Stats for review Flashcards

1
Q

An educational psychologist has data on 12 different variables collected from students in the graduating high school class of the preceding year, including high school GPA, SAT scores, teacher ratings, and various tests of motivation and personality. She is interested in using these measures to predict success in college. In this instance, the psychologist would use stepwise multiple regression in order to
Select one:
A. develop a predictive equation using all 12 measures.
B. determine the optimal set of measures to use.
C. determine if mean differences on the 12 measures significantly differ from each other.
D. identify any cultural bias in the predictor or criterion measure.

A

Correct Answer is: B
Stepwise multiple regression is a variation of multiple regression. In multiple regression, one develops an equation that uses two or more predictor variables to predict scores on a criterion (outcome) variable. Stepwise multiple regression involves starting with a large set of predictors and reducing them to a smaller set that provides significant predictive value without providing overlapping information. Specifically, the goal is to get predictors that have high enough correlations with the criterion and low enough correlations with each to be included. If predictors have high correlations with each other, they are basically providing overlapping information and there is no point in including them. The two variations of this technique are forward stepwise multiple regression and backwards stepwise multiple regression. In forward stepwise multiple regression, you choose the predictor with the highest correlation with the criterion, you add one predictor at a time, and then run a significance test to see if the added predictor significantly increases the combined predictive value of the overall equation. The process stops when an added predictor fails to significantly increase predictive value. In backwards stepwise regression, you start with all predictors, and remove predictors, starting with the one that is least correlated with the criterion. This process ends when removal of a predictor causes a significant decrease in the ability of the equation to predict values on the criterion.

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2
Q

Which of the following techniques would be appropriate when multiple predictors will be used to predict a score on a single criterion?
Select one:
A. multiple regression analysis
B. multiple discriminant function analysis
C. principal components analysis
D. linear regression analysis

A

Correct Answer is: A
When multiple predictors will be used to predict a score on a single criterion, multiple regression is appropriate. Discriminant function analysis is used to determine which continuous variables discriminate between two or more naturally occurring groups.
Multiple discriminant function analysis, an extension of discriminant function analysis, involves using multiple predictors to sort individuals into one of three or more criterion groups.

Principle components analysis, similar to factor analysis, is used to determine the variables or components that account for the total variance in test scores.

When a single predictor is used to predict or estimate a score on a single criterion, linear regression is appropriate.

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3
Q
The use of "pooled variance" in statistics assumes that:
Select one:
A. the sample sizes are equal
B. the sample variances are equal
C. the population sizes are equal
D. the population variances are equal
A

Correct Answer is: D
Pooled variance is the weighted average variance for each group. They are “weighted” based on the number of subjects in each group. Use of a pooled variance assumes that the population variances are approximately the same, even though the sample variances differ

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4
Q

Which of the following is NOT generally considered a direct threat to external validity?
Select one:
A. order effects
B. hawthorne Effect
C. interaction between selection and treatment
D. history

A

Correct Answer is: D
Distinguishing between internal and external threats to validity can be difficult. Indeed, some experts disagree on how to categorize some of them. However, all of the choices except “history” are generally considered to be threats to external validity.
Order effects* (also known as carryover effects) occurs in repeated measures designs, or in studies in which the same subjects are exposed to more than one treatment. For example, in a study on the effects of marital therapy interventions, couples are given relaxation training followed by communication training. If significant improvement occurs, it may be due to relaxation training preceding communication training; therefore, the results could not be generalized to situations in which subjects only receive communication training.

The Hawthorne effect* occurs when subjects behave differently due to the fact that they are participating in research. Obviously this threatens external validity since the results cannot be generalized to real-life situations in which people are not participating in research.

Interaction between selection and treatment* refers to when a treatment has different effects depending on the selection of subjects. For example, studies that only use undergraduate students (as many studies do) might not generalize to non-undergraduate students (* incorrect options).

Finally, history refers to an external event, other than the experimental treatment, that affects scores on the DV. This is primarily considered a threat to internal validity. For example, if a study on the effects of a new treatment for depression began several weeks before the events on “9-11” and concluded several weeks after “9-11,” the results might indicate that the new treatment is not effective. However, this might not be a valid conclusion due to the effects of history.

