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# Workshop on SPSS: Basic to Intermediate Level

A workshop on SPSS: Basic to Intermediate Level in Kuching on 16-17 May 2015 hosted by Sarawak Research Society

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### Workshop on SPSS: Basic to Intermediate Level

1. 1. SPSS: Basic to Intermediate Hiram Ting & Ernest Cyril de Run 16-17 May 2015, Kuching Organized by Sarawak Research Society
2. 2. Acknowledgement Gratitude to Prof Ernest Cyril de Run and Prof Thurasamy Ramayah for providing useful information during the preparation of the workshop slides.
3. 3. Content Installation of SPSS Introduction to SPSS Understanding of Analysis Preliminary Decision Data Entry Data Cleaning Frequency Cross-tabulation Normality Test Reliability Test Validity Test Handling Qualitative Data Test of Independence Test for Goodness of Fit Test of Difference • T-test • ANOVA Test of Relationship • Pearson Correlation • Linear Regressions • Multiple Regressions Factor Analysis Presentation of Findings Syntax
4. 4. Preparation • Install SPSS. • Download workshop materials folder. • Open SPSS Workshop 16-17 May 2015 file in the folder. • Open SPSS to check whether it works as a full version.
5. 5. Preparation Hands-on Exercise • Install SPSS (set-up) • Click ‘OK’ for every step. • Copy and paste license number, or • Copy and paste crack files in your program folder.
6. 6. Introduction to SPSS What is SPSS? • Statistical Package for the Social Sciences (SPSS) is a widely used program for statistical analysis in social sciences. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners and others. It is regarded as the first generation technique.
7. 7. Introduction to SPSS What is SPSS? Statistics included in the software: • Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics. • Bivariate statistics: Means, t-test, ANOVA, Correlation. • Prediction for numerical outcomes: Linear regression. • Prediction for identifying groups: Factor analysis, cluster analysis, Discriminant analysis. • Non-parametric tests and others.
8. 8. When is SPSS Useful SPSS is useful for: • Data entry • Data cleaning • Descriptive analysis and output • Parametric and non-parametric test – tests of relationship and difference • Data division based on factors and groups • Quantitative research with observed variables • Qualitative research with coded themes
9. 9. Understanding of Analysis
10. 10. Understanding of Analysis Before using SPSS, it is important to understand some of the fundamental things in research and data analysis techniques. • Types of data • Levels of measurement • Types of variable • Key terms in research • Types of analysis • Missing values
11. 11. Understanding of Analysis Types of data • Numeric • String (Categorical) Levels of measurement • Nominal • Ordinal • Interval • Ratio • Continuous
12. 12. Understanding of Analysis
13. 13. Understanding of Analysis Types of variable • Independent • Dependent • Moderating • Mediating • Control • Endogenous, exogenous
14. 14. Understanding of Analysis A B C D G F E J1 J2 J3 J4 H I
15. 15. Understanding of Analysis Key terms in research • Theory • Concept • Construct • Variable, item/indicator • Model, framework • Operational definition
16. 16. Understanding of Analysis • A theory of systematically interrelated concepts, definitions, and propositions that are advanced to explain and predict phenomena (facts). • A model is defined as a representation of a system that is constructed to study some aspect of the system or the system as a whole. • Theory’s role is explanation whereas a model’s role is representation. • While theoretical framework is the theory on which the study is based, conceptual framework is the operationalization of the theory. It is the researcher’s own position on the problem and gives direction to the study.
17. 17. Understanding of Analysis • A concept is a generally accepted collection of meanings or characteristics associated with certain events, objects, conditions, situations and behavior. • A construct is an image or abstract idea specifically invented for a given research and/or theory building purpose. • A variable can be defined as any aspect of a theory that can vary or change as part of the interaction within the theory. • An operational definition is a definition stated in terms of specific criteria for testing or measurement. Their characteristics and how they are to be observed must be specified.
18. 18. Understanding of Analysis
19. 19. Understanding of Analysis Types of Analysis • Parametric  Normal distribution is assumed • Non-parametric  Distribution free • Types of variables involved  Univariate, bivariate, multivariate
20. 20. Understanding of Analysis Handling blank responses/missing values • Initial screening  If the whole page is missing, discard the questionnaire.  If the whole section is missing, discard the questionnaire.  If important responses are missing (e.g. key questions using single item), discard the questionnaire.  If straight-lining or answering pattern is found, discard the questionnaire.
21. 21. Understanding of Analysis Handling blank responses/missing values • Data cleaning  If > 25% missing, remove the observation. Hair et al. (2014) advocate for less than15%.  Using the midpoint of the scale.  Replacing blank responses with a value.  Mean of those responding or respondents.  Using Expected Maximization (EM).
22. 22. Preliminary Decision Instrument design • Levels of measurement • Types of scale • Single or multiple items • Positive or negative worded statements • Structured, semi-structured or unstructured • Wordings (e.g. double negatives, double-barrelled, culture-specific terms, long complex questions)
23. 23. Preliminary Decision Distribution and collection of data  Sampling technique  Paper questionnaire, mail or online  Interview or self-administered questionnaire  Response rate: distributed, collected and usable copies
24. 24. Preliminary Decision In any report the first thing that is normally reported is the response rates. When the response rate is low it raises question about the representativeness of the sample. Another reason is the problem of non response. Would the responses of those who have not responded be different form those who responded?
25. 25. Preliminary Decision Data analysis and interpretation  Confidence level  Significant level  One-tailed or two-tailed  Types of analytical method  Hypothesis development and testing
26. 26. Preliminary Decision Addressing Errors  Random sampling errors  Systematic errors/non-sampling errors • Administrative errors: sample selection, administrator, data processing • Respondent errors: non-response, response bias - deliberate falsification, unconscious misinterpretation  Common method variance and social desirability
27. 27. Preliminary Decision Pre-test  The purpose is to ensure instrument is well-designed, hence the statements/questions would be understood and responded to the manner which they were developed for.  Using pilot study.  Using debriefing or protocol method.  Issue with sample size.
28. 28. Data Entry An Overview • SPSS Data Editor • SPSS Viewer (Output) • Variable View Includes Name, Type, Width, Decimals, Label, Values, Missing, Columns, Align, Measure • Data View
29. 29. Data Entry
30. 30. Data Entry Rules for naming of variables • Variable names: • Must be unique (i.e. each variable must have a different name) • Must begin with a letter (not a number) • Cannot include full stops, spaces or symbols (! , ? * “) • Cannot include words used as commands by SPSS (all, ne, eq, to, le, lt, by, or, gt, and, not, ge, with) • Cannot exceed 64 characters.
31. 31. Data Entry Hands-on Exercise • Open SPSS. • Open Questionnaire Sample. • Begin with ‘Variable View’, fill up the first row with information provided in Data Entry Exercise. • Continue with the second and third rows. • Continue with the fourth to sixth rows. • Move to ‘Data View’, fill up the blanks with responses of five respondents.
32. 32. Data Cleaning Hands-on Exercise • Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Frequencies’. • Move every variable from left column to right column, click ‘OK’. • Read the output and check. • Addressing missing values using EM.
33. 33. Useful Features Hands-on Exercise • Sort the data file Go to ‘Data’, click ‘Sort Cases’, choose ‘Ascending’ or ‘Descending’ • Split the data file Go to ‘Data’, click ‘Split File’ and ‘Compare Group’ • Select cases Go to ‘Data’, click ‘Select Cases’, ‘If Condition is Satisfied’ and ‘If’. For example, GEN = 1 to select only male respondents
34. 34. Useful Features
35. 35. Data Transformation Reason for transformation  to improve interpretation and compatibility with other data sets  to enhance symmetry and stabilize spread  improve linear relationship between the variables (Standardized score)
36. 36. Data Transformation Hands-on Exercise • Recode  The purpose is to redefine categories of data.  Go to ‘Transform’, click ‘Recode into Different Variables’.
