Test reliability determined by a correlation between scores from the same test taken at two different times is called

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Complete the following quiz. Choose your response by highlighting your answer.

1.When we perform an experiment, we

a. measure independent variables,

b. produce dependent variables.

c. produce control variables.

d. produce a comparison.

e. hold independent variables constant.

2. The control group in an experiment

a. fixes the level of a variable across all experimental conditions.

b. is often untreated.

c. receives the same level of the independent variable as the experimental group.

d. refers to the manipulation of the independent variable.

3. In research on the decompression of pregnant rats, the independent variable is ______, a dependent variable is ________, and a control variable is _______________.

a. Reduced air pressure; behavioral tests; strain of the rat

b. Body weight; climbing ability; time of day

c. Atmospheric pressure; age of rat; climbing ability

d. Number of decompressions; body weight; home cage

e. Experimental group; control group; test performance

4. In experiments, independent variables are

a. the result of careful measurements.

b. extraneous to the experiment and held constant.

c. extraneous to the experiment and allowed to vary randomly.

d. independent of experimenter control.

e. varied by the researcher.

5. Dependent variables are

a. manipulated by the researcher.

b. potential independent variables that are held constant.

c. measured by the researcher.

d. probable behavioral causes.

6. One reason a valid experiment may produce null results is

a. the range of levels in the independent variable was insufficient to show an effect.

b. the dependent variable reflects a broad range of performance.

c. that the experiment is conducted in an environment that is too difficult.

d. that reactivity occurs in the participants (e.g., they adopt the role of “good behavior”).

7. In experiments, the independent variable should be _________, the dependent variable should be __________, and the control variable should be ________.

a. controlled; constant; randomized

b. constant; an effect; causal

c. free; restricted; elevated

d. balanced; unconfounded; an effect

e. manipulated; measured; held constant

8. An interaction occurs when

a. an independent variable effects a dependent variable.

b. one independent variable effects a second independent variable.

c. the effect one dependent variable has is not the same at each level of a second dependent variable.

d. the effect one independent variable has is not the same at each level of a second independent variable.

9. Which of the following is an example of the Hawthorne effect?

a. Experimenter bias

b. Reactivity in an experiment

c. Participant observation

d. Unobtrusive outcomes

10.  A variable that inadvertently causes an experimental result is

a.  confounded with the dependent variable.

b.  confounded with the independent variable.

c.  confounded with the control variables.

d.  unlikely to be important in experiments.

11.  Construct validity permits one to do which of the following?

a.  Generalize

b.  Attribute causality

c.  Have confidence in constructs

d.  Support hypothesis

12.  Which of the following is a source of construct invalidity?

a.  Bias

b.  Random error

c.  Carry-over effects

d.  Counterbalancing

13.  If a study has external validity, one is entitled to

a.  generalize.

b.  attribute causality.

c.  have confidence in constructs.

d.  support hypotheses.

14.  Internal validity allows one to do which of the following?

a.  Generalize

b.  Attribute causality

c.  Have confidence in constructs

d.  Support hypotheses

15.  Which of the following is the most likely to have the greatest internal validity?

a.  Surveys

b.  Case studies

c.  Relational research

d.  Experiments

16.  Test reliability determined by a correlation between scores from the same test taken at two different times is called

a.  test-retest reliability.

b.  parallel forms reliability.

c.  split-half reliability.

d.  predictive reliability.

17.  Statistical reliability determines whether results

a.  will occur five percent of the time.

b.  occur because of chance.

c.  are internally valid.

d.  are produced by bias.

18.  Which of the following is a major threat to internal validity?

a.  Confounding

b.  Deviant-case analysis

c.  Truncated range

d.  Dependent variables

19.  A type of validity that is specifically concerned with being able to make causal statements about relationships between variables is _______________ validity.

