Term
| If we did repeated sampling and get a mean or percentage out of each sample, what would we create? |
|
Definition
| we would create a sampling distribution of means or percentages |
|
|
Term
| What is the standard deviation of a sampling distribution called? |
|
Definition
|
|
Term
| The sampling distribution taking on a shape of a normal curve is called? |
|
Definition
| • normal sampling distribution |
|
|
Term
| Does the Empirical Rule apply to a normal sampling distribution? |
|
Definition
|
|
Term
| Is the most frequent sample mean or sample percentage probably the population mean or population percentage? |
|
Definition
|
|
Term
| What are the chances that sample mean or a sample percentage is within .99 standard error of the true population mean or true population percentage? |
|
Definition
|
|
Term
| What chance does a sample mean or a sample percentage is within 1.96 standard errors of the true population mean or true population percentage? |
|
Definition
|
|
Term
| What chance does a sample mean or a sample percentage is within 2.58 standard errors of the true population mean or true population percentage? |
|
Definition
|
|
Term
| How many times do social scientists usually sample? |
|
Definition
|
|
Term
| Slap on .99 standard error above the sample mean or sample percentage and we create what? |
|
Definition
| • 68% confidence interval |
|
|
Term
| Slap on 1.96 standard errors above the sample mean or sample percentage and we create what? |
|
Definition
| • 95% confidence interval |
|
|
Term
| Slap on 2.58 standard errors above the sample mean or sample percentage and we create what? |
|
Definition
| • 99% confidence interval |
|
|
Term
| What is a significance test? |
|
Definition
| • A test that produces a probability that the null hypothesis is true. |
|
|
Term
| What is a one sample t-test? |
|
Definition
| • a statistical method that tests whether the null hypothesis of a guessed population mean or population percentage is true |
|
|
Term
| Do we reject the null hypothesis if the sample mean or sample percentage falls outside of the 95% confidence interval of the guessed population mean or population percentage? |
|
Definition
| • Yes, we reject the null hypothesis. |
|
|
Term
| What z is used to produce a 95% confidence interval? |
|
Definition
|
|
Term
| What do statisticians call those two z's of a 95% confidence interval? |
|
Definition
| • Statisticians call -1.96 and 1.96 “critical values.” |
|
|
Term
|
Definition
| • the shape of sampling distribution |
|
|
Term
| What two t’s are used to build a 95% confidence interval? |
|
Definition
|
|
Term
|
Definition
| • The probability that the null hypothesis is true. |
|
|
Term
| In the social sciences, we reject the null hypothesis if the P value is greater than? |
|
Definition
| • we reject the null hypothesis if the P value is greater than .05 |
|
|
Term
|
Definition
| • Alpha refers to the P value researchers set to reject the null hypothesis |
|
|
Term
| When we reject the null hypothesis, researchers say that the result is? |
|
Definition
| • “statistically significant.” |
|
|
Term
| Does value of the t-statistic and P value contradict each other when making the conclusion of rejecting or failing to reject the null hypothesis? |
|
Definition
|
|
Term
| Is there a difference between t and z when we work with sample sizes above 120 people? |
|
Definition
|
|
Term
| When we reject the null hypothesis, what type of finding do we have? |
|
Definition
|
|
Term
| What is the standard null hypothesis regarding the relationship between two variables? |
|
Definition
| • The null hypothesis is that there is no relationship between the two variables in the population |
|
|
Term
| If we find a relationship between two variables, do we reject the null hypothesis? |
|
Definition
|
|
Term
| What is the numerical value when there is no relationship between two variables? |
|
Definition
|
|
Term
| Independent Two-Sample T Test is used to determine what? |
|
Definition
| • The probability that a dichotomous variable is not associated with an interval or ratio variable in the population. |
|
|
Term
| In an independent two-sample t test, is the dichotomous variable the independent variable? |
|
Definition
|
|
Term
| In an independent two-sample t test, is the interval/ratio variable the dependent variable? |
|
Definition
|
|
Term
| What is a dichotomous variable? |
|
Definition
| • variable that has only two categories such as gender |
|
|
Term
| What are the two critical values of the t-statistic to build a 95% confidence interval? |
|
Definition
|
|
Term
| Do we reject the null hypothesis when the value of the t-statistic is above 1.96 and below -1.96? |
|
Definition
|
|
Term
| In ANOVA, can you compare the means between 3 or more groups? |
|
Definition
|
|
Term
| What type of curve does ANOVA uses? |
|
Definition
|
|
Term
| Is F=0 the null hypothesis? |
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
| What is the standard null hypothesis between two variables? |
|
Definition
| • There is no relationship between the two variables in the population |
|
|
Term
| What is the value when there is no relationship between two variables? |
|
Definition
|
|
Term
| If you theoretically did repeated sampling/surveys, what do the statistics such as the mean or percentage stack up to? |
|
Definition
| • A t curve. (It’s basically the normal curve if the number of cases in each repeating sample is 120 or above.) |
|
|
Term
| What are the critical values of the t statistic? |
|
Definition
|
|
Term
| What does the χ2 look at? |
|
Definition
| • The difference of frequencies in the categories of two nominal/ordinal variables. |
|
|
Term
| The frequencies are called? |
|
Definition
| • Those frequencies are called crosstabs |
|
|
Term
| Do we reject the null hypothesis if χ2=0? From Σ((fo - fe)2 / fe) |
|
Definition
| • No, we fail to reject the null hypothesis that there is no relationship between two nominal/ordinal variables |
|
|
Term
| What are at least two reasons that critical chi squared, χ2, values are different from critical t values? |
|
Definition
1) x^2 is squared so you can’t have a negative critical value.