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5
Q
A test measuring verbal fluency is administered to 250 college students, and a split-half reliability coefficient is obtained. If the same test instead had been administered to 250 students aged 12-21, the obtained reliability coefficient probably would have
Select one:
A. been higher.
B. been lower.
C. remained about the same.
D. moved from negative to positive.
A

One factor that affects any correlation coefficient, including a reliability coefficient, is the range of scores. If the range of scores is restricted on either or both sets of scores, the correlation coefficient will be lowered. The two sets of scores involved in a split-half reliability coefficient are scores obtained by the same group of individuals on two different halves on the test. Originally, the test was administered to only college students. In the second scenario, the test was administered to a broader range of students.

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6
Q

A psychologist uses a two-group pretest/posttest design to evaluate the effects of a new treatment. She obtains the following data:
PreTest Post Test
Group 1 Mean 13.4, SD 1.2 Mean 19.8, SD 1.5
Group 2 Mean 19.5, SD 1.5 Mean 21.7, SD 1.9

The biggest threat to this study's internal validity is
Select one:
A. reactivity.
B. test x treatment.
C. selection.
D. history
A

Correct Answer is: C
In this study the means of the two groups are very different initially (Pretest), which will make it hard to interpret the results. When internal validity is threatened by initial group differences, this threat is called selection. Note that the term selection is misleading because it actually refers to assignment. If assignment was random, we would expect the pretest scores for Groups 1 and 2 to be approximately equal, which they are not, 13.4 and 19.5, respectively.

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7
Q
You are investigating whether there is a relationship between the number of years one has been smoking cigarettes and the number of psychotherapy sessions required to quit smoking. The best statistical method to analyze the results is:
Select one:
A. chi-square
B. Pearson r
C. t-test for independent samples
D. multiple regression analysis
A

Correct Answer is: B
In this case, you are attempting to assess the relationship between two variables that are measured on a continuous (interval or ratio) scale. The Pearson r allows you to do this. The Pearson r is the bivariate (i.e., for two variables) correlation coefficient used when variables are measured on an interval or ratio scale.

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8
Q

According to the central limit theorem,
Select one:
A. as sample size increases, the shape of a sample distribution becomes more normal.
B. as the size of a sampling distribution of means increases, its distribution becomes more normal.
C. as sample size increases, the shape of a sampling distribution of means becomes more normal.
D. as sample size increases, the shape of a sampling distribution of means approximates the shape of the population distribution.

A

Correct Answer is: C
According to the central limit theorem, the shape of a sampling distribution of means approaches normality as sample size increases. The central limit theorem is covered in the Advanced Statistics section of your materials, and you should study it after you have a reasonably solid grasp of the material presented in the rest of the section.

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9
Q
Which of the following is NOT a disadvantage of a repeated measures design?
Select one:
A. multicollinearity
B. autocorrelation
C. practice effects
D. carryover effects
A

Correct Answer is: A
A “repeated measures” design, sometimes referred to as a “within-subjects design,” uses more than one measurement of a given variable for each subject. For example, longitudinal studies and pre-test/post-test designs measure the same subjects multiple times. These designs have several disadvantages including: “Autocorrelation”, which means that observations obtained close together in time from the same subjects tend to be highly correlated. This violates the independence of observations assumption made by statistical tests. “Practice effects”, “carryover effects”, and “order effects” all refer to systematic changes in subjects’ performance due to prior exposure to a treatment condition or measurement ( incorrect options) .
However, multicollinearity refers to a problem associated with multiple regression which occurs when two or more predictors are highly correlated with each other.

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10
Q
A psychologist believes that physical exercise can reduce a person's anxiety level, which reduces the strength of substance cravings in people recovering from substance dependence. According to this hypothesis anxiety is a:
Select one:
A. suppressor variable
B. mediator variable
C. moderator variable
D. criterion contaminator
A

Correct Answer is: B
A mediator variable is a variable that accounts for or explains the effects of an IV on a DV. That is, the IV affects the mediator variable, which affects the DV. In this example, the IV is exercise, the mediator variable is anxiety, which explains how the DV, substance craving, is reduced.
A moderator variable is similar to a mediator variable, but a moderator variable only influences the strength of the relationship between two other variables, it doesn’t fully account for it. For example, if a job selection test has different validity coefficients for different ethnic groups, ethnicity would be a moderator variable because it influences the relationship between the test (predictor) and actual job performance (the criterion) but it does not fully account for the relationship.

