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There are three common types of variables in social research:

Step one purpose statement

Independent,dependent, and control


Independent variable (IV)

Independent variables are those that are manipulated or selected by the researcher to cause, influence, or otherwise effect the outcome.

Example, to identify independent variable(s) ask, "what is the name of the theory or technique the researcher is using to cause change?"

Known as the experimental group


Dependent variable (DV)

Dependent variable are those that are affected or changed as a result of the manipulation of the independent variable.

For example, to determine the depended variable, ask "what is the researcher attempting to measure or test"?



Control variables are possible confounding variables that the researcher attempts to hold constant so that their effects are cancelled out or controlled for, such as demographics or background characteristics of the subjects of a study.
For example, to determine the control variable (s) ask, "what is the demographic or background information identified in the purpose statement"?


Null hypothesis
Step 2 hypothesis

Null hypotheses
Pure experimental research is based on a null hypothesis

Statement that there is no relationship existing between the independent variable and dependent variables.

Type 1 alpha error
When a researcher rejects a null hypothesis that is true, it is a type one alpha error.

Type 2 beta error
When a researcher accepts null hypothesis when is should have been rejected, this type is a type two beta error occurs.


Hypothesis testing is mostly closely related to the work of?

R.A Fisher


Are four types of sampling techniques?

Simple random sample, stratified random sample, cluster sample, and systematic random sample


Simple random sample

Each item or subject in a sample is considered to have an equal, independent chance of being selected into the sample.

For example, a school district has 10,000 students. Researchers want to do a project with 1000 students. Every student has an equal, in the independent chance of being selected.


Stratified random sample

Items or subjects are divided into PARTS, such as, Grades, ages, income, etc.

For example, 10,000 children are divided by grade each child has an equal independent chance of being selected into the sample


Cluster sample

In a CLUSTER SAMPLE PARTS that go together are, researched/studied together, such as, neighborhood, classes, etc.

Example: researchers picks Fifth grade. There's two schools with fifth-graders. One on the north side and one on the southside. Put them together would be a cluster.


Systematic random sample

Systematic rules of selections or PREDICTABLE interval is employed.

For example, every third person,odd numbers, etc.


Procedures: that threaten the internal validity concerns flaws and the design of the study:

History, maturation, testing, statistical regression, and subject attrition.


Threats to external validity concerns the extent to which the researcher can generalize findings to a larger population.

Multiple treatment interference, Hawthorne (placebo) effect, novelty effect, experimental or Rosenthal effect, halo effect


Hawthorne (placebo) effect

Occurs when the subjects knowledge that they are participants in a study alters or otherwise influences their usual responses


Rosenthal effect

Occurs when the researchers behavior or appearances affects the subject performance


Halo effect

Occurs when the researcher allows his or her initial impression of the subject to influence later ratings of the subject


Types of measurement scales

Nominal/ categorical

Ordinal/ order

Interval/ equal



Nominal / categorical

Is the lowest and least precise level of measurement as it simply classifies or sort persons and/or objects into the categories.

example: yes or no, male or female, married or never Married


Ordinal (order)

Not only classifies subject or their behavior, but also ranks them in terms of the degree to which they possess a characteristic of interest.

Intervals between ranks are not equal.

For example, BA/MA/PhD, 1st place, Second Place, third place


Interval (equals)

Has all the characteristics of both a nominal ordinal scales, but, in addition, it is based upon predetermined equal intervals.

Most of the teachers use an educational research, such as achievement us, aptitude test, and intelligent tiff, represents interval scale.

Interval scale do not have a true zero. Which means they can have negative numbers.

For example: one, two, three. 20°, 40°, 60°. -20°, -40°, -60°.



If the highest, most precise, level of measurement. A ratio scale has all of the advantages of the other types of scales and, in addition, it has a true zero point meaning no negative numbers.

For example, 5 pounds, 10 pounds, 15 pounds,. 10 inches, 12 inches, 14 inches. $.25, 50 Cent, .75 cent, one dollar


Types of derived scores:

(GE) Grade equivalent: denotes that average raw scores or are assigned a grade-level value. We interpret a given score compared to other x graders.
For example, in the third grade Mary scored a 4.5 on a reading test, the appropriate way to interpret this score is to say Mary scored above average in reading.

Percentile ranks: indicates the percentage of score that fall at or below a given score.
For example, John scored a 59 on a reading test and that score landed him at the PR of 45 (basedss on a graphing of the scores), The meaning is that 45% of the students who took the test earned scores of 59 or Less.

Standard scores:
Z scores- is the most basic standard score and allows scores from different is to be compared. Has a mean of zero and a standard deviation of one.
T scores- is widely used and has a mean of 50 and a standard deviation of 10.
Stanines- divide the normal curve and nine points, not nine equal parts.


Measures of central tendency

Mean – is the most frequently used. Interval scale. Meaning the average.

Median- mid point. Most appropriate measure of central tendency for ordinal level data.

Mode- most frequent.


Measure of variability

Range, standard deviation, and variance


Two variables is expressed as a correlation coefficient:

The closer to one either a positive one or a negative one that's stronger correlation.

Positively ( as x increases, y also increases) correlation coefficient Will be close to positive one, a perfect positive relationship.

Negatively ( as x increases, y decreases) The correlation call fission will be close to -1, a perfect negative


These are the two most widely used correlation:

Pearson product moment correlation (Pearson r)
- which is used for interval or ratio measures.

Spearman rho- which use for ordinal data


Skewed distribution

Value of the mean, the median, and the mode are different.

Negative skewed: the meaning is pulled in the direction of low scores me the tail to the left.
-the mean would be smaller then the median.

Positive skewed: the mean is pulled in the direction of the highest score. Meaning the table to the right.
- The mean is larger than the median


T test

Is used to determine whether there is a statistical significance between the means of two groups.

It is used with: interval and ratio


Analysis of variance

Analysis of variance

Is like multiple t-tests and is used with three or more groups.

The ANOVA provides F values and the F tests will tell you significant differences are present.


Multiple or multivariate analysis of variance

Show the correlation between each IV andDV because multiple IV are used.


Analysis of covariance

Shows how a covariate interacts (co-varies)with the DV.

A covariate is a variable correlated with the dependent variable.


Occam's Razor

Interpret the results in the simplest manner.


Which level of significance would best rule out chance factors?



Single blind study versus double blind study

Single-blind study the subject would not know whether he or she is a member of the control group or the experimental group.

A double-blind study goes one step beyond the single blind Version by making certain that the experimenter is also aware of the subjects status. In fact, in the double blind situation. The person assigned to rate or judge the subjects are often unaware of the hypothesis


X axis
Y axis

X axis is also known as the abscissa. Aka horizontal line

Y axis is also known as the ordinate. Aka vertical line.


Normal distribution

In a normal distribution approximately 68% of the population will fall between +1 and -1 SD.


Confounded/flawed/ contaminating variable

Is when an undesirable variables are not kept out of the experiment.

Researcher doesn't know something about the participant and this can effect the research.


Chi2 (the most common nonparametric test) or Kurskal-Wallace (similar to ANOVA)

Is use with nominal data and compares observed frequencies with expected frequencies. May be used when a study has only one group subjects.