This document discusses variables, hypothesis development, and hypothesis testing. It defines variables as measurable characteristics that can assume different values. Hypothesis development involves asking a research question, conducting preliminary research, and formulating a hypothesis statement. A hypothesis should specify the variables, population, and predicted relationship. Hypotheses can be null or alternative, directional or non-directional. Hypothesis testing involves stating hypotheses, collecting data, performing a statistical test, deciding whether to reject or fail to reject the null hypothesis, and presenting findings.

1. Variable and Hypothesis
Development
Rabia Ehsan

2. CONTENTS
• Variables and Types of Variables
• Hypothesis development
• Definition and statement of hypothesis
• Formats of hypothesis
• Deductive and Inductive methods
• Null and alternate hypothesis
• Directional and non directional hypothesis
• Hypothesis testing with qualitative research

3. VARIABLE
• Variable is any characteristic of an individual. It can take on different
values for different individuals
• A variable is a characteristic that can be measured and that can assume
different values.
For Example
• Age, gender, height, weight, grades, visual acuity etc.

4. Types of Variable
Variables
Quantitative
or Numeric
Discrete Continuous
Qualitative
Or Categorical
Nominal Ordinal

5. Discrete Variable
• Quantitative discrete variables are variables for which the values it
can take are countable and have a finite number of possibilities.
For Example
• Number of children per family
• Number of students in a class
• Number of citizens of a country

6. Continuous Variable
• On the other hand, quantitative continuous variables are variables
which can take any value within an interval with decimal figures.
For Example
• Weight
• Height
• Blood pressure
• Sugar level
• Pulse rate

7. Nominal Variable
• A qualitative nominal variable are used to classify objects,
individuals or groups.
For Example
• Gender
• Country
• Religion
• Status

9. Ordinal Variable
On the other hand, a qualitative ordinal variable is a qualitative
variable with an order implied in the levels.
For Example
Levels of Pain
1.Mild
2.Moderate
3.Severe

11. Scales of Measurements

12. Scale
Variable
Interval Ratio

14. Other Variables
• Independent variable
• Dependent variable
• Connecting variable
• Composite variable
• Universal variable

15. Hypothesis Development
Ask a question
• Writing a hypothesis begins with a research question that you want to
answer. The question should be focused, specific, and researchable within
the constraints of your project.
Do some preliminary research
• Your initial answer to the question should be based on what is already
known about the topic. Look for theories and previous studies to help you
form educated assumptions about what your research will find.
Formulate your hypothesis
• Now you should have some idea of what you expect to find. Write your
initial answer to the question in a clear, concise sentence.

16. Refine Your Hypothesis
You need to make sure your hypothesis is specific and testable. There are various ways of
phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis
should contain:
 The relevant variables
 The specific group being studied
 The predicted outcome of the experiment or analysis
Phrase your hypothesis in three ways
• To identify the variables, you can write a simple prediction in if…then form. The first part of the
sentence states the independent variable and the second part states the dependent variable.
Write A Null Hypothesis
• If your research involves statistical hypothesis testing you will also have to write a null
hypothesis. The null hypothesis is the default position that there is no association between the
variables. The null hypothesis is written as H0, while the alternatives hypothesis is H1 or Ha.

17. Statement of Hypothesis
1.State the problem that you are trying to solve. Make sure that the
hypothesis clearly defines the topic and the focus of the experiment.
2.Try to write the hypothesis as an if-then statement.
3.Define the variables.

18. For Example
• A study designed to look at the relationship between sleep deprivation and
test performance might have a hypothesis that states,
• "This study is designed to assess the hypothesis that sleep-deprived
people will perform worse on a test than individuals who are not sleep-
deprived."

19. Formats of Hypothesis
• A hypothesis often follows a basic format of "If {this happens}
then {this will happen}."
• One way to structure your hypothesis is to describe what will
happen to the dependent variable if you make changes to
the independent variable.
• The basic format might be: "If {these changes are made to a
certain independent variable}, then we will observe {a change in
a specific dependent variable}."

20. For Example
• "Students who eat breakfast will perform better on a math exam
than students who do not eat breakfast."
• "Students who experience test anxiety prior to an English exam
will get higher scores than students who do not experience test
anxiety."

21. Approaches to Format Hypothesis
Inductive Approach
• Inductive approach is concerned with the generation of new theory
emerging from the data,
Deductive Approach
• Deductive approach starts with a statement or hypothesis and then tests to
see if it's true through observation.