A suppressor variable reduces or conceals the relationship between variables. For example, the K scale in the MMPI-2 is a suppressor variable because it measures defensiveness, which can suppress the scores on the clinical scales. The K scale is, therefore, used as a correction factor for some of the clinical scales.

Criterion contamination is the artificial inflation of validity which can occur when raters subjectively score ratees on a criterion measure after they have been informed how the ratees scored on the predictor.

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11
Q
A colleague of yours is interested in studying the effects of aging on IQ scores. He consults with you for some ideas regarding how to proceed with this research. Which of the following types of research designs would you recommend?
Select one:
A. longitudinal
B. cross-sectional
C. cross-sequential
D. multiple baseline
A

Correct Answer is: C
The colleague is interested in conducting developmental research, in which the effects of development (e.g., aging) on a dependent variable (in this case, IQ scores) are investigated. Longitudinal, cross-sectional, and cross-sequential are all types of developmental research designs. Of these, cross-sequential research is the strongest from a scientific point of view. Cross-sequential research is a combination of cross-sectional and longitudinal research. In cross-sequential research, as in cross-sectional research, subjects are divided into age groups (e.g., young, middle-aged, and old). And, as in longitudinal research, subjects are assessed repeatedly on the dependent variable over time. Because cross-sequential research combines the methodology of the two strategies, it is not associated with the limitations of one or the other.

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12
Q

A factorial design, unlike a two group design:
Select one:
A. allows more independent variables to be studied
B. requires a larger sample
C. shows the effect of an independent variable on the dependent variable
D. cannot detect a curvilinear relationship between variables

A

Correct Answer is: A
In a two group design, one group is exposed to a treatment and another, control group, is not exposed or gets a different treatment. The results of both groups are tested in order to compare the effects of treatment. A factorial design is a design with more than one independent variable. In this design, the independent variables are simultaneously investigated to determine the independent and interactive influence they have on the dependent variable. The effect of each independent variable on the dependent variable is called a main effect and in a factorial design there are as many main effects as there are independent variables. An interaction effect between two or more independent variables occurs when the effect that one independent variable has on the dependent variable depends on the level of the other independent variable.
At least three levels must be used to predict a curvilinear relationship.

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13
Q
A 10-year-old child is administered the WISC-III and obtains a score of 140 Full Scale IQ. If she is retested at the age of 16, her IQ score will most likely be:
Select one:
A. higher
B. lower
C. the same
D. impossible to predict
A

Correct Answer is: B
This question was a little tricky in that it appears to be about the reliability of IQ scores over time, when it is really a statistics question. The WISC-III does have very good reliability over time and, if the IQ score was in the normal range, we could predict that it would stay the same over time. However, a score of 140 on the WISC-III is extremely high (classified as “very superior”) and would, therefore, likely be lower upon retesting due to regression to the mean – which is the tendency of extreme scores to be less extreme upon retesting.

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14
Q
All of the following are measures of variability except:
Select one:
A. variance
B. standard error
C. range
D. standard deviation
A
Correct Answer is: B
Variability represents the amount of difference found in responses from a population or sample on a topic being investigated. Variance*, range*, and standard deviation* all reflect the variability in the data (* incorrect options).
Standard error (of measurement) is not a measure of variability. It is a statistic indicating the amount of difference in results that is accounted for by flaws or "noise" in the instrument used to measure a variable.
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15
Q
While studying the use of journaling in the treatment of depression, a researcher finds only individuals with good writing ability benefit from journaling. Writing ability is a(n):
Select one:
A. outcome variable
B. mediating variable
C. moderator variable
D. feedback variable
A

Porrect Answer is: C
The strength of the relationship between the independent and dependent variables is affected by a moderator variable. Writing ability is moderating the effects of journaling on the treatment of depression.
Outcome variable* is another term for dependent variable. A mediating variable* is affected by the independent variable and affects the dependent. It is responsible for an observed relationship between an independent variable and a dependent (outcome) variable. A feedback variable* is an unrelated term (* incorrect options).

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16
Q

Path analysis is useful for:
Select one:
A. examining the unidirectional relationships among a set of measured and latent traits.
B. examining the bidirectional relationships among a set of measured and latent traits.
C. examining the unidirectional causal relationships among a set of measured traits.
D. examining the bidirectional causal relationships among a set of measured traits.