2) The critical x^2 always changes, depending on the degree of freedom |
|
|
Term
| What can be the levels of measurement for the variables for cross tabs? |
|
Definition
| • Nominal and Ordinal Dichotomous variables |
|
|
Term
| In cross tabs, are we asking whether there is a relationship between two nominal or ordinal variables? |
|
Definition
|
|
Term
| Are means and standard deviations meaningless for cross tabs tests? |
|
Definition
|
|
Term
| What does a chi-squared, χ2, tell us about the numbers in a sample? |
|
Definition
| • whether the numbers in our sample deviate from the null hypothesis that there is no relationship b/t variables |
|
|
Term
| What does a big chi-square imply about the relationship between two nominal/ordinal variables? |
|
Definition
| • that there is a relationship between our two nominal variables |
|
|
Term
| What is degrees of freedom in crosstabs and chi-squared? |
|
Definition
• the number of rows and columns
(r-1)(c-1)=df |
|
|
Term
| If the chi-squared statistic is significant, are your two nominal/ordinal variables related? |
|
Definition
|
|
Term
| When we add controls, what are we considering when looking at relationships between two variables? |
|
Definition
| • We are considering other variables. |
|
|
Term
|
Definition
| • The intersection of a row and a column in a bivariate table. |
|
|
Term
|
Definition
| • The row and column totals in a bivariate table. |
|
|
Term
| What are column percentages? |
|
Definition
|
|
Term
| What are row percentages? |
|
Definition
|
|
Term
|
Definition
| • Elaboration is a process designed to further explore a bivariate relationship; it involves the introduction of control variables. |
|
|
Term
| What is a control variable? |
|
Definition
| • A control variable is an additional variable considered in a bivariate relationship. The variable is controlled for when we take into account its effect on the variables in the bivariate relationship |
|
|
Term
| In elaboration, is the independent variable usually the column variable or row variable? |
|
Definition
|
|
Term
| In elaboration, is the dependent variable usually the column variable or row variable? |
|
Definition
|
|
Term
| Is it easier to use column percentages for elaboration? |
|
Definition
|
|
Term
| What is a direct causal relationship? |
|
Definition
| • A direct causal relationship is a bivariate relationship that cannot be accounted for by other theoretically relevant variables. |
|
|
Term
| What is a spurious relationship? |
|
Definition
| • A spurious relationship is a relationship in which both the IV and DV are influenced by a causally prior control variable and there is no causal link between them. The relationship between the IV and DV is said to be “explained away” by the control variable. |
|
|
Term
|
Definition
| • Partial tables are bivariate tables that display the relationship between the IV and DV while controlling for a third variable. |
|
|
Term
| What are partial relationships? |
|
Definition
| • Partial relationships are the relationship between the IV and DV shown in a partial table. |
|
|
Term
| What is an intervening variable? |
|
Definition
| • An intervening variable is a control variable that follows an independent variable but precedes the dependent variable in a causal sequence. |
|
|
Term
| What is a conditional relationship? |
|
Definition
| • A conditional relationship is a relationship in which the control variable’s effect on the dependent variable is conditional on its interaction with the independent variable. The relationship between the independent and dependent variables will change according to the different conditions of the control variable. |
|
|
Term
| What is another way to say a conditional relationship? |
|
Definition
| • Another way to describe a conditional relationship is to say that there is a statistical interaction between the control variable and the independent variable. |
|
|
Term
| What is the value of the slope of a flat, horizontal line? |
|
Definition
|
|
Term
| What do we call a slope in statistics? |
|
Definition
|
|
Term
| What are the two critical t statistics to reject the null hypothesis that b=0? |
|
Definition
|
|
Term
| What can be the levels of measurements for the dependent variable for correlation and regression? |
|
Definition
|
|
Term
| What can be the levels of measurements for the independent variable for correlation and regression? |
|
Definition
|
|
Term
| What is a continuous variable? |
|
Definition
| • A variable can have values of 1, 2, 3, 4, and as well as in between such as 1.3, 2.7, 3.5, and so forth. Length, weight, time are examples of continuous variables. |
|
|
Term
| What is a discrete variable? |
|
Definition
| • A variable can only take on whole values such as 1, 2, 3, 4, and so forth. On example is gender in which you code female as “1” and male as “0.” Political party is another example. |
|
|
Term
| What is a bivariate relationship? |
|
Definition
| • Two variables are related to (associated with) each other. |
|
|
Term
|
Definition
| • Correlation is a statistic that assesses the direction and strength of the linear association of two interval/ratio variables . . . It is created through a technique called “regression” |
