VID OF SIR JHUNAR Flashcards by Adel Camante

A value (numerical value) that describes a population

Parameter

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Set of all individuals of interest in a particular study

Population

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Value (numerical) that describes a sample

Statistic

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Every population paramater has a sample statistic

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A characteristics or condition that has changes or has different values for different individuals

It varied in different individuals
Ex: Anxiety, stress level

Variable

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The act of assigning numbers or symbols to characteristics of things (people, events, whatever) according to rules

Measurement

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A particular persons value on a variable also called “ OBSERVED SCORE”

Straightforward, unmodified accounting of perfromance that is usually numerical

Raw score

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A set of numbers (or symbols) whose properties model empirical properties of the objects which numbers are assigned (another term is test)

Can be continous and discrete
Para ma quantify ang objects

Scale

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Scale that is indivisible (can’t be define and no in between values)
Ex: Male or female
- Can be acquired by counting

Discrete scale

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A classification that describes the nature of information within the values assigned to variables.

Scale of measurement

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Scale involved in classification or categorization based on one or more distinguishing characteristics, where all things measured must be placed into mutual exclusive and exhaustive categories

Jersey shirt

Nominal scale

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Rank Ordered classification ( Clothing sides, Class grades A-F, level of self - confidence (low, average, high) Likert scale
- Level of self confidence
- Agree, strongly agree , disagree

Ordinal

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Variable that contains equal-interval between numbers and contain no absolute zero point
( Temperature, Farenheit and celsius, IQ, Stress)

Interval

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An interval scale with the additional feature of an absolute zero point

Time to complete a task
Number of correct answers
weight gain in past 6 months

Ratio

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Most psychological scales in psychology is ___ however most psychologist treat those data as ___ because they are much flexible for statistical manipulation/analysis

Ordinal
Interval

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Procedures for summarizing a group of scores or otherwise making them more understandable

Descriptive statistics

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Table showing test scores and how often they occur

Frequency distribution

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Scores grouped into intervals (class intervals)

Grouped frequency distribution

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Line graph that connects data points; closed at both ends (for interval and ratio data)

Frequency polygon

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Vertical bars for categorical data (nominal/ordinal)

Column chart

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Tracks trends or changes over time

Line graph

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Arithmetic average; best for NORMAL DISTRIBUTION without OUTLIERS

Mean

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Middle value; best for SKEWED DISTRIBUTION with outliers

Median

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Measures variability for ORDINAL DATA with OUTLIERS

Median absolute deviation (MAD)

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Most frequent; useful for CATEGORICAL DATA

Mode

Peak on the left, tail on the right Mean > Median > Mode

Positively skewed

Peak on the right, tail on the left Mean < median < mode

Negatively skewed

Spread between the first and third quartile

Interquartile range (IQR)

Mean of absolute difference from the mean [| x- M| / n

Average deviation

Average squared deviation from the mean q2 = [(x-u)2 / N

Variance

Square root of a variance Q= √q2

A standard score converts a raw score into a common scale based on the mean and standard deviation of a distribution

Standard scores

Measures how many standard deviations a particular score is from the mean Z= x-M /SD

Z-score

The area under the curve of normal curve represents ___ of the data

100%

Z-score range ±1 SD = Between ± 1 SD and ±2 SD ±2 SD Between ±2 SD and ±3 SD ±3 SD

68.26% 13.59% 95.44% 2.15% 99.74%

Allows you to convert one type of score (Z-score) into another type (T-score) using a specific mean and standard deviation NS= z (SD) + M

Linear transformation

A statistical procedure used to determine whether to reject or fail to reject a null hypothesis based on sample data

Null hypothesis significance testing

Assumes no effect or no difference between groups

Null hypothesis

Assumes a significant effect or difference

Alternative hypothesis

Data should follow a normal distribution

Normality of data

Data points should not be influenced by other data points

Independence of observation

Variance across samples should be approximately equal

Equal variance (Homoscedasticity)

A __ is used when the following assumptions are met • ___ data follows an approximately normal distribution • ___ observation are independent of each other • ___ the variance among groups is similar

Parametric test Normality Independence Equal variance

Compares the mean of two related (paired) samples

T-test for dependent samples

Measuring stress levels before and aftern an intervention

T-test for dependent samples

1 group of subjects with 2 scores • IV = categorical (type of treatment) • DV = interval/ratio (stress score)