A

Correct Answer is: C
Path analysis is a causal modeling technique. It is somewhat limited compared to other techniques because it permits only one-way (unidirectional) paths between variables and involves looking only at the relationships among measured variables. (LISREL, a more complicated technique, looks at both measured variables and the latent traits measured by those variables and permits one- and two-way paths.)

17
Q

To use the statistical technique known as trend analysis, you need:
Select one:
A. a quantitative independent variable.
B. a linear relationship between independent and dependent variables.
C. a true experimental research design.
D. two or more independent variables

A

Correct Answer is: A
Trend analysis is what is sounds like; i.e., it is used to identify trends and, therefore, requires a quantitative independent variable. You might use trend analysis, for example, to determine if amount of time you spend studying is related to your score on the licensing exam in a linear or nonlinear fashion.

18
Q

In forward stepwise multiple regression analysis, the goal is to obtain the smallest subset of predictors to account for the largest amount of variability in the criterion variable. Statistically, this involves:
Select one:
A. adding predictors to the multiple regression equation and determining, through statistical analysis, if the coefficient of multiple determination is significantly increased
B. using the correction for attenuation formula to estimate what the predictive power of the multiple regression equation would be if all the predictors had perfect reliability
C. using the Spearman-Brown Prophecy formula to estimate the magnitude of the multiple correlation coefficient if all the predictors were used, and comparing the result to the magnitude of the coefficient when different subsets of the predictors are used
D. administering different subsets of the predictors to two validation samples, and conducting statistical analyses to estimate the degree of shrinkage in the multiple correlation coefficient from the first to the second validation sample

A

Correct Answer is: A
The goal of stepwise regression analysis is to derive the smallest subset of predictors, out of a larger set, that maximizes the ability to predict outcome on a criterion variable. There are two types of stepwise multiple regression: forward and backward. In forward stepwise regression, predictors are successively added to the multiple regression equation. With each addition, an analysis is conducted to determine if the predictive power of the equation is increased. Predictive power is measured by the squared multiple correlation coefficient (also known as the coefficient of multiple determination).

19
Q
The probands, in a study comparing characteristics of adult ADHD patients, with characteristics of their first degree and second degree biological relatives and non-patients (controls), are:
Select one:
A. non-patients
B. first degree relatives
C. first and second degree relatives
D. ADHD patients Correct
A

Correct Answer is: D
The ADHD patients are the probands in this study. Probands, or index cases, are the individuals who are first brought to the attention of the researcher - i.e., individuals manifesting the characteristic of interest or disease.

20
Q
Which of the following is a measure of "amount of variability accounted for"
Select one:
A. alpha
B. Cohen's d
C. eta squared
D. F-ratio
A

Correct Answer is: C
The “amount of variability accounted for” is assessed by a squared correlation coefficient. Eta squared is the square of the correlation coefficient (i.e., the correlation between the treatment and the outcome) and is used as an index of effect size.
Alpha* is the level of significance set by a researcher prior to analyzing the data. Cohen’s d* is used as an index of effect size, but it is a measure of the mean difference between two groups. The F-ratio* is the statistic calculated when using the analysis of variance (* incorrect options).

21
Q
A set of past graduate students are divided into two groups by a doctorate admissions committee. One group consists of students who finished the program in five years or less, the other consists of those who did not. Based on undergraduate grade point average and GRE score, which of the following could be used to predict successful completion of the graduate program?
Select one:
A. MANOVA
B. Structural equation modeling
C. Discriminant function analysis
D. Cluster analysis
A

Correct Answer is: C
Discriminant function analysis (DA) is used to determine which continuous variables discriminate between two or more naturally occurring groups, or provide insights into how each predictor (e.g., grades, GRE score) individually and/or in combination predicted completion or non-completion of a graduate program. In DA, the independent variables are the predictors and the dependent variables are the groups. In contrast, in MANOVA, the independent variables are the groups and the dependent variables are the predictors.
A multivariate analysis of variance (MANOVA) is used to analyze the effects of one or more independent variables on two or more dependent variables that are each measured on an interval or ratio scale.

Structural equation modeling is a technique used to evaluate or confirm the cause-and-effect or hypothesized relationship between both measured and latent variables.

Cluster analysis is a method for grouping objects of similar kind into respective categories. It can be used to discover structures in data without providing an explanation/interpretation.

