. The plan is the complete program of the research. It includes an outline of what the investigator will do from writing the hypothesis and its operational implications to the final analysis of data. – Kerlinger, 1986.

A traditional research design is a blueprint or detailed plan for how a research study is to be completed – operationalizing variables so they can be measured, selecting a sample of interest to study, collecting data to be used as a basis for testing hypothesis, and analyzing the results – Thyer 1993.

Green and Tull, “ It is the specification of techniques and processes for obtaining the information required. It is the overall operational pattern or framework of the project which states what data is to be gathered from which source by what processes.”


Failing to distinguish between design and method leads to poor evaluation of designs. Equating cross-sectional designs with questionnaires, or case studies with participant observation means that the designs are often evaluated against the strengths and weaknesses of the method rather than their ability to draw relatively unambiguous conclusions or to select between rival plausible hypotheses.


Research design and methods types


A within-subject design is a type of experimental design in which all participants are exposed to every treatment or condition. The same group of subjects is treated differently in different experimental conditions and finally, their dependent variables scores are compared. Called repeated measure because the same individuals are treated differently at different times, and we compare their scores as a result of different experimental treatments.

The term “treatment” is used to describe the different levels of the independent variable, the variable that’s controlled by the experimenter. These levels could be different\treatments”, or they may be different measurements for the same treatment (e.g., height and weight as outcomes for each subject), or they may be repetitions of the same outcome over time (or space) for each subject. In the broad sense, the term repeated measure is a synonym for a within-subject factor, although often the term repeated measures analysis is used in a narrower sense to indicate the specific set of analyses. Can be divided into 2 parts:

  • A design with 2 conditions and many subjects,
  • Design with more than 2 conditions and many subjects.


A between-subjects design is a way of avoiding the carryover effects that can plague within-subjects designs, and they are one of the most common experiment types in some scientific disciplines, especially psychology. The basic idea behind this type of study is that participants can be part of the treatment group or the control group, but cannot be part of both. If more than one treatment is tested, a completely new group is required for each.


TWO RANDOMIZED GROUP DESIGNS– A two-randomized-group design is so-called because here the subjects are randomly assigned to two groups only. Subsequently, the scores of all subjects of these two groups on the DV will be recorded and subjected to statistical analysis. Usually t-test or its non-parametric substitute, the Mann Whitney-U-Test is applied in a two-randomized-group design. Underwood (1966) has suggested two primary ways through which unbiased groups or random groups of subjects can be formed:

  1. Captive Assignment: all subjects are individually known to the experimenter by name and they all are present at one time. For random assignment of subjects into groups, the following procedure can be used:
    • Table of random numbers
    • Using slips
    • Dived arbitrary according to setting position
    • Arrange alphabetical order, odd-even.
  2. Sequential Assignment: experimenter does not know the detail of the subjects. He is simply aware of the fact that a certain number of subjects will participate in the experiment. This design usually continues several days or weeks. Two main technique been used are:
    • Complete randomization
    • Block randomization


  • In a two-randomized-groups design usually, the experimenter loses some subjects.
  • Behavioural research have two general purpose: one is to determine which of the many independent variables tends to influence the dependent variable most and another is to determine what type of relationship exists between the influential independent variable and the dependent variable. A two-randomized-groups design serves the first purpose but does not serve the second .

MORE THAN TWO-RANDOMIZED GROUP DESIGN- Multi-group design in which there are three or more conditions or values of the independent variables. In multi-group design, the two most common statistics applied are the ANOVA and Duncan range test.


  • Multi-group design or more-than-two-randomized group design is superior to the two-randomized-group design.
  • In multi-group design it is easier to establish the adequate relationship between the IV and DV because the design utilized several conditions or values of the IV.


Matched-group design: like the randomized groups, the matched-group design may be a two-matched-group or a more-than-two-matched-group design. Whatever the types, in the matched-group design all subjects are first tested on a common task or a pre-test measure, and then, they are formed into groups on the basis of the performance of the common task or the matching variable. The groups thus formed are said to be equivalent groups. It’s a simple way of establishing the fact that all the groups have equal dependent variable values prior to the administration of the experimental treatment. The matched-group design is based upon the principle that the experimental unit can form a block or group.

Way of matching-

  • Selection of the Matching variable: We have attempted to equate our two groups with respect to their mean values on the DV. If the matching variable is highly correlated with the DV scores, our matching has been successful. In short, an initial measure of the DV is the best possible criterion by which to match individuals to form two equivalent group prior to the administration of the experiment treatment.
  • Matching by pairs: On the basis of the obtained scores by the subjects, the experimenter matches subjects in a way that each subject has a corresponding partner in the matched group.
Subject NoScores
  • Matching in terms of mean & SD: Mean & SD are commonly selected as measures of central tendency and variability of the distribution respectively. Three methods of matching in terms of mean & SD.
    1. Random-block Design,
    2. Method of counterbalancing order,
    3. Block-Repetition Method
Which method of Matching should the experimenter follow?
It all depends upon the experimenter’s purpose.
Random blocks methodMethod of counterbalancing orderBlock-repetition method
Means 139.375139.000138.625139.000138.750139.250140.375138.750137.875
SDs 13.9313.2313.1513.6013.5513.1713.4113.5513.25
Random-block Design: The number of subjects in each block is kept constant and is determined by the number of values of IV. Subsequently, subjects from each block are randomly assigned to the different groups as required by the different conditions/values of IV.
Method of counterbalancing order: Namely, ABCCBA…etc., a pattern would be adopted for assigning the subjects into three groups: 1st subject to A group, 2nd to B, 3rd to C, 4th to D, and so on. Thus subjects assigned to the different groups will be matched, groups.
Block-Repetition Method: A block of 3 conditions is made in the natural sequence and the same order of the sequence of conditions repeated in each block., For eg, ABC in one block, which is further, maintained for upcoming sequences.


Randomized-group design, randomized-block design, matched-group design all are appropriate for studying a single Independent Variable at a time. —A factorial design may be defined as a design in which the selected value of two or more independent variables is manipulated in all possible combinations so that their independent, as well as interactive variables, are manipulated in all possible combinations so that their independent, as well as interactive effects upon the dependent variable, may be studied. A few characteristics of the factorial design are listed below:

  • Two or more Independent Variable are manipulated in all possible combinations.
  • Different subgroups or subjects must serve under every possible combination of the independent variable.
  • The experimenter can study the independent effect as well as the interactive effect of the two or more independent variables.
factorial design example


  • Manipulated two or more than two independent variables simultaneously.
  • Also permit evaluation of interaction upon DV.
  • More comprehensive and can generalized to a wider range.


  • Experimental setup and resulting statistical analysis become so complex.
  • When the number of treatment combinations or treatments become large, it becomes difficult for the experimenter to select a homogeneous experimental unit (or subject).
  • Some treatment combinations arising out of the simultaneous manipulation of several independent variable become meaningless.

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