PSYCHSTATQUALIFYING Flashcards
- a branch of mathematics that involves data collection, analysis. and presentation
- a set of mathematical procedures for organizing, summarizing, and interpreting information
How do we use ______ in the field of psychology?
1) Describe
2) Predict
3) Explain
4) Control
Statistics
- the entire set of the individuals of interest for a particular research question
Population
- is a value, usually a numerical value, that describes a population
- is usually derived from measurements of the individuals in the population
Parameter
- a set of individuals selected from a population, usually intended to represent the population in a research study
Sample
- is a value, usually a numerical value, that describes a sample
- is usually derived from measurement of the individuals in the sample
Statistic
- a characteristic or condition that changes or has different values for different individuals
Variable
TYPES OF VARIABLE
- consists of separate, indivisible categories
- no values can exist between two neighboring categories
Discrete
TYPES OF VARIABLE
- an infinite number of possible values that fall between any two observed values.
- a ____ is divisible into an infinite number of fractional parts
- temperature, weight, height, etc.
Continuous
- one that takes one of only two possible values when observed or measured
Artificial Dichotomous - derived from scores
True Dichotomous - naturally occuring
Dichotomous variable
- possible number or category that a score can have
Values
- a single measurement or observation
- the particular value
Score
- are measurements or observations
Data
are collection of measurements or observations
Data set -
- statistical procedures used to summarize, organize, and simplfy data
Descriptive Statistics
- the naturally occuring discrepancy, or error, that exists between a sample statistic and the corresponding population parameter.
Sampling Error
- categories used to measure a variable
- classification that describes the nature of information within the values assigned to variables
Scales of measurements
FOUR LEVELS OF MEASUREMENTS
- having to do with “names”
- categorical variable
- a set of categories that have different names
- not quantitative values, they are occasionally represented by numbers
Nominal
FOUR LEVELS OF MEASUREMENTS
- rank-order variable
- a set of categories that are organized in an ordered sequence
- clothing size, class rank, level of stress (low, average, high), likert scale
Ordinal
FOUR LEVELS OF MEASUREMENTS
- ordered categories that are all intervals of exactly the same size
- equal differences between numbers on scale reflect equal differences in magnitude
- no absolute zero
- temperature, IQ, stress, motivation
Interval
FOUR LEVELS OF MEASUREMENTS
- highest form of measurement
- an interval scale with the additional feature of an absolute zero point
- number of correct answers, weigh gain
Ratio
- an organized tabulation of the number of individuals located in each category on the scale of measurement
Frequency Distribution
- the percentage of individuals in the distribution with scores at or below the particular value
- exact position within the distribution
Percentile Rank
- represent the accumulation of individuals as you move up the scale
Cumulative frequency
- a statistical measure to determine a single score that defines the center of a distribution
- the goal of_____ is to find the single score that is most typical or most representative of the entire group
Measure of central tendency
- a distribution is the sum of the scores divided by the number of scores (average)
Mean
- a mean where some values contribute more than others.
- when calculating a theoretically expected outcome where each outcome has a different probability of occuring
Weighted mean
- midpoint of the list
- the scores in a distribution are listed in order from smalles to largest
-extreme scores or skewed distributions
-undetermined values
-open-ended distributions
-ordinal scale
Median
- the score or category that has the greates frequency
- nominal scales
-discrete variables
Mode
- a quantitative measure of the difference between scores in a distribution
- describes the degree to which the scores are spread out or clustered together
- how well an individual score represents the entire distribution
- inferential statistics in which relatively small samples are used to answer questions about populations
Measure of variability
- the distance covered by the scores in a distribution from the smallest score to the largest score
Range
- most commonly used and the most important measure of variability
- a measure of the standard or average distance from the mean, and describes whether the scores are clustered closely around the mean or are widely scattered
- low in this (spread) means low volatility while a high of this (spread) means higher volatility
Standard Deviation
- a form of regression analysis to determine the variave from data points from the mean
- if there is a low of____ it means there’s low variation. A higher of this means higher variance
Sum of the squared deviation
- a standard score to identfy and describe the exact location of each score in a distribution
- always consists of two parts (+or-) signifies whether the score is above or below the mean
- its numerical value specifies the distance from the mean by counting the number of standard deviations between X and μ
Z- scores
- as a fraction or a proportion of all the possible outcomes
- predict the type of samples that are likely to be obtained from the population
Probability
- a statistical method that uses sample data to evaluate a hypothesis about a population
Hypothesis testing
- states that in the general population there is not change, no difference, or no relationship
- predicts that the independent variable (treatment) has no effect on the dependent variable (scores) for the population.
Null hypothesis
- states that there is a change, a difference, or a relationship for the general population
-predicts that the independent variable (treatment) does have an effect on the dependent variable
Alternative Hypothesis
-determines whether there is a statistical significance
or differnece in one direction
- a test is ____ when it predicts a change in results in one direction
-if you have a clear and specific expectation about the direction of the effect.
One-tailed test (directional)
-determines if there is statistical significance or difference between two groups in either direction
- a test is ___ if it does not predict a chance in results in either direction
- if you have not specific expectation or if both directions are equally plausible
Two-tailed test (Non-directional)
- a probability value that is used to define the concept of “very unlikely” in a hypothesis test
- is a small probability that is used to identify the low-probability samples
Alpha level
- actual area under the standard normal distribution curve representing the probability of a particular sample statistic or a more extreme sample statistic occuring if the null hypothesis is true
P value
- used to test hypotheses about an unknown population mean when the value of the variance is unknown
T-statistics
- assumes that the source population is normally distributed
- interval or ratio and tat the population from which the samples are drawn is normal
Situations must meet these assumptions
1) Population mean is known
2) but the population standard devision is unknown
3) Sample size is less than 30
Parametric tests
WHAT TYPE OF T-TEST
- no. of variables:1
- type of variable: continuous
- purpose: decide if the population mean is equal to a specific value or not
One sample t-test
WHAT TYPE OF T-TEST
-no. of variables: 2
type of variable: continuous, categorical/nominal to define group
-purpose: decide if the population means for two different grops are equal or not.
Independent samples t-test
WHAT TYPE OF T-TEST
-no. of variables: 2
- type of variable : continuous, nominal/categorical, pairing within group
- purpose: decide if the difference between paired measurements for a population is zero or not.
Paired samples t-test
- a hypothesis-testing procedure that is used to evaluate mean differences between two or more treatments (or populations)
Analysis of Variance
- the variable (independent or quasi-independent) that designates the groups being compared
Factor
the individual conditions or values that make up a factor
Level of factors -
- a study that uses a seperate group of participants for each treatment condition
- teaching methods affect academic performance
- three levels: lecture-based, hands-on activities, problem-solving exercises.
Single factor design
- a study that combines two different factors and to mix different design within one study
- teaching methods and student motivation levels affect academic performance
Two factor design or a factorial design
- are additional hypothesis test that are done after an ANOVA to determine exactly which mean differences are significant and which are not
Tukey’s HSD test - equal variances assumed is accepted
Games Howell - equal variances not assumed is accepted
Post hoc tests (post tests)
- a statistical technique that is used to measure and describe the relationship between two variables
Correlations
- the two variables tend to chance in the same direction
- as the value of the X variable increases from one individual to another
Positive correlation
- the two variables tend to go in opposite directions
- as the X variable increases, the Y variable decreases
Negative correlation
1.00 indicates a _____ relationship
Perfect correlation
- measures the degree and the direction of the linear relationship between two variables
- most used correlation
Pearson correlation
- consist of techniques that allow us to study samples and then make generalizations about the populations from which they were selected
- interpretation of results
Inferential Statistics
