The document discusses various topics related to experimental research methods, including defining research problems, sampling techniques, research designs, variables, hypothesis testing, and statistics. Specifically, it defines key terms like independent and dependent variables, different sampling methods, research designs like experimental and quasi-experimental, and statistical analyses commonly used in experimental research like t-tests, ANOVA, regression, and chi-square tests.

1. POINTERS!!!
1. Experimental research is commonly used in sciences such as sociology and psychology, physics,
chemistry, biology and medicine etc.
2. The experimental method
is a systematic and scientific approach to research in which the researcher manipulates one or more
variables, and controls and measures any change in other variables.
3. After deciding the topic of interest, the researcher tries to define the research problem. This helps the
researcher to focus on a more narrow research area to be able to study it appropriately. Defining the research
problem helps you to formulate a research hypothesis, which is tested against the null hypothesis.
4. Sampling Groups to Study
Sampling groups correctly is especially important when we have more than one condition in the experiment.
One sample group often serves as a control group, whilst others are tested under the experimental conditions.
Deciding the sample groups can be done in using many different sampling techniques. Population sampling may
chosen by a number of methods, such as randomization, "quasi-randomization" and pairing.
Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample
size to minimize chances of random errors.
5. Probability sampling is a sampling technique wherein the samples are gathered in a process that gives all the
individuals in the population equal chances of being selected.
6. Convenience sampling is a non-probability sampling technique where subjects are selectedbecause of
their convenient accessibility and proximity to the researcher.
7. Non-probability sampling is a sampling technique where the samples are gathered in a process that
does not give all the individuals in the population equal chances of being selected.
8. Random sampling is one of the most popular types of random or probability sampling. In this
technique, each member of the population has an equal chance of being selected as subject. The entire process
of sampling is done in a single step with each subject selected independently of the other members of
the population.
9. Systematic sampling is a random sampling technique which is frequently chosen by researchers for its
simplicity and its periodic quality. In systematic random sampling, the researcher first randomly picks the
first item or subject from the population. Then, the researcher will select each n'th subject from the list.
10. Stratified sampling is a probability sampling technique wherein the researcher divides the entire
population into different subgroups or strata, then randomly selects the final subjects proportionally
from the different strata.
11. The research design refers to the overall strategy that you choose to integrate the different components of
the study in a coherent and logical way, thereby, ensuring you will effectively address the researchproblem; it
constitutes the blueprint for the collection, measurement, and analysis of data.

2. There are two main approaches to a research problem:
 Quantitative Research
 Qualitative Research
12. Different Research Methods
There are various designs which are used in research, all with specific advantages and disadvantages. Which
one the scientist uses, depends on the aims of the study and the nature of the phenomenon:
Descriptive Designs
Aim: Observe and Describe
 Descriptive Research
 Case Study
 Naturalistic Observation
 Survey,
13. Correlational Studies
Aim: Predict
 Case Control Study
 Observational Study
 Cohort Study
 Longitudinal Study
 Cross Sectional Study
 Correlational Studies in general
14. Semi-Experimental Designs
Aim: Determine Causes
 Field Experiment
 Quasi-Experimental Design
 Twin Studies
15. Experimental Designs
Aim: Determine Causes
 True Experimental Design
 Double-Blind Experiment

3. 16. Reviewing Other Research
Aim: Explain
 Literature Review
 Meta-analysis
 Systematic Reviews
17. Test Study Before Conducting a Full-Scale Study
Aim: Does the Design Work?
 Pilot Study
18. Central Tendency and Normal Distribution
Much data from the real world is normal distributed, that is, a frequency curve, or a frequency distribution,
which has the most frequent number near the middle. Many experiments rely on assumptions of a normal
distribution. This is a reason why researchers very often measure the central tendency in statistical research,
such as the mean(arithmetic mean or geometric mean), median ormode.
The central tendency may give a fairly good idea about the nature of the data (mean, median and mode shows
the "middle value"), especially when combined with measurements on how the data is distributed. Scientists
normally calculate the standard deviation to measure how the data is distributed.
19. Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true.
20. The usual process of hypothesis testing consists of four steps.
1. Formulate the null hypothesis (commonly, that the observations are the result of pure chance) and
the alternative hypothesis (commonly, that the observations show a real effect combined with a component
of chance variation).
2. Identify a test statistic that can be used to assess the truth of the null hypothesis.
3. Compute the P-value, which is the probability that a test statistic at least as significant as the one
observed would be obtained assuming that the null hypothesis were true. The smaller the -value, the stronger
the evidence against the null hypothesis.
4. Compare the -value to an acceptable significance value (sometimes called an alpha value). If ,
that the observed effect is statistically significant, the null hypothesis is ruled out, and the alternative hypothesis
is valid.
21. What Are Scientific Variables?
In science, a variable is any item, factor, or condition that can be controlled or changed. There are three types
of variables in scientific experiments, but we will define them later in the lesson.
22. Types of Variables