|
|
Term
| What is a bivariate regression? |
|
Definition
| • Bivariate regression is a technique that fits a straight line as close as possible between all the coordinates of two interval/ratio variables plotted on a two-dimensional graph--to summarize the relationship between the variables |
|
|
Term
| Does the slope of the regression line tell us whether relationship between two variables is positive, negative, or no relationship? |
|
Definition
|
|
Term
| Do we use squared deviations to fit a regression line that is closest to all data points |
|
Definition
|
|
Term
|
Definition
|
|
Term
| Is correlation a standardized slope? |
|
Definition
| • The correlation is the standardized slope, it refers to the standard deviation change in Y when you go up a standard deviation in X. |
|
|
Term
| What do we mean by standardize? |
|
Definition
| • To standardize something is to make similar ranges between two variables. Remember that we had an example of SAT scores and ACT scores. In order to compare equivalent scores, we used standard deviation |
|
|
Term
| What does the Pearson correlation tell us? |
|
Definition
| • The Pearson correlation t tells the direction and strength of the relationship between continuous variables, that it ranges from -1 to +1, is + when the relationship is positive and - when the relationship is negative, the higher the absolute value of r, the stronger the association, a standard deviation change in x corresponds with r standard deviation change in Y. The pearson correlation is a statistic that is an inferential statistic too. It also tells us that r - (null = 0) and tn-2 = (1-r2) (n-2) When it is significant, there is a linear relationship between the two variables in the population—we reject the null hypothesis that there is no relationship between the two variables. |
|
|
Term
| What can R2 be interpreted as? |
|
Definition
| • The coefficient of determination. |
|
|
Term
| In multiple regression, are there two or more interval-ratio or dichotomous variables as independent variables? |
|
Definition
|
|
Term
| If we are looking at the effects of education and family income on the number of children, what are the two null hypotheses? |
|
Definition
| • There is no relationship between education of respondents and the number of children in families in the population. Ho : b1 = 0 And There is no relationship between family income and the number of children in families in the population. Ho : b2 = 0 |
|
|
Term
| What are dummy variables? |
|
Definition
• They are simply dichotomous variables that are entered into regression.
• They have 0 – 1 coding where 0 = absence of something and 1 = presence of something. E.g., Female (0=M; 1=F) or Southern (0=Non-Southern; 1=Southern). |
|
|
Term
| Do you leave out of one of the dummy variables in a multiple regression equation? |
|
Definition
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, is the male dummy variable or the female dummy variable left out? |
|
Definition
| • The male dummy variable is left out. |
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, which race variable is left out? |
|
Definition
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, how many points are lower for women’s self-esteem compared to men’s self-esteem? |
|
Definition
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, how many points are higher for Blacks’ self-esteem compared to Whites’ self-esteem? |
|
Definition
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, how many points are lower for Others’(referring to other races) self-esteem compared to Whites’ self-esteem? |
|
Definition
| • Others’ is two points lower and is seven points lower than blacks’. |
|
|
Term
| In the regression equation in which the dependent variable is self-esteem, each year of education improves self-esteem by how many units? |
|
Definition
|
|
Term
| What is the self-esteem score of White males with 10 years of education? |
|
Definition
|
|
Term
| What is the self-esteem score of Black females with 10 years of education? |
|
Definition
|
|
Term
| What is the self-esteem score of White females with 20 years of education? |
|
Definition
|
|
Term
| What is the self-esteem score of Other (referring to other races) males with 20 years of education? |
|
Definition
|
|
Term
| Do most people read articles straight through? |
|
Definition
| • No, the read the title and abstract first. |
|
|
Term
| Is a value that is zero or close to zero suggest a relationship between two variables? |
|
Definition
| • No, it suggests no relationship. |
|
|
Term
| What does one asterisk indicate? |
|
Definition
| • That we reject the null hypothesis, P < .05 |
|
|
Term
| What does two asterisks indicate? |
|
Definition
|
|
Term
| What does three asterisks indicate? |
|
Definition
|
|
Term
Table 1: Below is SPSS output for the confidence interval for owning a home
One-Sample Test Test-Value=0 dwelownx:
T=27.787
df=489
Sig.(2-tailed)=.000
Mean Difference=.61224
95% Confidence Interval of the Differences:
Lower=.5690
Upper .6555
Is the confidence level 95% or 99%?
