Research chapter 12

in assessments of internal consistency, a reliability coefficient, Cronbach's alpha.

Alpha:

in tests of statistical significance, the significance criterion-the risk the researcher is willing to accept of making a Type 1 Error

Alpha:

a statistical procedure used to test mean differences among groups on an outcome variable, while controlling for one or more covariates [là confounding variables].

Analysis of covariance (ANCOVA)[checked]:

a statistical procedure for testing mean differences among three or more groups or when there is more than one IV, by comparing variability between groups to variability within groups, yielding an F-ratio statistic.

Analysis of variance (ANOVA)[checked]

in statistical testing, the probability of a Type 2 error.

Beta:

a statistical index of the "typicalness" of a set of scores, derived from the center of the score distribution; indices of central tendency include the mode, media, and mean.

Central tendency

a statistical test used in various contexts, most often to assess differences in proportions, symbolized as X2 (X2 squared). Used to test hypothesis about the proportion of cases in different categories, as in a crossabulation.

Chi-squared test [checked]

an index summarizing the degree of relationship between variables, ranging from +1.00 ( a perfect positive relationship) through 0.0 (no relationship) to -1.00 (a perfect negative relationship). It describes intensity and direction of a relationship.

Correlation coefficient[checked]:

statistics used to synthesize, describe, and summarize data (e.g., means, percentages). Know difference between this and inferential statistics.

Descriptive statistics [checked]:

a statistical expression of the magnitude of the relationship between two variables, or the magnitude of the difference between groups on an attribute of interest; also used in meta summaries of qualitative research to characterize the salience of a theme or category

Effect size [checked]:

a systematic array of numeric values ordered from the lowest to the highest, together with a count of the number (or percentage) of times each value was obtained.

Frequency distribution:

through statistical tests enables researchers to make objective decisions about relationships between variables. Uses objective criteria for deciding whether research hypotheses should be accepted as true or rejected as false.

Hypothesis testing[checked]

statistics that permit inferences about whether results observed in a sample are likely to occur in the larger population. Based on laws of probability, provides a means for drawing conclusions about a population, given data from a sample

Inferential statistics:

the risk of making a Type 1 error in a statistical analysis, established by the researcher beforehand (e.g., the .05 level, the conventional standard. It means that only 5 out of 100 samples would the null hypothesis be rejected when it should have been accepted). The probability that such an error will occur.

Level of significance[checked]:

a multivariate regression procedure that analyzes relationships between one or more independent variables and a categorical dependent variable and yields an odds ratio.

Logistic regression:

a measure of central tendency, computed by summing all scores and dividing by the number of cases. Most stable measure of central tendency.

Mean

Sự khác nhau giữa SD và mean:

Mean tells the best value for summarizing an entire distribution. SD tells how much, on average, the scores deviate from the mean.

a descriptive statistic that is a measure of central tendency, representing the exact middle value in a score distribution; the value above and below which 50% of the scores lie.

Median

a measure of central tendency; the value that occurs most frequently in a distribution of scores.

Mode

a statistical procedure for understanding the effects of two or more independent (predictor) variables on a dependent variable. A method for understanding the effect of two or more predictor IVs on a continuous DV.

Multiple regression analysis[checked]:

statistical procedures designed to analyze the relationships among three or more variables (e.g., multiple regression, ANCOVA). They are used to untangle complex relationships among three or more variables.

Multivariate statistics [checked]:

states that no relationships exists between variables; rejection of the null hypothesis lends support to the research hypothesis. In testing hypotheses, researchers compute a test statistic and then determine whether the statistic falls beyond a critical region on the relevant theoretical distribution. The value of the test statistic indicates whether the null hypothesis is "improbable".

Null hypothesis[có, chưa check]:

The squared multiple correlation coefficient, indicating the proportion of variance in the dependent variable explained by a group of independent variables.

R2:

a measure of variability (how spread out the data are), computed by subtracting the lowest value from the highest value in a distribution of scores.

Range

an analysis of variance used when there are multiple measurements of the dependent variable over time.

Repeated measures ANOVA [checked]:

a measure of variability, the most frequently used statistic for measuring the degree of variability in a set of scores. How much on average the scores deviate from the mean. It summarizes the average amount of deviation of values from the mean.

Standard deviation:

a term indicating that the results from an analysis of sample data are unlikely to have been caused by chance, at a specified level of probability (p value). .05 is statistically significant.

Statistically significance[checked]:

a distribution of values with two halves that are mirror images of each other. A normal distribution (bell-shaped curve) is symmetrical, unimodal, and not too peaked

Symmetric distribution:

an error created by rejecting the null hypothesis when it is true (i.e., the researcher concludes that a relationship exists when in fact it does not-a false positive).

Type 1 error[checked]:

an error created by accepting the null hypothesis when it is false (i.e., the researcher concludes that no relationship exists when in fact it does-a false negative).

Type 2 error[checked]

the degree to which values on a set of scores are dispersed.

Variability

a widely used effect size index for comparing two groups means, computed by subtracting one mean from the other and dividing by the pooled standard deviation;

d statistic, also called Cohen's d.

in statistical testing, the probability that the obtained results are due to change alone: the probability of a Type 1 error.

p value

When the longer tail point to the right: distribution has ------- skew, to the left: ------ skew

positive skew left: negative skew

a parametric statistical test for analyzing the difference between two group means.

t-test: