STATISTICS

• STATISTICS

  Refer to methods for organizing, summarizing, and interpreting data.
• POPULATION

  the entire set of the individuals of interest for a particular research question.
• SAMPLE

  A set of individuals selected from a population.
• INFERENTIAL STATISTICS

  Consist of techniques that allow us to study samples and then make generalizations about the populations from which they were selected.
• INFERENTIAL STATISTIC

  Interpret experimental data.
• CONSTRUCTS

  They are intangible and cannot be directly observed, they are often called hypothetical constructs.

  Ex. intelligence, anxiety, and hunger
• OPERATIONAL

  First, it describes a set of operations for measuring a construct. Second, it defines the construct in terms of the resulting measurements.

  Ex. height, weight, and eye color
• DISCRETE VARIABLE

  Consists of separate, indivisible categories. No values can exist between two neighboring categories. Commonly restricted to whole, countable numbers.
• CONTINUOUS VARIABLE

  Are an infinite number of possible values that fall between any two observed values. A continuous variable is divisible into an infinite number of fractional parts.
• REAL LIMITS

  The boundaries of intervals for scores that are represented on a continuous number line. The real limit separating two adjacent scores is located exactly halfway between the scores.
• CORRELATIONAL METHOD

  The goal is to describe the type and magnitude of the relationship
• LIMITATIONS OF CORRELATIONAL METHOD

  Does not provide an explanation for the relationship.

  Most importantly, does not demonstrate a cause-and-effect relationship between the two variables.
• The goal of an experimental method

  To demonstrate a cause-and-effect relationship between two variables
• Manipulation

  The level of one variable is determined by the experimenter
• Control

  Rules out influence of other variables (so-called extraneous variables)
• Independent variable

  the variable that is manipulated by the researcher
• Dependent variable

  the one that is observed to assess the effect of treatment
• The Result

  From the sample are generalized to the population
• VARIABLES

  Characteristic or condition that changes or has different values for different individuals.
• DATA (PLURAL)

  Are Measurements or observations of a variable
• DATA SET

  A collection of measurements or observations.
• DATUM

  A single measurement or observation. Commonly called a score or raw score.
• PARAMETER

  A value, usually a numerical value, that describes a population.
• STATISTIC

  A value, usually a numerical value, that describes a sample.
• DESCRIPTIVE STATISTIC

  A statistical procedure concerned with describing the characteristics and properties of a group of persons, places, or things.
• SCALE OF MEASUREMENT

  That data collection requires that we make measurements of our observations. Measurement involves assigning individuals or events to categories.
• Nominal Scale

  Consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations.

  Ex. Gender, Blood type, Marital status, Personality type, Occupation.
• Ordinal Scale

  Organized in a fixed order corresponding to differences of magnitude. An ordinal scale consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude.

  Ex. Rank order, Likert Scale Responses, Educational Attainment, Stage Levels, Severity Conditions, Socioeconomic Status.
• Interval Scale

  A value of zero does not indicate a total absence of the variable being measured. For example, a temperature of 0 degrees Fahrenheit does not mean that there is no temperature, and it does not prohibit the temperature from going even lower.

  Ex. Temperature, Time, IQ Scores, Attitude Measures.
• Ratio Scale

  A blank is anchored by a zero point that is not arbitrary but rather is a meaningful value representing none (a complete absence) of the variable being measured.

  Ex. Height, Weight, Money, Volume, Speed.
• DESCRIPTIVE METHOD

  One (or more) variables measured per individual
• NON EXPERIMENTAL METHOD

  Nonequivalent groups

  – Researcher compares groups of scores

  – Researcher cannot control who goes into which group

  • Pretest/posttest

  – Individuals measured at two points in time

  – Researcher cannot control the influence of the passage of time
• quasi-independent

  Independent variable is
• SUMMATION NOTATION

  Many statistical procedures involve summing (adding up) a set of scores.
• FREQUENCY DISTRIBUTION

  An organized tabulation. Showing the number of individuals located in each category on the scale of measurement.
• Symmetrical Distribution

  It is possible to draw a vertical line through the middle so that one side of the distribution is a mirror image of the other.
• Skewed Distribution

  Distribution, the scores tend to pile up toward one end of the scale and taper off gradually at the other end.
• Mean

  The blank for a distribution is the sum of all the scores divided by the number of scores.
• Median

  It is the midpoint of the scores in a distribution when they are listed in order from smallest to largest
• Mode

  This is the score or category that has the greatest frequency of any score in a frequency distribution.