What is the confidence interval of persons who own a house? |
|
Definition
• The Confidence level is 95%
• The 95% confidence interval of persons who own a house is .5490 and .6555
• Or We are 95% confident that the population percentage falls between .5490 and .6555 |
|
|
Term
Below is the SPSS output for ANOVA for views on life and hours on the internet.
Between Groups
Sum of Squares=1535.338
DF = 2
Mean Square= 767.669
F=6.639
Sig.=.001
Within Groups
Sum of Squares=211503.081 DF=1829
Mean Square=115.639
Total
Sum of Squares=213038.419 DF=1831
What are the two ways to state the null hypothesis? |
|
Definition
• The null hypothesis is that the variation in attitudes toward life is not associated with the variation in the number of hours on the internet in the population.
• The null hypothesis is that the population differences between means are 0. |
|
|
Term
Below is the SPSS output for ANOVA for views on life and hours on the internet.
Between Groups
Sum of Squares=1535.338
DF = 2
Mean Square= 767.669
F=6.639
Sig.=.001
Within Groups
Sum of Squares=211503.081 DF=1829
Mean Square=115.639
Total
Sum of Squares=213038.419
DF=1831
What is the F? |
|
Definition
|
|
Term
Below is the SPSS output for ANOVA for views on life and hours on the internet.
Between Groups
Sum of Squares=1535.338
DF = 2
Mean Square= 767.669
F=6.639
Sig.=.001
Within Groups
Sum of Squares=211503.081 DF=1829
Mean Square=115.639
Total
Sum of Squares=213038.419
DF=1831
How do we interpret the P value? |
|
Definition
| • The probability that the statistics come from a population where the null hypothesis is true is .1%. |
|
|
Term
Below is the SPSS output for ANOVA for views on life and hours on the internet.
Between Groups
Sum of Squares=1535.338
DF = 2
Mean Square= 767.669
F=6.639
Sig.=.001
Within Groups
Sum of Squares=211503.081 DF=1829
Mean Square=115.639
Total
Sum of Squares=213038.419
DF=1831
What are the two ways to interpret the result of the null hypothesis? |
|
Definition
• We reject the null hypothesis that the variation in the attitudes toward life is not associated with the variation in the number of hours on the internet in the population.
• We reject the null hypothesis that the population differences between means are 0. |
|
|
Term
| What are random (chance) errors known as? |
|
Definition
|
|
Term
| What does the central limit theorem say? |
|
Definition
| • That the sampling distribution of means is normal in shape (i.e. it forms a normal curve) |
|
|
Term
| What term is used to describe the standard deviation of a sampling distribution? |
|
Definition
| • The Standard error of the mean |
|
|
Term
| If the sample is larger, the standard error of the mean is smaller or larger? |
|
Definition
| • Smaller the standard error |
|
|
Term
| If the sample is less variable, is the standard error of the mean smaller or larger? |
|
Definition
| • Smaller the standard error |
|
|
Term
| What percentages are more commonly reported on confidence intervals? |
|
Definition
|
|
Term
| Does using a large sample keep the standard error of a mean and confidence intervals small? |
|
Definition
|
|
Term
| Can the difference between two means be due to sampling error? |
|
Definition
|
|
Term
| Is this an appropriate null hypothesis: “There is no true difference between the means.”? |
|
Definition
|
|
Term
| What is the symbol for the null hypothesis? |
|
Definition
|
|
Term
| What type of hypothesis predicts that one particular group’s mean will be higher than other group’s mean? |
|
Definition
| • Directional Research Hypothesis |
|
|
Term
| What does a significant test yield? |
|
Definition
| • A probability that the null hypothesis is true |
|
|
Term
| If the p is less than 5 in 100 in a null hypothesis, do we conclude that null hypothesis is not likely to be true? |
|
Definition
|
|
Term
|
Definition
| • Rejecting the null-hypothesis when it is true |
|
|
Term
|
Definition
| • Failing to reject the null-hypothesis when it is false |
|
|
Term
| Is the “.05 level” or the “.01 level” more significant? |
|
Definition
|
|
Term
| When p > .05, is the difference between two means usually regarded as “statistically significant” or “statistically insignificant”? |
|
Definition
| • Statistically insignificant |
|
|
Term