37. 37. Data Transformation Hands-on Exercise • Compute  The purpose is to create a new variable.  Go to ‘Transform’, click ‘Compute Variable’.
38. 38. Descriptive Analysis • The purpose is to describe the distribution of the variable of interest. • It includes Frequencies and Cross-tabulation for nominal or categorical data, and Descriptives (Mean and Standard Deviation) for continuous data.
39. 39. Frequencies • The purpose is to provide frequency counts. It is useful in presenting respondents profile and categorical findings. Hands-on Exercise • Open Data Analysis Exercise • Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Frequencies’. • Splitting dataset is useful when presenting findings based on categories in separation. Go to ‘Data’, click ‘Split File’
40. 40. SAMPLE
41. 41. Cross-tabulation • The purpose is a joint frequency distribution of cases based on two or more categorical variables. • Chi-square will be explained in later slides. Hands-on Exercise • Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Crosstabs’. Select the variables on for ‘Row’ and ‘Column’. In ‘Cell’, click ‘Percentages’.
42. 42. Cross-tabulation
43. 43. Descriptives • The purpose is to provide statistical summary of descriptive findings. • ‘Kurtosis’ and ‘Skewness’ are useful to assess data distribution. Hands-on Exercise • Go to ‘Analyze’, click ‘Descriptive Statistics’ and ‘Descriptives’. • Click ‘Option’, check ‘Mean’ and ‘Std. Deviation’.
44. 44. Descriptives
45. 45. SAMPLE
46. 46. Normality Test • Parametric test assumes data is normally distributed. • Assessing normality using Q-Q Plots. • Hands-on Exercise: Go to ‘Analyze’ and click Q-Q Plots. • Assessing normality using Explore. • Hands-on Exercise: Go to ‘Analyze’, and click ‘Explore’. • Assessing outliers using Scatterplot. • Hands-on Exercise: Go to ‘Graphs’, and click ‘Legacy Dialogs’ and ‘Scatter/Dot’.
47. 47. Normality Test • Skewness value provides an indication of the symmetry of the distribution. Kurtosis, on the other hand, provides information about the ‘peakedness’ of the distribution. • If the distribution is perfectly normal, you would obtain a skewness and kurtosis value of 0 (rather an uncommon occurrence in the social sciences). • With reasonably large samples, skewness will not ‘make a substantive difference in the analysis’ (Tabachnick & Fidell 2007, p. 80). Kurtosis can result in an underestimate of the variance, but this risk is also reduced with a large sample (200+ cases: see Tabachnick & Fidell 2007, p. 80).
48. 48. Normality Test
49. 49. Normality Test General guideline • From 5% Trimmed Mean, compare the original mean and the new trimmed mean to assess whether extreme scores are having a strong influence on the mean. • The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). • For sample more than 100, use Kolmogorov-Smirnoff test; for sample less than 100, use Shapiro Wilk test. A non-significant result (Sig. value of more than .05) indicates normality. • Shape of histogram, Q-Q plots and boxplot. • Outliers appear as little circles with a number attached. Outliers are cases with scores that are quite different from the remainder of the sample, either much higher or much lower.
50. 50. Normality Test
51. 51. Goodness of Measures Reliability and Validity
52. 52. Goodness of Measures Reliability and Validity
53. 53. Goodness of Measures Reliability • Typically, in any research we use a number of questions (sometimes) referred to as items to measure a particular variable. • Cronbach's Alpha is a measure of how well each individual item in a scale correlates with the sum of the remaining items. It measures consistency among individual items in a scale. • Reliability refers to the degree of consistency, as Kerlinger (1986) puts it; if a scale possesses a high reliability the scale is homogeneous. According to Nunnally (1978) alpha values equal to or greater than 0.70 are considered to be a sufficient condition. Thus, it can be concluded that these measures possess sufficient reliability.
54. 54. Goodness of Measures • Go to ‘Analyze’, click ‘Scale’ and ‘Reliability Analysis’.