22. For Example
Inductive Approach
• My mother is Irish. She has blond hair. Therefore, everyone from Ireland
has blond hair.
Deductive Approach
• My mother is Irish. Everyone from Ireland has blond hair. Therefore, my
mother has blond hair.

24. Hypothesis
• A supposition or proposed explanation made on the basis of limited
evidence as a starting point for further investigation.
• A hypothesis is a tentative statement about the relationship
between two or more variables.
• It is a specific, testable prediction about what you expect to happen
in a study.
• It includes components like variables, population and the relation
between the variables.
.

25. Types of Hypothesis
Null Hypothesis
• It is a statement about a population parameter.
• Null Hypothesis is also called statistical hypothesis because this
type of hypothesis is used for statistical testing and statically
interpretation.
• We test the likelihood of this statement being true in order to
decide whether to accept or reject our alternative hypothesis.
• The null hypothesis predicts that, there is no relationship
between the independent variable and dependent variable.

26. Alternative Hypothesis
• A statement that directly contradicts the null hypothesis.
• Alternative Hypothesis is also known as Research Hypothesis.
• We determine whether or not to Accept or reject this statement
based on the likelihood of the null (opposite) hypothesis being
true.
Example:
• Acceptance and rejection.
• Probability value (P-value).
• Upper and lower limit of confidence interval.

27. Directional Hypothesis
• The alternative hypothesis contains the less than (<) or
greater than sign (>).
• This indicates that we are testing whether or not there is
a positive or negative effect.

28. Examples
• The amount of sunshine enhances the growth of plants.
• Children who do homework without the TV on produce better
results than those who do homework with the TV on.
• Pesticide will influence the mean plant growth to decrease.

29. Non- Directional Hypothesis
• The alternative hypothesis contains the not equal (“≠”) sign.
This indicates that we’re testing whether or not there is some
effect, without specifying the direction of the effect.
• Non-directional hypothesis tests are also called “two-tailed”
tests.

30. Examples
• There is difference between average pulse rate of men and
women.
• The amount of sunshine affects the growth of plants.
• Studying technique effect on mean score of students in the
exam.

31. Hypothesis Testing
Hypothesis testing is a formal procedure for investigating our ideas about
the world using statistics. It is most often used by scientists to test specific
predictions, called hypotheses, that arise from theories.
There are 5 main steps in hypothesis testing:
1. State your research hypothesis as a null hypothesis and alternate
hypothesis (Ho) and (Ha or H1).
2. Collect data in a way designed to test the hypothesis.
3. Perform an appropriate statistical test.
4. Decide whether to reject or fail to reject your null hypothesis.
5. Present the findings in your results and discussion section.

32. Step 1: State Your Null And Alternate Hypothesis
• After developing your initial research hypothesis (the prediction that you want to
investigate), it is important to restate it as a null (Ho) and alternate (Ha) hypothesis
so that you can test it mathematically.
• The alternate hypothesis is usually your initial hypothesis that predicts a
relationship between variables. The null hypothesis is a prediction of no
relationship between the variables you are interested in.
Step 2: Collect Data
• For a statistical test to be valid, it is important to perform sampling and collect
data in a way that is designed to test your hypothesis. If your data are not
representative, then you cannot make statistical inferences about the population
you are interested in.

33. Step 3: Perform A Statistical Test
• There are a variety of statistical tests available, but they are all based on
the comparison of within-group variance (how spread out the data is
within a category) versus between-group variance (how different the
categories are from one another).
• If the between-group variance is large enough that there is little or no
overlap between groups, then your statistical test will reflect that by showing
a low p-value. This means it is unlikely that the differences between these
groups came about by chance.
• Alternatively, if there is high within-group variance and low between-group
variance, then your statistical test will reflect that with a high p-value. This
means it is likely that any difference you measure between groups is due to
chance.

34. Step 4: Decide Whether To Reject Or Fail To
Reject Your Null Hypothesis
• Based on the outcome of your statistical test, you will have to decide whether to
reject or fail to reject your null hypothesis.
• In most cases you will use the p-value generated by your statistical test to guide
your decision. And in most cases, your predetermined level of significance for
rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5%
chance that you would see these results if the null hypothesis were true.
Step 5: Present Your Findings
• The results of hypothesis testing will be presented in the results and discussion
sections of your research paper.
• In the results section you should give a brief summary of the data and a summary
of the results of your statistical test (for example, the estimated difference between
group means and associated p-value). In the discussion, you can discuss whether
your initial hypothesis was supported by your results or not.