| When the t test yields a low probability that the null hypothesis is correct, do researchers usually reject the null hypothesis? |
|
Definition
|
|
Term
| The larger the sample, is the null hypothesis more likely to be rejected? |
|
Definition
|
|
Term
| The larger the observed difference between the two means, is the null hypothesis is more likely to be rejected? |
|
Definition
|
|
Term
| The smaller the variance, is the null hypothesis is more likely to be rejected? |
|
Definition
|
|
Term
| Is the null hypothesis rejected when p > .05? |
|
Definition
|
|
Term
| What is the synonym for rejecting the null hypothesis? |
|
Definition
| • Statistically Significant |
|
|
Term
| What does ANOVA stand for? |
|
Definition
|
|
Term
|
Definition
| • To test the differences among 2 or more means |
|
|
Term
| What test is used for ANOVA? |
|
Definition
|
|
Term
| Do we reject the null hypothesis for ANOVA when one or more differences in means is statistically significant? |
|
Definition
|
|
Term
| What is a direct relationship? |
|
Definition
• Those who score high on one variable score high on the other
• Those who score low on one variable score low on the other |
|
|
Term
| Is a direct relationship the same thing as a positive relationship? |
|
Definition
|
|
Term
| What is an inverse relationship? |
|
Definition
| • Those who score high on one variable score low on the other |
|
|
Term
| Is an inverse relationship the same thing as a negative relationship? |
|
Definition
|
|
Term
| What is the range of the Pearson r? |
|
Definition
|
|
Term
| Does -1.00 indicate the perfect inverse relationship? |
|
Definition
|
|
Term
| Does 1.00 indicate the perfect direct relationship? |
|
Definition
|
|
Term
| Does 0.00 indicate the complete absence of a relationship? |
|
Definition
|
|
Term
| The closer a value to 0.00, is the relationship stronger? |
|
Definition
| • No, the closer to 0 a relationship gets the weaker it gets |
|
|
Term
| The closer a value to -1.00 or 1.00, is the relationship weaker? |
|
Definition
| • No, the closer the relationship value gets to -1 or 1 the stronger the relationship gets |
|
|
Term
|
Definition
| • No, multiplying by a hundred does not yield a percentage |
|
|
Term
| Is it possible for a relationship to be both direct and weak? |
|
Definition
| • Yes, example would be a .01 relationship. Because .01 is positive it is a direct relationship, but because it is close to 0 it is considered weak. |
|
|
Term
| Is it possible for a relationship to be both inverse and strong? |
|
Definition
| • Yes, example would be -1 relationship. Because -1 is negative it is considered to be inverse, but because it is -1 it is considered strong |
|
|
Term
| Which of the following values of r represents the strongest relationship: .64, -.79, or 0.00? |
|
Definition
|
|
Term
| How do we obtain the coefficient of determination? |
|
Definition
| We square Pearson’s r which is represented by r^2 |
|
|
Term
| What is the coefficient of determination when converted to a percentage? |
|
Definition
| • It indicates how much variance on one variable is accounted for by the variance on the other |
|
|
Term
| When r = .40, is the percentage of variance accounted for is 16%? (Remember to square and convert to percentage.) |
|
Definition
| • Yes, .40x.40=.16x100=16% |
|
|
Term
| When r = .40, is the percentage of variance not accounted for is 84%? (Remember to square, convert to percentage, and subtract from 100%.) |
|
Definition
| • Yes, .40x.40=.16x100=16% 100%-16%=84% |
|
|
Term
| When r = .80, what is the percentage of variance accounted for? |
|
Definition
| • 64%, this is calculated by .80x.80=.64x100=64% |
|
|
Term
| When r = .80, what is the percentage of variance not accounted for? |
|
Definition
| • 36%, this is calculated by .80x.80=.64x100=64% then 100%-64%=36% |
|
|
Term
| What does a scattergram illustrate? |
|
Definition
| • Correlation between 2 variables |
|
|
Term
| When the dots on the scattergram are more scattered, is the relationship between two variables weaker? |
|
Definition
|
|
Term
| If the dots in a scattergram form a pattern from the lower left to the upper right, is the relationship inverse? |
|
Definition
| • No, the relationship is direct. To have an inverse relationship in a scattergram the pattern must go from the upper left to the lower right |
|
|