a.  External

b.  Internal

c.  Construct

d.  Predictive

20.  A replication of research helps to determine ______________ validity.

a.  Construct

b.  External

c.  Internal

d.  Predictive

Team B-Statistics Project Amal Andersen Jessica Bogunovich Jocelyn Cuff Zachary Ramoz PSYCH 625 Mary Sue Farmer April 13, 2015 1 Introduction Key Terms Degrees of Freedom Descriptive Statistics Interval ratio variables Pearson Product-Movement Correlation Positive correlation Significance Level 1. Degrees of Freedom is a value, which is different for different statistical tests, that approximates the sample size of number of individual cells in an experimental design. Descriptive statistics are values that organize and describe the characteristics of a collection of data, sometimes called a data set. Interval variables are those that measure a variable by giving a numerical value in steps Pearson Product-Movement correlations show the strength of a relationship using summations of values from each axis, the summation of the squares of the data points for each axis, and takes the sample number all into a neat equation. Positive correlations show a relationship between variables and a trend moving in the same direction be it small to great or great to small. Significance level is the risk set by the researcher for rejecting a null hypothesis when it is true. 3 Independent T-Test Another analysis we decided to run on the data set was an independent t-test comparing the means of reading, math, and total test scores between males and females. The independent t-test was used because this analysis deals with two groups and the participants were not being tested more than once (per topic over time). 4 Independent T-Test Results Degrees of freedom = 48 for all three tests (math, reading and total score) Math t value = -.487 Significance = .628 Reading t value = -1.250 Significance = .217 Total t value = -.956 Significance = .344 The SPSS output for the independent t-test on the previous slide demonstrates the t value of reading scores, math scores and total test scores, as well as the degrees of freedom (48 for all three computations). If one were to analyze the math scores only, they would find the t value to be -.487 and the significance to be .628. Analyzing the reading scores only we find the t value to be 1.250 and the significance to be .217. Analyzing the total score only one would find the obtained value, or t value, to be -.956 and find the significance to be .344 (p=.344). 5 Pearson Product-Movement Correlation Correlations TESTPREP MATHSCORE TESTPREP Pearson Correlation 1 .653** Sig. (1-tailed) .000 N 50 50 MATHSCORE Pearson Correlation .653** 1 Sig. (1-tailed) .000 N 50 50 **. Correlation is significant at the 0.01 level (1-tailed). The Pearson Product-Movement was run through SPSS to show a bivariate correlation between test preparation and how well the participants scored on the math portion of the test. The chart displays the numbers in a more readable, decipherable fashion with test prep as the x-axis and math score as the y-axis. The two varaibles, test prep and math score, are interval/ratio varaibles, thus the easy conversion to a correlation. 6 Pearson Product-Movement Correlation Results Positive Correlation = .653 As test prep number increases, so does the math score An up slope The visual representation shows the relationship Significance Shoes a relationship Not very strong Meaningful? The results of the correlation show a positive relationship. As the number of hours of test prep, the x-axis, increases so do does the score on the math test, y-axis. The direction of this positive relationship goes up. The scatterplot helps to being a visual representation to the chart for more discernable visuals. Although there is a decent correlation number, at .653, it seems the relationship is not very strong. This is due to the median time of 2 having many points. Also, the outliers also bring down the significance level as well. These results do show there is a relationship between the two varibles and one could argue that more test prep may yield a higher test score; However, it should be noted the realtionship is not high on significance thus making meaningfulness come to question. 7 Descriptive Statistics Descriptive statistics are used to describe the common data from a study (Salkind, 2014). These deliver summaries about the sample used in the study, as well as the types of measures that were used. The descriptive statistics combined with analytical visuals provide a quantitative analysis of the data. Descriptive statistics describe what the data is and illustrates. These are useful when trying to present and describe quantitative data descriptions in manageable pieces (Salkind, 2014). Researchers are able to simplify huge amounts of data in a meaningful way, as each descriptive statistics reduces the large amounts of data into a smaller summary. 8 Descriptive Statistics Summary 50 total participants 26 males 24 females Ages ranged from 25-40 Average age=32 Reading Test Scores ranged from 45-9 Average reading score=75.58 Math Test Scores ranged from 45 to 92 Average math score=75 Total Test Scores ranged from 95 to 186 Average total test score=150.78 Analytical data from a test group of 50 people was collected and studied. There were 26 males and 24 females in the test group. The participants were surveyed on age, sex, years of college experience, caffeine consumption, test prep, as well as math, reading and comprehensive test scores. This analysis focuses on descriptive statistics and uses the age, math score, reading score and total test score variables. The descriptive statistics demonstrate that the age of the participants ranges from 25 to 40 and the participants have an average age of 32. Math scores range from 45 to 92, and the average math score was 75. Reading scores range from 45 to 96 and the average reading score was 75.78. Total scores ranged from 95 to 186 and the average total score was 150.78. 9 Conclusion References Salkind, N. (2014). Statistics for people who think they hate statistics (5th ed.). Thousand Oaks, CA: Sage Publishing.

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Please answer the following in 60 or more words per topic.

 

When would a researcher use a t test of independent means? Provide an example.

 

When would a researcher use a t test of dependent means? Provide an example.

 

What is an analysis of variance (ANOVA)? Describe the theory underlying it.

 

 

When would a researcher use ANOVA for data analysis? Provide an example

Psych 625 Week 6 Team Introduction And Summary

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Team B-Statistics Project

Amal Andersen

Jessica Bogunovich

Jocelyn Cuff

Zachary Ramoz

PSYCH 625

Mary Sue Farmer

April 13, 2015

1

 

Introduction

 

Key Terms

Degrees of Freedom

Descriptive Statistics

Interval ratio variables

Pearson Product-Movement Correlation

Positive correlation

Significance Level

1. Degrees of Freedom is a value, which is different for different statistical tests, that approximates the sample size of number of individual cells in an experimental design.

Descriptive statistics are values that organize and describe the characteristics of a collection of data, sometimes called a data set.

