# SPSS Assignment Help

## Statistics using SPSS Assignment Help

SPSS is one of the most comprehensive and flexible tool for statistical analysis and data management. SPSS has range of applications in Statistics such as Descriptive Statistics, Hypothesis Testing, Regression Analysis, Factor Analysis etc.

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• Analysis of covariance
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• Kruskal-Wallis Test
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• Mann-Whitney U Test
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• Multiple regression
• Multivariate analysis of variance
• Negative Binomial Regression
• Non-parametric statistics
• One-way analysis of variance
• One-way MANOVA
• Ordinal Logistic Regression
• Paired-samples t-test
• Partial correlation
• Planned comparisons/Post-hoc analyses
• Poisson Regression
• Preliminary analyses
• Probit Regression
• Spearman’s Rank Order Correlation
• Standard multiple regression
• Summary of techniques covered in this chapter
• T-tests
• Two-way between-groups ANOVA
• Type 1 error, Type 2 error and power
• Wilcoxon Signed Rank Test
• Advanced Quantitative Methods
• Advanced Topics in State Space Models and Dynamic Factor Analysis
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• Propensity Score Analysis
• Rates and survival analysis: Poisson, Cox, and parametric survival models
• Regression and Model Building
• Regression Models for Categorical Dependent Variables using Stata
• Sample Size and Statistical Power
• Stata programming

DATA ANALYSIS USING SPSS

The questions asked in this assignment pertains to psychology topic in the statistics subject. The various types of questions that has been asked makes use of SPSS statistical package in arriving at the solution. Few questions that are asked over here are regarding analysis of research results which also involves use of SPSS package. Another chunk of  questions are involving analysis and interpretation of results that are obtained by making use of SPSS. SPSS assignment help offered by our experts is simply impeccable  so, students all across the globe rely on us for scoring top grades.

1.A psychology professor is trying to decide whether to use a textbook for next year’s stats class. To help him make the decision, the professor asks the current students what they prefer. The data is as follows:

 Book No book Men 8 22 Women 21 45

(Using SPSS) Do the data suggest that there is a gender difference in textbook preference?

 gender * textbook Crosstabulation % within gender textbook Total book no book gender men 27.6% 72.4% 100.0% women 32.8% 67.2% 100.0% Total 31.3% 68.8% 100.0%
 Chi-Square Tests Value df Asymptotic Significance (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square .260a 1 .610 Continuity Correctionb .073 1 .787 Likelihood Ratio .263 1 .608 Fisher’s Exact Test .811 .398 N of Valid Cases 96 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 9.06. b. Computed only for a 2×2 table

The association is not significant: χ2(1)=.26, p=.61>.05. We conclude that there is no gender difference in textbook preference.

2.For the following data, x represents a rating of a client’s satisfaction after the first therapy session (on a scale of 1-10). y represents the number of sessions attended by that client after three months.

 Client x : satisfaction y : # sessions A 7 16 B 5 2 C 6 1 D 3 2 E 4 9 F 10 12 G 3 4 H 10 11 I 6 8

A researcher hypothesizes that greater satisfaction after the first session will lead one to attend more sessions. (Using SPSS) Test this hypothesis, and interpret the result.

The relation looks linear, but with the case A having a high residual (it is far from the regression line). The correlation coefficient is positive and quite high (.06), but insignificant (p=.07>.05). Based on this coefficient, we conclude there is no relation between the two variables, and we cannot reject the null hypothesis. The satisfaction with the first session is not associated with attending more sessions. Even if we exclude case A, the coefficient is not significant (p=.054>.05), and we retain the null hypothesis.

 Correlations satisfaction satisfaction sessions Pearson Correlation 1 .628 Sig. (2-tailed) .070 N 9 9

3.In a study of embarrassment, Harris (2001) asked participants to sing the Star Spangled Banner in front of a video camera while she recorded their heart rate for two minutes.  The following are data similar to the research results.

Participant           Baseline        1 minute        2 minutes

1                                74                    88                    75

2                                76                    90                    77

3                                78                    89                    76

4                                76                    85                    76

(Using SPSS) Do the data show significant changes in heart rate over time?

 Paired Samples Test Paired Differences t Sig. (2-tailed) Mean Std. Deviation 95% Confidence Interval of the Difference Lower Upper Pair 1 baseline – 1 min -12.000 2.449 -15.898 -8.102 -9.798 .002 Pair 2 baseline – 2 min .000 1.414 -2.250 2.250 .000 1.000 Pair 3 1 min – 2 min 12.000 2.000 8.818 15.182 12.000 .001

A series of paired-samples t-tests show that the effect of embarrassment lasts only one minute. The t-test between baseline and values recorded after one minute is significant (t(3)=-9.7, p=.002<.05), indicating increases in heart rate. The same applies to the test between after 1 minute and after 2 minutes (t(3)=12.0, p=.001<.05), showing that heart rate decreases. Finally, there is no difference between initial and final heart reate: (t(3)=-0.0, p<.0005)

4.Research shows that similarity in attitudes, beliefs, and interests plays an important role in interpersonal attraction. Thus in theory, one’s attitude should be similar to that of his/her relationship partner. Suppose a researcher developed a questionnaire that measures how liberal or conservative one’s attitudes are. Low scores indicate that the person has liberal attitudes, whereas high scores indicate conservatism. The following hypothetical data are scores for married couples.

 Couple x :partner 1 y :partner 2 A 11 14 B 6 7 C 16 15 D 4 7 E 1 3 F 10 9 G 5 9 H 3 8

(Using SPSS) Test the hypothesis that partners’ attitudes are significantly related to each other.  Interpret the result.

 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 partner1 – partner2 -2.00000 -3.84250 -.15750 -2.567 7 .037

A paired-samples t-test was run (t(7)=-2.567, p=.037<.05). It proved to be significant, showing that there are differences between the two partners. However, when comparing across couples, it turns out that partner’s attitudes are correlated very strong: r=.904, p=.002<.05.

 Correlations partner1 partner2 partner1 Pearson Correlation 1 .904** Sig. (2-tailed) .002 N 8 8 partner2 Pearson Correlation .904** 1 Sig. (2-tailed) .002 N 8 8 **. Correlation is significant at the 0.01 level (2-tailed).

5.For the following data, x1 represents a rating of a client’s satisfaction after the first therapy session (on a scale of 1-10). x2 represents a rating of a client’s self-reported happiness before the first therapy session (on a scale of 1-12). y represents the number of sessions attended by that client after three months.

 Client x1 : satisfaction x2 :happiness y : # sessions A 7 12 16 B 5 6 2 C 6 6 1 D 3 5 2 E 4 8 9 F 10 6 12 G 3 3 4 H 10 3 11 I 6 7 8

A researcher hypothesizes that greater satisfaction after the first session, and less happiness before the first session, will each lead one to attend more sessions.  (Using SPSS) Test this hypothesis, and interpret the result.

A regression model is set up. It turns out being significant (F(2,6)=5.59, p=.043<.05), and explains 53% out of the variance in the number of sessions.

 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .807a .651 .534 3.592 a. Predictors: (Constant), happiness, satisfaction b. Dependent Variable: sessions
 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 144.161 2 72.081 5.588 .043b Residual 77.394 6 12.899 Total 221.556 8 a. Dependent Variable: sessions b. Predictors: (Constant), happiness, satisfaction

However, residuals are not normal distributed, being larger for higher values of the dependent variable.

The partial relations between happiness and number of sessions and between satisfaction and number of sessions look linear.

Also there is no collinearity between the independent variables (tolerance =.999).

 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Collinearity Statistics B Beta Lower Bound Upper Bound Tolerance VIF 1 (Constant) -6.141 -1.466 .193 -16.393 4.110 satisfaction 1.215 .611 2.530 .045 .040 2.390 .999 1.001 happiness .976 .506 2.096 .081 -.163 2.115 .999 1.001 a. Dependent Variable: sessions

Both satisfaction and happiness are positively related to number of sessions, but only satisfaction has a significant impact: one-point increase on the scale of satisfaction leads to an average of 1.2 sessions increase.

We conclude there is an effect from satisfaction, but not from happiness, with the caution that not al regression assumptions are fulfilled.

6.Suppose a survey of Psych 320 students finds the following data for the students’ favorite basketball team:

Lakers                        Clippers                     Warriors                     Kings

55                                 27                                 13                              1

(Using SPSS) Do the data indicate a significant preference among the four basketball teams?

We test against the null hypothesis that an equal number of students support each team. A chi-square test proves significant: there is a significant preference among the four basketball teams

7.Shrauger (1972) conducted an experiment that compared the effect of an audience on the performance of participants with high and low self-esteem. Hypothetical data from this experiment are as follows. The data represents the number of errors made by each participant (6 participants in each condition):

 Alone With audience Self-esteem High 3, 6, 2, 2, 4, 7 9, 4, 5, 8, 4, 6 Low 7, 7, 2, 6, 8, 6 10, 14, 11, 15, 11, 11

(Using SPSS) Is there 1) a main effect of self-esteem, 2) a main effect of alone/audience, and 3) an interaction between the two?