55. 55. Goodness of Measures Several types of validity • Content/Face validity • Convergent validity • Discriminant validity • Criterion-related validity
56. 56. SAMPLE
57. 57. Goodness of Measures Content validity • Content validity refers to the extent to which an instrument covers the meanings included in the concept (Babbie, 1992). Researchers, rather than by statistical testing, subjectively judge content validity (Chow and Lui, 2001). The content validity of the proposed instrument is at least sufficient because the instrument is carefully refined from a proven instrument with an exhaustive literature review process (Chow and Lui, 2001). This can also be tested during the pre-test by using subjects who are qualified (academicians and practitioners) to rate whether the content of each factor was well represented by the measurement items (Saraph et al., 1989). As Nunnally (1967) put it content validity depends on how well the researchers created measurement items to cover the domain of the variable being measured.
58. 58. Goodness of Measures Convergent validity • According to Campbell and Fiske (1959) convergent validity refers to all items measuring a construct actually loading on a single construct. • The criteria used by Igbaria et al., 1995 to identify and interpret factors were: each item should load 0.50 or greater on one factor and 0.35 or lower on the other factor. • These results confirm that each of these constructs is unidimensional and factorially distinct and that all items used to measure a particular construct loaded on a single factor.
59. 59. Goodness of Measures
60. 60. Goodness of Measures Discriminant validity • Discriminant validity refers to the extent to which measures of 2 different constructs are relatively distinctive, that their correlation values were neither an absolute value of 0 nor 1 (Campbell and Fiske, 1959). Correlation analysis is used. If all the factors are not perfectly correlated where their correlation coefficients range between 0 or 1, we can conclude that discriminant validity has been established.
61. 61. Goodness of Measures Criterion-related validity • Criterion related validity refers to the extent to which the factors measured are related to pre-specified criteria (Saraph et al., 1986). This is also called as nomological validity or external validity. We can also do this by running a multiple regression analysis and looking at the Multiple R value (correlation coefficient), the values we are looking for are any values higher that 0.5.
62. 62. Handling Qualitative Data
63. 63. Handling Qualitative Data • If data is collected using qualitative methods, such as interview and focus group, coding process is required to quantify the themes before using SPSS to perform any analysis.
64. 64. Handling Qualitative Data
65. 65. Handling Qualitative Data Example: Beliefs about the use of Instagram 1. I enjoy using Instagram coz it is fun. 2. Taking and uploading pictures are what attracts me. 3. When I am bored, I play Instagram. 4. I am enthralled by its ease of use. 5. I find hashtag a useful function of Instagram. 6. It is easy to use, even my little brother is using it. How many theme(s) can you identify?
66. 66. Test of Independence • Chi-square test is used when you wish to explore the relationship between 2 categorical variables with each having 2 or more categories. • It is also a statistical method assessing the goodness of fit between a set of observed values and those expected hypothetically. It is used when the parameter to be tested is proportion and there is no assumption of normality. • If the level of significance is set at 0.05, then p-value of less than 0.05 means rejection of null hypothesis.
67. 67. Test of Independence Chi square test for independence • Example: Is the proportion of male employees with high intention to share information the same as the proportion of female with high intention to share information?  For Gender, we have (1= Male/2=Female) whereas for Level, we have(1=Low/2=High)  As such, we will have a (2 X 2) contingency table
68. 68. Test of Independence
69. 69. Test of Independence Chi square test for goodness of fit • Example: A researcher would like to test the association between cigarette smoking and lung cancer. After randomly selecting smokers, it is found that 25 out of 65 heavy smokers are at high risk of developing lung cancer while for light smokers the figure is 20 out of 124. • Ho: There is no association Ha: There is association • Findings: X2 = 11.66; p-value = 0.0016 • Decision: Reject null hypothesis • Conclusion: Smoking and lung cancer risk are associated
70. 70. SAMPLE
71. 71. Test of Independence Hands-on Exercise Test for Independence/Relatedness • Go to ‘Analyze’, click ‘Cross-tabulation’. Test for Goodness of Fit • Go to ‘Analyze’ , click ‘Non-parametric Test’ and ‘Dialog Legacy’.
