# Relationship between hypothesis and variables

### Guide 2: Variables and Hypotheses

Get an answer for 'Please explain the relationship between a hypothesis and an experiment; how do independent and dependent variables differ?' and find. The relationship between variables determines how the right conclusions are reached. "Statistical Hypothesis Testing" · Back to Overview · "Statistical Tests". A hypothesis is a tentative statement about the relationship between two or more variables. Learn more about the elements of a good.

For example, suppose we wanted to study the income levels of single adults. If one researcher classified his single adult subjects into these three categories 17 through 22, 23 through 27, and 28 through 33he would get different results than this second researcher who used three different categories 20 through 40, 41 through 60, and 61 and over.

The first researcher is interested in young adults and the second in all ages. Thus, without operational definitions we could think that they both were studying the same variable. When we use behavioral operational definitions for variables, we define exactly what we are studying and enable others to understand our work.

This is called operationalism. Formulating Hypotheses Once the research question has been stated, the next step is to define testable hypotheses.

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Usually a research question is a broad statement, that is not directly measurable by a research study. The research question needs to be broken down into smaller units, called hypotheses, that can be studied. A hypothesis is a statement that expresses the probable relationship between variables. There are two types of hypotheses: Descriptive hypotheses ask a specific question regarding some phenomenon.

For example, we might want to study this research question: A descriptive hypotheses that would test a part of the above research question is: Descriptive hypotheses are always phrased in the form of a question regarding some aspect of the research question. Usually a descriptive hypothesis does not include an active independent variable.

When we use an independent variable, a directional hypothesis is usually needed. The second type of hypothesis is a directional hypothesis. Directional hypotheses are never phrased as a question, but always as a statement.

## Relationship Between Variables

Directional hypotheses always express the effect of an independent on a dependent variable. For example, hypotheses drawn from the anxiety and dying research question above would be: A client who is at the "acceptance" stage will exhibit less anxiety, as measured by GSR recordings, when discussing their pending death than clients in the other stages. IV - stage of client attribute DV - anxiety level 2. Client anxiety levels, as measured by GSR recordings, will be lower at the end of any state denial, anger, bargaining, depression, acceptance than at the beginning.

IV - client at beginning, middle, or end of stage attribute DV - anxiety level 3.

Some clients will reach their lowest anxiety levels, as measured by GSR recordings, after progressing through only one stage. Others will require two or more stages before achieving their lowest anxiety levels. IV - number of stages client has gone through since learning of their condition attribute DV - anxiety level 4. A planned program of counseling interventions will enable clients to achieve low anxiety levels more rapidly than clients who receive normal nursing and medical care.

IV - presence or absence of counseling active DV - time taken to reach a low anxiety level 5. Clients who achieve and maintain low levels of anxiety when discussing their pending death will be more responsive to pain medication.

### Hypotheses & Variables: POSSpring 0W58

IV - anxiety level attribute DV - effectiveness of pain medication Hypotheses also are as specific as possible and deal in behaviors rather than attitudes or general feelings. Any time a non-behavioral term, such as anxiety or happiness, is used, the researcher needs to define that term as behaviors that can be measured or observed. Also, the hypothesis needs to include the conditions under which the behaviors will be seen.

In the above hypotheses we planned to study anxiety levels, as measured by GSR recordings under the conditions of the client discussing his pending death. In an inverse or negative relationship, the values of the variables change in opposite directions.

### Variables and Hypotheses

That is, if the independent variable increases in value, the dependent variable decreases; if the independent variable decreases in value, the dependent variable 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: 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. Researchers should try to gather data as completely as possible for example, get education in number of years rather than degree level because one can collapse or move around categories later on with computer programs.

Avoid "open-ended" categories that do not have fixed end points when possible e. Make questions and responses explicit enough that respondents or interviewers do not need to guess about the answer.

Nominal, ordinal and interval-ratio variables are different types of category systems. These form a cumulative and hierarchical set of data properties, so that nominal properties are true for ordinal and interval data.

And ordinal properties are also true for interval data. The reverse does NOT hold.

Thus, you can sort all cases into mutually exclusive, exhaustive categories. Examples of nominal variables include: Zodiac sign Birth country and Religious affiliation or denomination Nominal variables are also sometimes called categorical variables or qualitative variables.

The categories are not only not numbers, they do not have any inherent order. South Koreans or Turks? Country of origin is NOT a number or even a "relative judgment".

If you suspect that ranking the categories NOTE: NOT the cases within the categories would start a war, you probably have nominal variables. You can only do very basic statistics or presentations with nominal data, such as: Of course, many nominal variables are very important, especially as explanatory variables. This means the scores must be rank-ordered from highest to lowest or vice versa first, before any ordinal statistics can be used.