T-test for dependent samples

Compares the means of two independent groups

T-test for independent samples

Comparing stress levels between males and females

T-test for independent samples

2 independent groups IV- Categorical (gender) DV- Interval/ratio (stress score) Quasi- IV - categorical (male/female)

T-test for independent samples

Compares the means of two or more independent groups

One-way ANOVA (F-ratio)

Testing the effect of three different study methods on test scores IV- Categorical (study method) DV- Interval/ratio (test score) - A researcher wants to test whether different study methods impact students' exam performance. They divide students into three groups, each using a different study technique for a month:

One way ANOVA

Compares the means of a single group measured multiple times

Repeated measures ANOVA

Measuring reaction time at three different time points

Repeated measures ANOVA

1 group with multiple scores over time IV - Time (categorical) DV - Interval/ ratio (reaction time)

Repeated measures ANOVA

Measures the strength and direction of the relationship between two variables

Pearson's r

Examining the relationship between stress levels and sleep hours • 1 group with paired scores • Variables - interval/ratio

Pearson's r

Used when data is not normally distributed or the assumptions for parametric tests are violated

Non-parametric tests

Compares two related (paired) samples • Comparing pretest and postest stress levels (ordinal data)

Wilcoxon signed - rank test

Compares two independent groups • Comparing stress levels between meals and females (ordinal data) • Comparing stress levels across three study groups (ordinal data)

Mann-whitney U test

Compared repeated measures within the same group • Measuring stress levels over three time points (ordinal data)

Friedman test

Measure the strength and direction of the relationship between two variables • Relationship between stress and study hours (ordinal data)

Spearman's RHO

Test if the observed frequency distribution matches the expected distribution * checking if voter preference matches population statistics ## Footnote ex: gustong malaman ng fast food chain kung pantay-pantay ang orders ng tatlong flavors ng burger: beef, chicken, fish. gusto nilang malaman kung pantay pantay ang orders

Chi-square test of Goodness of Fit

Test the relationship between two Categorical variables * relationship between gender and political preference ## Footnote kung ang isang variable ay nakaka apekto sa isa o kung sila ay independent sa isat isa

Chi-square test of independence

Value computed from sample data, used to decide whether to reject the null hypothesis

Test statistic

Probability of rejecting the null hypothesis when its actually true Common choices : 0.05 or 0.01

Significance level

Valuesof the test statistic that lead to rejecting the null hypothesis

Critical region

Probability of observing a result as extreme as the test results, assuming the null hypothesis is true

P-value

If p ≤0.05 : If P > 0.05 :

Reject Fail to reject

A statistical test that measures the strength and direction of the relationship between two paired variables

Correlation

Forms a line from the bottom left to the top right of the scatter plot • height and weight often shows a positive correlation

Positive correlation (direct)

Forms a line from the top left to the bottom right

Negative correlation

Measures the strength and direction of a linear relationship between two variables

Correlation coefficient (r)

-1 = 0 = +1 = "A high correlation means a strong relationship"

Perfect negative correlation No correlation Perfect positive correlation

The result of multiplying a persons z-score on one variable by their z-score on another variable

Cross-product of z-score

CROSS PRODUCT • both z scores are either postive or negative= • One z score is positive while the other is negative= • If the cross product is close to zero =

Positive correlation Negative correlation Not related

Measures the strength of a linear association between two variables • r= [ZxZy/ n-1

Pearson product moment correlation

Measures how much variability in one variable is explained by the other variable • High means stronger predictive relationship • if r²= 0.64, 64% of the variation in test scores can be explained by study hours

Coefficient of determination (r²)

Ranked ordered correlation coefficient: • measures the strength and direction of a relationship between two ordinal variables • Used when the sample size is small (n≤30)

Spearman's RHO

Similar to spearman's RHO but better for tied ranks • measures strength and direction of a relationship between two ordinal variables

Kendall's Tau-b

If the sample is limited to a narrow range, correlation may appear weaker than it is • Ex: If you only test people with very high IQ, you might underestimate the true correlation between IQ and job performance

Restriction range

Correlation does not imply causation

Predicts the value of one variable based on another variable • predicting college GPA based on high school GPA

Linear regression

Predicts the value of one variable based on two or more variable • predicting job performance based on IQ, motivation and experience

Multiple regression

Percentile formula

(CF + 0.5 F (if 2 or more) /N) ×100

VID OF SIR JHUNAR Flashcards

(84 cards)