22
Q
A psychological researcher would like to determine what variables best distinguish between patients who benefit from psychotherapy and patients who do not. To identify these variables, the research would most likely use which of the following?
Select one:
A. discriminant function analysis
B. factor analysis
C. canonical correlation
D. MANOVA
A

Correct Answer is: A
Discriminant function analysis is used to identify variables that distinguish between two or more existing or naturally occurring groups. Its use would involve collecting data on a variety of measures and determining which combination of them best predict differences between the groups. Since the researcher’s purpose is to find variables that distinguish between existing groups, discriminant function analysis is the best answer.
Regarding the other choices, factor analysis is used to reduce variability in a set of variables to a smaller set of unobserved variables, or factors. For example, factor analysis might be use to confirm a theory that score differences on a variety of intelligence measures can be explained in terms of two factors, verbal intelligence and performance intelligence.

Canonical correlation is a technique for assessing the relationship between two sets of variables: i.e., it is used to assess the relationship between multiple predictor and multiple criterion variables.

And MANOVA, or multivariate analysis of variance, is used in research studies to evaluate the effects of one or more independent variables on multiple (two or more) dependent variables.

23
Q
A significant finding for a one-way ANOVA indicates that the
Select one:
A. group means were different.
B. sample means were different.
C. population means were different.
D. within-group variance was different.
A

Correct Answer is: C
We use statistical tests to make inferences about a population. So if we have significant results, we assume that this represents what happens in the real world – that is, in the population.

24
Q

In the analysis of the effects of two independent variables, multiple regression analysis is sometimes used as a substitute for the factorial ANOVA. One advantage of using multiple regression as opposed to a factorial ANOVA is that:
Select one:
A. multiple regression analysis can be used for multiple dependent variables as well as multiple independent variables.
B. continuous or categorical data (as opposed to solely categorical data) can be used to measure the independent variables in multiple regression analysis.
C. the use of multiple regression allows one to estimate the probability that obtained differences on the dependent variable between groups represent true population differences.
D. when multiple regression is used and a significant result is obtained, the conclusion that there is a causal relationship between the independent variables and the dependent variables is more plausible.

A

Correct Answer is: B
One limitation of the ANOVA technique is that independent variables must be divided into categories for the analysis to be conducted. In multiple regression, the researcher has the choice of using categories or continuous data (e.g., scores on a test) to measure the independent variables. This is considered an advantage of regression, because it allows for the data to provide more precise and specific information about the variables being measured.
multiple regression analysis can be used for multiple dependent variables as well as multiple independent variables.

This choice is not true of multiple regression; it is designed for use with one dependent variable only.

when multiple regression is used and a significant result is obtained, the conclusion that there is a causal relationship between the independent variables and the dependent variables is more plausible.

This is also not true; the strength of the conclusion that variables are causally related depends on the research design, not the statistical analysis.

the use of multiple regression allows one to estimate the probability that obtained differences on the dependent variable between groups represent true population differences.

This is true of both multiple regression and ANOVA, since they are both inferential statistical methods.

25
Q
The upper and lower limits of the standard error of measurement for a test with a mean of 80 and standard deviation of 10 are:
Select one:
A. 0 to 80
B. 0 to 10
C. -1.0 to +1.00
D. 0 to +1.0
A

Correct Answer is: B
There is no error in measurement and the standard error of measurement equals zero when the reliability coefficient of a test is equal to +1.0 (the highest reliability coefficient possible). The standard error of measurement equals the standard deviation of the test scores when the test’s reliability coefficient is equal to 0 (the lowest possible). It is helpful to know the formula for the standard error of measurement: the standard error of measurement equals the standard deviation times the square root of one minus the reliability coefficient, when answering this type of question.

26
Q
Which of the following techniques is most similar to latent trait analysis (LTA)?
Select one:
A. cluster sampling
B. analysis of covariance
C. multitrait-multimethod matrix
D. latent class analysis
A
Correct Answer is: D
Latent class analysis, like latent trait analysis, is used to identify the underlying latent structure of a set of observed data. The techniques differ in that in LTA, the latent variable that determines the structure is continuous whereas in LCA, the latent variable is nominal.
Cluster sampling is a sampling technique in which groups of participants are selected instead of individuals.

Used to statistically remove the effects of the covariate, or an extraneous variable, on the dependent variable, the analysis of covariance (ANCOVA) makes it easier to determine the effects of the independent variable on the dependent variable.

The multitrait-multimethod matrix is used to assess convergent and divergent validity.

