Scientific Work and Empirical Research / Methods in Empirical Communication Research

Transcription

1 Scientific Work and Empirical Research / Methods in Empirical Communication Research Prof. Dr. Jens Wolling (Participation in the seminar is voluntary!) (Writing the term paper not!)

2 Trends in Media and Communication Science Start today at (2.30 p.m.) Last session on Friday at 14:30 (2.30 p.m.)

3 Files on your computer SPSS Data File: This Powerpoint File: Excel file: Residens_123basis_Seminar.sav Data Analysis.pptx Questionnaire 3 Waves.xlsx Files you have to create and submit: SPSS Syntax File: Term paper: Lastname_Firstname.sps Lastname_Firstname.pdf

4 What you already should know: What is (empirical) research! The difference between qualitative and quantitative research! Methods of data gathering used in communication research and how to employ them! The difference between experimental and correlational research designs! Define a basic population and sampling methods! Challenges for research: Validity, reliability, reactivity How to organize empirical measurements and build a data set! What you will learn or repeat: Modifying and analyzing data with SPSS to answer research questions. Building an annotated syntax file (sps file)

5 What s the aim of the game? What? Everybody has to write a paper (about 5 6 pages) on his own. Answer the research question that you have received Answer must be based on statistical analysis with SPSS using the provided data Obligatory: crosstab, t test, correlation, (and data modification) Advanced methods are required to obtain an excellent grade (better than good) Why? Preparation for your work in the research seminars The lecturer is interested in the results When? Papers (pdf) must be send via until 5th of November (12 p.m.) (no prolongation!) Results will be published not later then the 19th of November.

6 Parts of the term paper Front page with all relevant information No table of contents but 4 parts: 1. Theoretical background (1 page) Relevance of the research question Stating hypotheses and explaining why they are meaningful 2. Operationalization (1 page) Which survey questions are used to operationalize the concepts? How are the variables modified? (recoding, index construction) 3. Findings (2 3 pages) Descriptive results and analytic results. Tables and/or charts and verbal descriptions of the main findings. 4. Interpretation (1 page) Are the hypotheses falsified? (Why?) What could have been done better? What should be the next steps in these research field? Appendix Annotated SPSS Syntax File (included in the pdf and also as sps file)

7 For whom? Imagine you write the paper for someone, who had never heard about the project and doesn t know anything about it (this is the situation when your are writing for a scientific journal) Give details about the project (who was asked, when ) Give details about the variables: * Wording of the questions and the answers * Details how you recoded the variables (not the commands, but for example which values were put together) Don t use the variable names in the paper. They are just for your internal use and not for publication! Write in a scientific style but don t forget that you should try to make your paper as interesting as possible. Imagine you want to convince an undecided reader to read the whole paper.

8 Every lecture consists of 3 parts 1. Presentation of the methods of data modifying and analyzing: The SMART METER example 2. Repetition and exercise: The MEDIA USE example 3. Working on your own project: Answering your research question (at the beginning the first part will be longer, at the end of the seminar, the first part will be shorter)

9 Layout Font: Times / Arial Font Size: 12 Line Spacing: at least 1,3 Margin: at least 2,5 cm

10 How to work on your own project Start with your research question! Theory based Make an theoretical analysis of the concepts used in the question! Develop a theory how the question can be answered! (What is a theory?) Adjust your research question if necessary! (Inform the lecturer!!!) As you are running a secondary data analysis, you have to do this at the same time: Data based Make yourself familiar with the data set! Check if you can identify the variables you need! Let the variables in the data set inspire you for additional hypotheses!

11 Eight days a week Monday Tuesday Wednesday Thursday Friday Saturday Monday Measurement (basics) Variables & Values Scales Research Question Hypotheses Dependent / Independent Variable Basic population & weighting of data Split ballot Variance/SD Effect size Explained variance Measurement (multiple indicators) Causality using cross sectional data Causality using longitudinal (panel) data The data set: Panel Survey Significance Description & modifying Frequencies Descriptives Mean Recode Compute Combining distributions Crosstabs Chi Square Count If Comparing means T test Relationship between variables Correlations Identifying dimensions Factor analysis Scale reliability Causal Relationship Regression Analysis I Causal Relationship Regression Analysis II

12 The Data Set: A Panel Survey Method Telephone Survey Instrument: Standardized questionnaire Design: Population: Sampling: Panel design: 3 waves Persons 18 years and older, living in Private Households in Thuringia 2 stage random process (RLD & Next birthday) Questionnaire 3 Waves.xlsx Residens_123basis_Seminar.sav

13 The questionnaire: A Synopsis of the three waves!

14 The Data Set: A Panel Survey No. of respondents n= 530 n= 554 n= 551 Variable names Vname_1 Vname_2 Vname_3 How to identify the cases of the waves welle_gr_1 welle_gr_2 welle_gr_3 1 just first wave 12 first and second wave How many percent of the survey participants in 2011 were asked for the first, the second and the third time?