72. 72. Test of Difference
73. 73. Test of Difference • Parametric Techniques: t-test; paired t-test, one-way ANOVA, two- way ANOVA • Non-Parametric Techniques: Mann-Whitney/Wilcoxon rank sum test; Wilcoxon signed rank sum test, Kruskal Wallis; Friedman test
74. 74. Test of Difference Independent Sample T-test • Comparing two populations/groups using Mean Paired Samples T-test • Comparing the Mean of two related populations/groups One-way ANOVA • Comparing the Mean of more than two populations/groups Hands-on Exercise • Go to ‘Analyze’, click ‘Compare Means’.
75. 75. SAMPLE
76. 76. Test of Difference How to interpret the findings.  For Independent t-test, if the Levene test is significant (Sig. value is less than 0.05), this indicates the variance of the two samples is significantly different.  For Paired t-test, the two variables correlate if Sig. value is less than 0.05.  If the t-test is significant (Sig. 2-tailed value is less than 0.05), this indicates the two samples are significantly different in the variable under investigation.
77. 77. Test of Difference • Example: A one-way between-group ANOVA was conducted to test whether intention to share differed by level of education.
78. 78. Test of Difference
79. 79. Test of Difference
80. 80. Test of Difference
81. 81. Test of Difference How to interpret the findings. • There was a statistically significant differences at the p< 0.05 level in intention scores for the 4 educational levels [F(3,188) = 2.728, p=0.045]. Despite reaching statistical significance, the actual difference in mean scores between the groups was quite small. The effect size, calculated using the eta squared, was 0.04. Post-hoc comparison using the Duncan’s range test indicated that the mean score for Masters (M=3.51, SD=0.92) and First degree (M=3.82, SD=0.62) was statistically different from PhD (M=4.40, SD=0.46). Those with Diploma education (M=3.89, SD=0.47) did not differ statistically from the PhD group.
82. 82. SAMPLE
83. 83. Test of Relationship Correlation • Correlation is used to denote association between two quantitative variables, assuming that the association is linear. It provides information about the strength and direction of relationship. • Strength: 0.10-0.29 (small), 0.30-0.49 (medium), 0.50 (large) Hands-on Exercise • Go to ‘Analyze’, click ‘Correlate’ and ‘Bivariate’. • Select ‘Pearson’ (for continuous data) and ‘One-tailed’. • If the Sig. value is less than 0.05, then the two variables are significantly correlated. Only then the strength and direction of relationship are looked at.
84. 84. Test of Relationship How to present the findings. • There was a strong positive correlation between intention to share and actual sharing [r=0.76, n=192, p<0.01] with high levels of intention associated with high levels of actual sharing.
85. 85. SAMPLE
86. 86. Test of Relationship Regressions • Simple linear regression is used when we would like to see the impact of a single independent variable on a dependent variable. • Multiple linear regression is used when we would like to see the impact of more than one independent variable on a dependent variable. • Multiple regression analysis is a statistical technique that can be used to analyze the relationship between a single dependent variable (continuous) and several independent variables (continuous or even nominal). In the case of nominal independent variables, dummy variables are introduced.
87. 87. Test of Relationship • In standard multiple regression, all of the independent variables are entered into the regression equation at the same time • Multiple R and R² measure the strength of the relationship between the set of independent variables and the dependent variable. An F test is used to determine if the relationship can be generalized to the population represented by the sample. • A t-test is used to evaluate the individual relationship between each independent variable and the dependent variable.
88. 88. Test of Relationship Things to consider: • Strong Theory (conceptual or theoretical) • Measurement Error The degree to which the variable is an accurate and consistent measure of the concept being studied. If the error is high than even the best predictors may not be able to achieve sufficient predictive accuracy. • Specification error Inclusion of irrelevant variables or the omission of relevant variables from the set of independent variables.
89. 89. Test of Relationship Assumptions • Normality One of the basic assumptions is the normality which can be assessed by plotting the histogram. If the histogram shows not much deviation then we can assume the data follows a normal distribution.