27
Q

A percentile rank is
Select one:
A. a norm-referenced score, but not a standard score.
B. a standard score, but not a norm-referenced score.
C. a standard score and a norm-referenced score.
D. neither a standard score nor a norm-referenced score.

A

Correct Answer is: A
To answer this question, you have to be able to define and understand three terms: norm-referenced, standard score, and percentile rank. A norm-referenced score is one that is interpreted in terms of a comparison to others who have taken the same test. A standard score is a type of norm-referenced score that is interpreted in terms of how many standard deviation units a score falls above or below the mean. Examples include z-scores and T-scores. A percentile rank indicates the percentage of scores that fall below a given score. For example, a person who achieves a percentile rank of 90 on the SAT scored better than 90% of others who took the test. Since interpretation of percentile ranks involves a comparison between scorers, a percentile rank is a norm-referenced score. However, since it is not interpreted in terms of standard deviation units, it is not a standard score.

28
Q

All of the following are assumptions of the regression equation, except:
Select one:
A. a linear relationship exists between X and Y.
B. the variability of Y scores is equal throughout the range of X scores.
C. one can predict scores on Y on the basis of scores on X.
D. changes in the level of X cause changes in the level of Y.

A

Correct Answer is: D
A regression equation is used to predict the value of a Y variable on the basis of a person’s score on an X variable. For example, if an industrial psychologist wanted to use a job applicant’s score on a job selection test to predict his future score on a supervisor’s rating scale, he could develop and use a regression equation to do so. When regression is used, there is not necessarily the assumption that changes in the value of X cause changes in Y. The X and Y variables are correlated, but a correlation between two variables does not always mean that they are causally related.
The incorrect choices are all assumptions of the use of regression.

29
Q

In the analysis of the effects of two independent variables, multiple regression analysis is sometimes used as a substitute for the factorial ANOVA. One advantage of using multiple regression as opposed to a factorial ANOVA is that:
Select one:
A. multiple regression analysis can be used for multiple dependent variables as well as multiple independent variables. Incorrect
B. continuous or categorical data (as opposed to solely categorical data) can be used to measure the independent variables in multiple regression analysis.
C. the use of multiple regression allows one to estimate the probability that obtained differences on the dependent variable between groups represent true population differences.
D. when multiple regression is used and a significant result is obtained, the conclusion that there is a causal relationship between the independent variables and the dependent variables is more plausible.

A

Correct Answer is: B
One limitation of the ANOVA technique is that independent variables must be divided into categories for the analysis to be conducted. In multiple regression, the researcher has the choice of using categories or continuous data (e.g., scores on a test) to measure the independent variables. This is considered an advantage of regression, because it allows for the data to provide more precise and specific information about the variables being measured.
multiple regression analysis can be used for multiple dependent variables as well as multiple independent variables.

This choice is not true of multiple regression; it is designed for use with one dependent variable only.

when multiple regression is used and a significant result is obtained, the conclusion that there is a causal relationship between the independent variables and the dependent variables is more plausible.

This is also not true; the strength of the conclusion that variables are causally related depends on the research design, not the statistical analysis.

the use of multiple regression allows one to estimate the probability that obtained differences on the dependent variable between groups represent true population differences.

This is true of both multiple regression and ANOVA, since they are both inferential statistical methods.

30
Q

Single-Simple Chi Square Test

A

“single variable”

Use: one variable; nominal data
Stat: x^2
df = C - 1; C = # of columns

31
Q

Multiple-Simple Chi Square Test

A

“multiple variable”

Use: 2+ variables; frequency of observations in each category (nomical)
Stat: x^2
df = (C-1)(R-1); where C = # of columns, and R = # of rows

32
Q

Statistical power

A

ability to detect (reject) a false null hypothesis

33
Q

Behavioral sampling technique: event sampling (recording)

A
  • observing and recording bx each time it occurs

- best for bxs that occur infrequently, have a long duration, or leave a permanent record/other product

34
Q

Behavioral sampling technique: interval recording

A
  • observing bx for a period of time that has been divided into equal intervals
  • best for studying complex interactions/bxs with no clear beg/end (laughing, talking, playing)
35
Q

Situational sampling

A
  • goal is to observe bx in mult. settings

- helps increase generalizability

36
Q

Sequential analysis

A
  • coding bx sequences rather than isolated bx events

- used to id patterns in complex social bxs (patterns of action b/w partners)