Wednesday, 2 April 2014


A hypothesis is a tool of quantitative studies.
It is a tentative and formal prediction about the relationship between two or more variables in the population being studied, and the hypothesis translates the research question into a prediction of expected outcomes.
So…a hypothesis is a statement about the relationship between two or more variables that we set out to prove or disprove in our research. study.
To be complete the hypothesis must include three components:  
  •  The variables.
  •  The population.
  •  The relationship between the variables.

A hypothesis should be:
  •  stated clearly using appropriate terminology;
  •  testable;
  •  a statement of relationships between variables;
  •  limited in scope (focused).

Examples of a hypothesis are:
  • Health Education programmes influence the number of people who smoke.
  • Newspapers affect people's voting pattern.
  • Attendance at lectures influences exam marks.
  • Diet influences intelligence.

Types of hypotheses
There are different types of hypotheses:
  • Simple hypothesis - this predicts the relationship between a single independent variable (IV) and a single dependent variable (DV) 

For example:   
  • Lower levels of exercise postpartum (IV) will be associated with greater weight retention (DV).  
  • IV = independent variable
  • D V = dependent variable

  • Complex hypothesis - this predicts the relationship between two or more independent variables and two or more dependent variables.

1. Example of  a complex multiple independent variable hypothesis:
Low risk pregnant women (IV) who:  
  •        value health highly;                                                   
  •        believe that engaging in health promoting behaviours will result in positive outcomes;
  •        perceive fewer barriers to health promoting activities;
  •        are more likely than other women to attend pregnancy-related education programmes (DV).  

2. Example of a complex multiple dependent variable hypothesis:
The implementation of an evidence based protocol for urinary incontinence (IV) will result in (DV):
  •             decreased frequency of urinary incontinence episodes;
  •        decreased urine loss per episode;
  •        decreased avoidance of activities among women in ambulatory care settings. 

Hypotheses can be stated in various ways as long as the researcher specifies or implies the relationship that will be tested.

For example:
  • Lower levels of exercise postpartum are associated with greater weight retention.
  • There is a relationship between level of exercise postpartum and weight retention.
  • The greater the level of exercise postpartum, the lower the weight retention.
  • Women with different levels of exercise postpartum differ with regard to weight retention.
  • Weight retention postpartum decreases as the woman's level of exercise increases.
  • Women who exercise vigorously postpartum have lower weight retention than women who do not.

  •  Directional hypotheses  
These are usually derived from theory.
They may imply that the researcher is intellectually committed to a particular outcome.
They specify the expected direction of the relationship between variables i.e. the researcher predicts not only the existence of a relationship but also its nature.

  •  Non-directional hypotheses
Used when there is little or no theory, or when findings of previous studies are contradictory.
They may imply impartiality.
Do not stipulate the direction of the relationship.

Associative and causal hypotheses
  •  Associative hypotheses
Propose relationships between variables - when one variable changes, the other changes.
Do not indicate cause and effect.
  •  Causal hypothesese
Propose a cause and effect interaction between two or more variables.
The independent variable is manipulated to cause effect on the
dependent variable.   

The dependent variable is measured to examine the effect created by
the independent variable.

A format for stating causal hypotheses is:
The subjects in the experimental group who are exposed to the independent variable demonstrate greater change, as measured by the dependent variable, than do the subjects in the control group who are not exposed to the independent variable.
  •  Null hypotheses
These are used when the researcher believes there is no relationship between two variables or when there is inadequate theoretical or empirical information to state a research hypothesis  
Null hypotheses can be:
  •   simple or complex;
  •   associative or causal.

  •  Testable hypotheses
Contain variables that are measurable or able to be manipulated.
They need to predict a relationship that can be 'supported' or 'not
supported' based on data collection and analysis.  


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