90. 90. Test of Relationship • Normality of the error terms The second assumption is that the error term must be normally distributed. This can be assessed by looking at the normal P-P plot. The idea is that the points should be as close as possible to the diagonal line. If they are then we can assume that the error terms are normally distributed.
91. 91. Test of Relationship • Linearity The third assumption is the relationship between the independent variables and the dependent variable must be linear. This is assessed by looking at the partial plots. The idea is to see if we can draw a straight line on the scatter plot that is generated.
92. 92. Test of Relationship • Constant Variance – Homoscedasticity The fourth assumption is that the variance must be constant (Homoscedasticity) as opposed to not constant (Heterosedasciticity). Heterosedasciticity is generally observed when we see a consistent pattern when we plot the studentized residual (SRESID) against the predicted value of Y (ZPRED).
93. 93. Test of Relationship • Multicollinearity The fifth assumption is the collinearity problem. This is a problem when the independent variables are highly correlated among one another, generally at r > 0.8 to 0.9 which is termed as multicollinearoty. To assess this assumption we will look at two indicators. The first one is the VIF and tolerance. A low tolerance value of < 0.1 will result in a VIF value of > 10 as VIF is actually 1/Tolerance. If the value is more than 10 we can suspect there is a problem of multicollinearity.
94. 94. Test of Relationship • Multicollinearity The second value that we should look at is the conditional index. If this value exceeds 30 we can also suspect the presence of multicollinearity. When the value is more than 30 we should also look across the variance proportions and see if we can spot any 2 or more variables with a value of 0.9 and above excluding the constant. If there are 2 or more than only we can conclude there is multicollinearity.
95. 95. Test of Relationship • Independence of the error term - Autocorrelation This is an assumption that is particularly a problem with time series data and not for cross sectional data. We assume that each predicted value is independent, which means that the predicted value is not related to any other prediction; that is, they are not sequenced by any variable such as time. This can be assessed by looking at the Durbin Watson value. If the D-W value is between 1.5 – 2.5 then we can assume there is no problem.
96. 96. Test of Relationship • Outliers These are values which are extremely large and influential that they can influence the results of the regression. Usually the threshold is set at ± 3 standard deviations. Although this is the default some researchers may set a threshold of ± 2.5 to get better predictive power. This assumption can be easily identified by looking at whether there are casewise diagnostics.
97. 97. SAMPLE
98. 98. Test of Relationship Hands-on Exercise • Go to ‘Analyze’, click ‘Regression’ and ‘Linear’ Descriptive Statistics 3.15 2.653 113 2.12 1.084 113 2.90 1.575 113 HOW OFTEN R ATTENDS RELIGIOUS SERVICES STRENGTH OF AFFILIATION HOW OFTEN DOES R PRAY Mean Std. Dev iation N The minimum ratio of valid cases to independent variables for multiple regression is 5 to 1. With 113 valid cases and 2 independent variables, the ratio for this analysis is 56.5 to 1, which satisfies the minimum requirement. Different authors tend to give different guidelines concerning the number of cases required for multiple regression. Stevens (1996, p. 72) recommends that ‘for social science research, about 15 participants per predictor are needed for a reliable equation’.
99. 99. Test of Relationship ANOVAb 374.757 2 187.379 49.824 .000a 413.685 110 3.761 788.442 112 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), HOW OFTEN DOES R PRAY, STRENGTH OF AFFILIATIONa. Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESb. The probability of the F statistic (49.824) for the overall regression relationship is <0.001, less than or equal to the level of significance of 0.05. We reject the null hypothesis that there is no relationship between the set of independent variables and the dependent variable (R² = 0). We support the research hypothesis that there is a statistically significant relationship between the set of independent variables and the dependent variable.
