Gradual improvement is never a headline By Prof Dr Sohail Ansari& Variable and hypothesis

The trust of the innocent is the liar's most useful tool. Stephen King It is great treachery that you should tell your brother something and have him believe you when you are lying. Hadith

Gradual happening is no happening

·         If News keeps pace with things as they unfold; gradual improvements are to be headlines.

Headlines, in a way, are what mislead you because bad news is a headline, and gradual improvement is not. Bill Gates

Distinguish between variable and hypothesis

Variables are measurable characteristics or properties of people or things that can take on different values. In contrast, characteristics that do not vary are constants. A hypothesis states a presumed relationship between two variables in a way that can be tested with empirical data.

VARIABLES AND HYPOTHESES

Begin with stating the research question, the purpose of the research, the resources needed, and a plan for the research, including a model of the phenomenon under study.

Where do research ideas come from?  Curiosity; experience; need for deciding or acting; job; school; building on or contesting existing theory; available funding; etc.
 

A preliminary research proposal, in one or two pages,

 a.  states the research question

 b.  states the purpose of the research

 c.  sketched the initial model

 d.  discusses (explains) the initial model

 e.  identifies pertinent background literature (bibliography)

A model shows how different elements are linked by relationships.  The elements for a model can be drawn from personal experience, consulting with key players, published literature, asking experts, existing data sets, and pilot studies.  Generally a model is fixed at the beginning of the research; it may be altered as a result of the data analysis.

A model is a visual representation of how something works; it both describes and explains some phenomenon.  The advantages and drawbacks of models are:     Advantages	Disadvantages
Helps to understand the research project	May over-simplify the problem
Explains the idea to others	May not meet the client's needs
Guides the research process	May not be well-suited to application

 
Elements of the model are variables.  Variables are measurable characteristics or properties of people or things that can take on different values.  In contrast, characteristics that do not vary are constants.

A hypothesis states a presumed relationship between two variables in a way that can be tested with empirical data.  It may take the form of a cause-effect statement, or an "if x,...then y" statement.

The cause is called the independent variable; and the effect is called the dependent variable.

Relationships can be of several forms:  linear, or non-linear.  Linear relationships can be either direct (positive) or inverse (negative).

In a direct or positive relationship, the values of both variables increase together or decrease together.  That is, if one increases in value, so does the other; if one decreases in value, so does the other.

(The example we gave of the relationship between height and weight is a direct or positive relationship. In a negative or indirect relationship, the two variables move in opposite directions, that is, as one increases, the other decreases.)

Negative Correlation in Psychology: Examples, Definition & Interpretation

Yolanda has taught college Psychology and Ethics, and has a doctorate of philosophy in counselor education and supervision.

Explore the relationship between positive and negative correlations. Learn about the characteristics of a negative correlation, how to determine the strength of a correlation, and more.

What Is Negative Correlation?

Imagine that you are conducting research on school performance. You want to know if a relationship exists between high school students' performance in school and video games. You collect the grade point average (GPA) and the weekly hours spent playing video games from 40 students. The table shown here summarizes your findings.

If you look at the data closely, you will begin to notice that as the number of hours spent playing video games increases, GPA decreases. In other words, there is a negative correlation between the school performance of high school students and playing video games.

Before we discuss negative correlation, we must first define correlation. A correlation is a single numerical value that describes a relationship between two things, or variables. The Pearson product moment correlation is the most common measure of correlation and is usually represented by the letter r. A correlation has two qualities: direction and strength.

The two directions of a correlation are positive and negative. In a positive correlation, both variables move in the same direction. In other words, as one variable increases, so does the other. For example, there is a positive correlation between smoking and alcohol use. As alcohol use increases, so does smoking.

When two variables have a negative correlation, they have an inverse relationship. This means that as one variable increases, the other decreases, and vice versa. Negative correlations are indicated by a minus (-) sign in front of the correlation value. In the example above, we noted that students who spent the higher amount of time playing video games each week had the lowest GPAs. As the hours spent playing video games decreased, the GPAs increased.

Some other examples of variables that are negatively correlated are:

• The weight of a car and miles per gallon: cars that are heavier tend to get less miles per gallon of gas.
• School achievement and days absent from school: people who miss more days of school tend to have lower GPAs.
• Vaccinations and illness: The more that people are vaccinated for a specific illness, the less that illness occurs.