Interval variables are those that measure a variable by giving a numerical value in steps

Pearson Product-Movement correlations show the strength of a relationship using summations of values from each axis, the summation of the squares of the data points for each axis, and takes the sample number all into a neat equation.

Positive correlations show a relationship between variables and a trend moving in the same direction be it small to great or great to small.

Significance level is the risk set by the researcher for rejecting a null hypothesis when it is true.

3

Independent T-Test

Another analysis we decided to run on the data set was an independent t-test comparing the means of reading, math, and total test scores between males and females. The independent t-test was used because this analysis deals with two groups and the participants were not being tested more than once (per topic over time).

4

Independent T-Test Results

Degrees of freedom = 48 for all three tests (math, reading and total score)

Math

t value = -.487

Significance = .628

Reading

t value = -1.250

Significance = .217

Total

t value = -.956

Significance = .344

 

The SPSS output for the independent t-test on the previous slide demonstrates the t value of reading scores, math scores and total test scores, as well as the degrees of freedom (48 for all three computations). If one were to analyze the math scores only, they would find the t value to be -.487 and the significance to be .628. Analyzing the reading scores only we find the t value to be 1.250 and the significance to be .217. Analyzing the total score only one would find the obtained value, or t value, to be -.956 and find the significance to be .344 (p=.344).

5

Pearson Product-Movement Correlation

 

Correlations

TESTPREP MATHSCORE

TESTPREP Pearson Correlation 1 .653**

Sig. (1-tailed) .000

N 50 50

MATHSCORE Pearson Correlation .653** 1

Sig. (1-tailed) .000

N 50 50

**. Correlation is significant at the 0.01 level (1-tailed).

 

The Pearson Product-Movement was run through SPSS to show a bivariate correlation between test preparation and how well the participants scored on the math portion of the test. The chart displays the numbers in a more readable, decipherable fashion with test prep as the x-axis and math score as the y-axis. The two varaibles, test prep and math score, are interval/ratio varaibles, thus the easy conversion to a correlation.

6

Pearson Product-Movement Correlation Results

Positive

Correlation = .653

As test prep number increases, so does the math score

An up slope

The visual representation shows the relationship

Significance

Shoes a relationship

Not very strong

Meaningful?

The results of the correlation show a positive relationship. As the number of hours of test prep, the x-axis, increases so do does the score on the math test, y-axis. The direction of this positive relationship goes up. The scatterplot helps to being a visual representation to the chart for more discernable visuals. Although there is a decent correlation number, at .653, it seems the relationship is not very strong. This is due to the median time of 2 having many points. Also, the outliers also bring down the significance level as well. These results do show there is a relationship between the two varibles and one could argue that more test prep may yield a higher test score; However, it should be noted the realtionship is not high on significance thus making meaningfulness come to question.

7

Descriptive Statistics

Descriptive statistics are used to describe the common data from a study (Salkind, 2014). These deliver summaries about the sample used in the study, as well as the types of measures that were used. The descriptive statistics combined with analytical visuals provide a quantitative analysis of the data. Descriptive statistics describe what the data is and illustrates. These are useful when trying to present and describe quantitative data descriptions in manageable pieces (Salkind, 2014). Researchers are able to simplify huge amounts of data in a meaningful way, as each descriptive statistics reduces the large amounts of data into a smaller summary.

 

8

Descriptive Statistics Summary

50 total participants

26 males

24 females

Ages ranged from 25-40

Average age=32

Reading Test Scores ranged from 45-9

Average reading score=75.58

Math Test Scores ranged from 45 to 92

Average math score=75

Total Test Scores ranged from 95 to 186

Average total test score=150.78

Analytical data from a test group of 50 people was collected and studied. There were 26 males and 24 females in the test group. The participants were surveyed on age, sex, years of college experience, caffeine consumption, test prep, as well as math, reading and comprehensive test scores. This analysis focuses on descriptive statistics and uses the age, math score, reading score and total test score variables. The descriptive statistics demonstrate that the age of the participants ranges from 25 to 40 and the participants have an average age of 32. Math scores range from 45 to 92, and the average math score was 75. Reading scores range from 45 to 96 and the average reading score was 75.78. Total scores ranged from 95 to 186 and the average total score was 150.78.

9

Conclusion

 

References

Salkind, N. (2014). Statistics for people who think they hate statistics (5th ed.). Thousand Oaks, CA: Sage Publishing.

Psychology Paper

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Overview

In this course, you will write a paper that makes use of thefive general perspectives on human behavior – biological, learning, social and cultural, cognitive, and psychodynamic influences – to explain how a relationship begins, develops, and is maintained. Compare and contrast the impact of those perspectives on behavior in a love relationship – any relationship you choose – but one that shows a loving interaction between two people, such as spouses.

NOTE: Work completed for other courses is not acceptable for use in this class.

Format

 

You need at least 3 pages, double-spaced, for this.  Spelling and grammar count! Take time to organize your thoughts and develop a clear and coherent essay.