A MANOVA analysis reveals that there are significant effects form self-esteem (p<.005) and being with audience (p<.0005), as well as of their interaction (p=.028<..05).

 Tests of Between-Subjects Effects Dependent Variable:   performance Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 216.000a 3 72.000 16.744 .000 Intercept 1176.000 1 1176.000 273.488 .000 self_estime 96.000 1 96.000 22.326 .000 audience 96.000 1 96.000 22.326 .000 self_estime * audience 24.000 1 24.000 5.581 .028 Error 86.000 20 4.300 Total 1478.000 24 Corrected Total 302.000 23 a. R Squared = .715 (Adjusted R Squared = .673)

8.Suppose the low-self-esteem participants from Shrauger’s (1972) audience study were brought back in 2010 to repeat the same cognitive task.  The datastill represents the number of errors made by each participant (3participants in each condition).  This time, assume the same 3 participants experience each of the 4 conditions:

 Alone With audience Year 1972 8, 7, 2 12, 14, 11 2010 9, 6, 5 9, 6, 4

(Using SPSS) Is there 1) a main effect of year, 2) a main effect of alone/audience, and 3) an interaction between the two?

None of the effects is significant. Neither year, nor the interaction of year with audience make no effect within subjects. Audience makes no significant effect between subjects.

 Tests of Within-Subjects Effects Measure:   perfomance Source Type III Sum of Squares df Mean Square F Sig. year Sphericity Assumed .333 1 .333 .308 .609 Greenhouse-Geisser .333 1.000 .333 .308 .609 Huynh-Feldt .333 1.000 .333 .308 .609 Lower-bound .333 1.000 .333 .308 .609 year * audience Sphericity Assumed 1.333 1 1.333 1.231 .329 Greenhouse-Geisser 1.333 1.000 1.333 1.231 .329 Huynh-Feldt 1.333 1.000 1.333 1.231 .329 Lower-bound 1.333 1.000 1.333 1.231 .329 Error(year) Sphericity Assumed 4.333 4 1.083 Greenhouse-Geisser 4.333 4.000 1.083 Huynh-Feldt 4.333 4.000 1.083 Lower-bound 4.333 4.000 1.083
 Tests of Between-Subjects Effects Measure:   perfomance Transformed Variable:   Average Source Type III Sum of Squares df Mean Square F Sig. Intercept 481.333 1 481.333 41.554 .003 audience .333 1 .333 .029 .874 Error 46.333 4 11.583

9.A psychologist would like to examine how the rate of presentation affects people’s ability to memorize a list of words. A list of 20 words is prepared. For one group of participants the list is presented at the rate of one word every half second. The next group gets one word every second. The third group gets one word every two seconds, and the fourth group gets one word every three seconds. After the list is presented, the psychologist asks each person to recall the entire list. The dependent variable is the number of errors in recall. The data from this experiment are as follows:

Half second                    1 second                    2 seconds                  3 seconds

4                                        0                                  3                                  0

6                                        2                                  1                                  2

2                                        2                                  2                                  1

(Using SPSS) Can the psychologist conclude that the rate of presentation has a significant effect on memory?

A one-Way ANOVA was set up, with the number of errors as dependent variable, and the time interval as independent variable. The model is not significant (F(3,8)=2.96, p=.098>.05), therefore we retain the null hypothesis of non-differentiation, and conclude that the rate of presentation has no significant effect on memory.

 ANOVA errors Sum of Squares df Mean Square F Sig. Between Groups 16.250 3 5.417 2.955 .098 Within Groups 14.667 8 1.833 Total 30.917 11

10.(Using SPSS)Suppose a sample of n = 36 freshmen is selected to participate in a new 4-hour training program designed to improve study skills. To evaluate the effectiveness of the new program, the sample was compared with the rest of the freshman class.  The mean score on the final exam for the entire freshman class was µ = 74, while scores for the students in the new program are shown below.  Based on this data, can we say the training program worked?

Here are the test scores for the 36 students:

79, 56, 45, 100, 74, 74, 83, 82, 97, 75, 89, 87, 51, 99, 42, 85, 95, 88, 100, 58, 73, 98, 59, 65, 74, 85, 100, 87, 98, 56, 99, 45, 100, 80, 100, 79

 One-Sample Statistics N Mean Std. Deviation Std. Error Mean score 36 79.3611 18.04463 3.00744
 One-Sample Test Test Value = 74 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper score 1.783 35 .083 5.36111 -.7443 11.4665

The mean score in the sample is 79.4 (SD=18.04). An independent sample t-test was used to test the difference from the mean of the entire freshman class ( µ = 74 ). The test was not significant: t(35)=1.783, p=.083>.05. We conclude that the training program did not worked.