4. The first variable type is called the independent variable. This variable is the one that is manipulated or
changed by the scientist.
The second type of variable is the one that is observed or measured in the experiment, and it is known as the
dependent variable. You can remember this because the observation or measure of the dependent variable will
change as the independent variable is altered.
A control variable is the one element that is not changed throughout an experiment, because its unchanging
state allows the relationship between the other variables being tested to be better understood.
23. Statistics
Linear regression analysis is a powerful technique used for predicting the unknown value of a variable
from the known value of another variable.
Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable
from the known value of two or more variables- also called the predictors.
An independent one-sample t-test is used to test whether the average of a sample differ significantly from
a population mean, a specified value μ0.
When you compare each sample to a "known truth", you would use the (independent) one-sample t-test. If you
are comparing two samples not strictly related to each other, the independent two-sample t-test is used.
Any single sample statistical test that uses t-distribution can be called a 'one-sample t-test'. This test is used
when we have a random sample and we want to test if it is significantly different from a population mean.
Assumptions
This test is one of the most popular small sample test widely used in all disciplines - medicine, behavioral
science, physical science etc. However, this test can be used only if the background assumptions are satisfied.
 The population from which the sample has been drawn should be normal - appropriate statistical methods
exist for testing this assumption (For example the Kolmogorov Smirnov non parametric test). It has however
been shown that minor departures from normality do not affect this test - this is indeed an advantage.
 The population standard deviation is not known.
 Sample observations should be random.
Small Sample Test
This test is a small sample test. It is difficult to draw the clearest line of demarcation between large and small
samples. Statisticians have generally agreed that a sample may be considered small if its size is < 30 (less than
30).

5. Student's t-test is a test which can indicate whether the null hypothesis is correct or not. In research it is often
used to test differences between two groups (e.g. between a control group and an experimental group).
The t-test assumes that the data is more or less normally distributed and that the variance is equal (this can be
tested by the F-test).
 Dependent T-Test for Paired Samples
The dependent t-test for paired samples is used when the samples are paired. This implies that each
individual observation of one sample has a unique corresponding member in the other sample.
 one sample has been tested twice (repeated measures)
or,
 two samples have been "matched" or "paired", in some way. (matched subjects design)
A Z-Test is similar to a t-test, but will usually not be used on sample sizes below 30.
A Chi-Square can be used if the data is qualitative rather than quantitative.
chi-square applies when the variables are nominal or ordinal. Chi-square tests if one group of amounts is higher
or lower than you would expect by coincidence.
“the goal of a Chi-square goodness-of-fit test is to determine whether a set of frequencies or proportions is similar to and
therefore “fits” with a hypothesized set of frequencies or proportions”
A test of independence is a two variable Chi-square test. Like any Chi-square test the data are frequencies, so there are
no scores and no means or standard deviations. “the goal of a two-variable Chi-square is to determine whether or not the
first variable is related to—or independent of—the second variable”.
An ANOVA, or Analysis of Variance, is used when it is desirable to test whether there are different variability
between groups rather than different means. Analysis of Variance can also be applied to more than two groups.
The F-distribution can be used to calculate p-values for the ANOVA.
Analysis of Variance
 One way ANOVA
A One-Way ANOVA (Analysis of Variance) is a statistical technique by which we can test if three or
more means are equal. It tests if the value of a single variable differs significantly among three or more
levels of a factor.
 Two way ANOVA
A Two-Way ANOVA is useful when we desire to compare the effect of multiple levels of two factors and
we have multiple observations at each level.

6.  Factorial ANOVA
 Experiments where the effects of more than one factor are considered together are called 'factorial
experiments' and may sometimes be analyzed with the use of factorial ANOVA.
 For instance, the academic achievement of a student depends on study habits of the student as well as
home environment. We may have two simple experiments, one to study the effect of study habits and
another for home environment.
 Repeated Measures and ANOVA
RepeatedMeasures ANOVA is a technique used to test the equality of means.
It is used when all the members of a random sample are tested under a number of conditions. Here, we have
different measurements for each of the sample as each sample is exposed to different conditions.
However, it is used when all the members of a random sample are tested under a number of conditions. Here,
we have different measurements for each of the sample as each sample is exposed to different conditions.
In other words, the measurement of the dependent variable is repeated. It is not possible to use the standard
ANOVA in such a case as such data violates the assumption of independence of data and as such it will not be
able to model the correlation between the repeated measures.