100. 100. Test of Relationship Model Summary .689a .475 .466 1.939 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Predictors: (Constant), HOW OFTEN DOES R PRAY, STRENGTH OF AFFILIATION a. Look in the Model Summary box and check the value given under the heading R Square. This tells you how much of the variance in the dependent variable is explained by the model. The rule of thumb: a correlation less than or equal to 0.20 is characterized as very weak; greater than 0.20 and less than or equal to 0.40 is weak; greater than 0.40 and less than or equal to 0.60 is moderate; greater than 0.60 and less than or equal to 0.80 is strong; and greater than 0.80 is very strong. You will notice an Adjusted R Square value in the output. When a small sample is involved, the R square value in the sample tends to be a rather optimistic overestimation of the true value in the population (see Tabachnick & Fidell 2007). The Adjusted R square statistic ‘corrects’ this value to provide a better estimate of the true population value.
101. 101. Test of Relationship Coefficientsa 7.167 .442 16.206 .000 -1.138 .194 -.465 -5.857 .000 -.554 .134 -.329 -4.145 .000 (Constant) STRENGTH OF AFFILIATION HOW OFTEN DOES R PRAY Model 1 B Std. Error Unstandardized Coeff icients Beta Standardized Coeff icients t Sig. Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESa. For the independent variable strength of affiliation, the probability of the t statistic (-5.857) for the b coefficient is <0.001 which is less than or equal to the level of significance of 0.05. We reject the null hypothesis that the slope associated with strength of affiliation is equal to zero (b = 0) and conclude that there is a statistically significant relationship between strength of affiliation and frequency of attendance at religious services.
102. 102. Test of Relationship Coefficientsa 7.167 .442 16.206 .000 -1.138 .194 -.465 -5.857 .000 -.554 .134 -.329 -4.145 .000 (Constant) STRENGTH OF AFFILIATION HOW OFTEN DOES R PRAY Model 1 B Std. Error Unstandardized Coeff icients Beta Standardized Coeff icients t Sig. Dependent Variable: HOW OFTEN R ATTENDS RELIGIOUS SERVICESa. The beta coefficient associated with strength of affiliation is negative, indicating an inverse relationship in which higher numeric values for strength of affiliation are associated with lower numeric values for frequency of attendance at religious services. To compare the different variables it is important that you look at the standardised coefficients, not the unstandardised ones. ‘Standardised’ means that these values for each of the different variables have been converted to the same scale so that you can compare them. If you were interested in constructing a regression equation, you would use the unstandardised coefficient values listed as B.
103. 103. Factor Analysis • The purpose is to define the underlying structure in a data matrix; analyze the structure of interrelationships among a large number of variables by defining a set of common underlying dimensions called factors. • Factor analysis in SPSS is exploratory in function. The analysis is driven by data, rather than theory. • Sample size: preferably >100 cases or the ratio of 20:1 (case/variable).
104. 104. Factor Analysis • Important decisions includes:  Correlation matrix: KMO and Barlett’s test, Anti-image  Methods of extracting factors: Principal component  Latent root/eigenvalues criterion (>1)  Apriori criterion on number to be extracted  Percentage of variance explained (>50)  Rotation: Varimax, Promax  Loading significance (> 0.3 if 350 cases, > 0.5 if 120)
105. 105. Factor Analysis
106. 106. SAMPLE
107. 107. Factor Analysis Hands-on Exercise • Go to ‘Analyze’, click ‘Data Reduction’ and ‘Factor’.
108. 108. Using Syntax • Syntax in SPSS is the program language. • If you need to repeat your analysis, you can save the command language in a ‘Syntax’ file so that you can run an analysis at a later date or to repeat various analyses. • Whenever you run an analysis, you will notice that there is a Paste button. When you click on the paste button, a syntax file will open with the syntax for the analysis that you intended to do. Hands-on Exercise • Go to ‘File’, click ‘New/Open’ and ‘Syntax’.
109. 109. Using Syntax
110. 110. Thank You
111. 111. Thank You Next workshop 22 May : Advanced PLS-SEM 23-24 May : Advanced SPSS, Process Join us at Sarawak Research Society on
112. 112. Thank You Hiram Ting, PhD Email: hiramparousia@gmail.com Ernest Cyril de Run, PhD Email: drernest@feb.unimas.my

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A workshop on SPSS: Basic to Intermediate Level in Kuching on 16-17 May 2015 hosted by Sarawak Research Society

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