Determining Correlation Strength

So, how do we determine the strength of a relationship? We look at the numbers. A correlation of 0 means there is no relationship between the two variables. A correlation of -1 means that there is a perfect negative relationship between the variables. Similarly, a correlation of 1 indicates that there is a perfect positive relationship. Perfect relationships rarely exist in real-life. If you find two things that are negatively correlated, the correlation will almost always be somewhere between 0 and -1.

Common Examples of Negative Correlation

• A student who has many absences has a decrease in grades.
• As weather gets colder, air conditioning costs decrease.
• If a train increases speed, the length of time to get to the final point decreases.
• If a chicken increases in age, the amount of eggs it produces decreases.
• If the sun shines more, a house with solar panels requires less use of other electricity.
• If it is darker outside, more light is needed inside. 
• If a car decreases speed, travel time to a destination increases.
• If a car tire has more air, the car may use less gas per mile. 
• The warmer it is outside, the fewer layers of clothing one has to wear to be warm. 
• As one exercises more, his body weight becomes less.
• The older a man gets, the less hair that he has. 
• The more one works, the less free time one has.
• As a tadpole gets older, its tail gets smaller.
• The further one runs, the slower one’s pace may be.
• As the temperature increases, fewer hot chocolate products are sold.
• As more employees are laid off, satisfaction among remaining employees decreases. 
• As the temperature decreases, more heaters are purchased. 
• As a bikers speed increases, his time to get to the finish line decreases. 
• As the slope of a hill increases, the amount of speed a walker reaches may decrease.
• The more one eats, the less hunger one will have. 
• As humidity increases, people’s desire to be outside may decrease. 
• As snowfall totals increase, the amount of people driving decreases. 
• As one increases in age, often one’s agility decreases. 
• If the temperatures outside decrease dramatically, heating bills will increase.
• If a resident uses more mouse traps in the home, the amount of mice in the home will likely decrease.
• The more alcohol one consumes, the less judgment one has. 
• The more a window is obstructed by curtains, the less light that will enter the house. 
• The more one cleans the house, the less likely there are to be pest problems. 
• The more one works out at the gym, the less body fat one may have. 
• The more one smokes cigarettes, the fewer years she will have to live. 
• The more one runs, the less likely one is to have cardiovascular problems. 
• The more vitamins one takes, the less likely one is to have a deficiency. 
• The more iron an anemic person consumes, the less tired one may be. 

Not every change gives a positive result. These different examples of negative correlation show how many things in the real world react inversely. 

In an inverse or negative relationship, the values of the variables change in opposite directions.  That is, if the independent variable increases in value, the dependent variable decreases; if the independent variable decreases in value, the dependent variable increases.

An inverse correlation, also known as negative correlation, is a contrary relationship between two variables such that they move in opposite directions. For example, with variables A and B, as A increases, B decreases and as A decreases, B increases.

In a non-linear relationship, there is no easy way to describe how the values of the dependent variable are affected by changes in the values of the independent variable.

If there is no discernable relationship between two variables, they are said to be unrelated, or to have a null relationship.  Changes in the values of the variables are due to random events, not the influence of one upon the other.
 

To establish a causal relationship between two variables, you must establish that four conditions exist:

1)  time order:  the cause must exist before the effect;

2)  co-variation:  a change in the cause produces a change in the effect;

3)  rationale:  there must be a reasonable explanation of why they are related;

4)  non-spuriousness:  no other (rival) cause for the effect can be found.

To establish that your causal (independent) variable is the sole cause of the observed effect in the dependent variable, you must introduce rival or control variables.  If the introduction of the control variable does not change the original relationship between the cause and effect variables, then the claim of non-spuriousness is strengthened.

Commonly used control variables for research on people include sex, age, race, education, and income.  Commonly used control variables for research on organizations include agency size (number of employees), stability, mission, budget, and region of the country where located.

For example, consider the placement rates for three training programs.  The independent variable is the type of training, and the dependent variable is the placement rate.

Vocational education has a placement rate of 30%; on-the-job training has a rate of 40%; and work-skill training has a rate of 35%.  It would appear that on-the-job training is the best program, followed by work-skill training, with vocational education last.

However, when education is introduced as a control variable, it can be seen that the effect of the independent variable (type of training) on the dependent variable (placement rate) is quite different for people with different levels of education.    Level of Education	Vocational Ed	On-the-job training	Work-Skill Training
Less than high school	30%	20%	50%
High School	60%	45%	15%
More than high school	20%	60%	10%
Overall rate	30%	40%	35%

 
 (Note that there are different numbers of people in each educational category, and different numbers of people in each training program, so the overall rate is not simply the average of the rates for each educational category within each training program).