15 Never ever copy a SPSS Output in your Paper!!

16 1 just first wave 12 first and second wave = 530 Asked for the first time 530 Asked for second time = = = = = 200 Asked for third time 183

17 Missing data! 1. People, who did not participate in a certain wave. 2. People who participated in the wave, but were not asked a certain question. Because of split ballot Because of their earlier answers to other questions 3. People who were asked, but didn t gave an answer to a certain question

18 Research Questions for the Presentation How can peoples willingness to install a smart meter and to pay for the equipment be explained? Do communication / media related variables matter? Research Question for the Exercise How can peoples online use be explained? Does the use of traditional media matter?

19 10. How can peoples attitudes towards the different priorities concerning energy policy be explained? 11. How can peoples perceptions of the preferences of the German government towards the different priorities concerning energy policy be explained? 12. How can peoples perceptions of the preferences of the Thuringian government towards the different priorities concerning energy policy be explained? 13. How can peoples perceptions of the preferences of the Thuringian citizens towards the different priorities concerning energy policy be explained? 20. How can peoples attitudes towards the risks of nuclear power be explained? 21. How can peoples attitudes towards the proposed run time extension of nuclear power stations be explained? 22. How can peoples attitudes towards the proposed nuclear phase out be explained? 30. How can peoples attitudes towards renewable energies be explained? 31. How can peoples knowledge about renewable energies be explained? 32. How can peoples knowledge about the objectives of the energy turn be explained? 33. How can peoples knowledge about the activities concerning the energy turn be explained? 34. How can peoples willingness to pay for the energy turn be explained? 35. How can peoples perceived personal knowledge concerning laws and regulations in the area of energy saving be explained? 36. How can peoples objective knowledge concerning laws and regulations in the area of energy saving be explained? 40. How can peoples behavioral intentions to save energy be explained? 41. How can peoples willingness to change over to green electricity be explained? 42. How can peoples attribution of (moral) accountability for possible shortfalls in future energy supply be explained? 43. How can peoples conviction concerning the efficacy of energy saving activities be explained? Additional question for all the research questions: Do communication/media related variables matter? Your Research Questions

20 How can peoples willingness to install a smart meter and to pay for the equipment be explained? What are the relevant concepts? Which variables are available to operationalize the concepts? How many people are willing to install a smart meter and how many are not? How much money are people willing to pay? How can peoples online use be explained? What are the relevant concepts? Which variables are available to operationalize the concepts? Ask descriptive research questions! Use Frequencies and Descriptives to answer the Questions! Modify the Variables using Recode and Compute! Make an annotated documentation of the data modifying and the analyses! (Syntax)

21 2. Day Combining Distributions Crosstabs

22 Description of the Distribution of Single Variables Frequencies: Number of cases and percentages FREQUENCIES welle_gr_1 welle_gr_2 welle_gr_3 welle_gr_123. Description: Mean, Standard Deviation, Minimum, Maximum DESCRIPTIVES smarteuro_3. Index building: Multiple Indicators? Reliabilty of a Scale RELIABILITY /VARIABLES=smartverbrauchinfo_3 smarttarife_3 smartautomatisch_3 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /SUMMARY=TOTAL.

23 Modifying Variables Changing the values of a variable and creating at the same time a new variable with the new values. RECODE welle_gr_123 (3 =1) (13, 23 =2) (123 =3) (else = sysmis) INTO wave3howoften. Building a new variable out of several old ones. COMPUTE smarteuro_23 = (smarteuro_2 + smarteuro_3) / 2. COMPUTE smarteuro_23m = mean (smarteuro_2, smarteuro_3). COMPUTE smarteuro_23d = (smarteuro_3 smarteuro_2). Building a new variable out of several old ones by counting certain values of these. COUNT eeknowledge_3 = ewsolar_3 ewwind_3 ewbiomasse_3 ewgeothermie_3 ewwasserkraft_3 ewpumpspeicher_3 (1). Changing (the values of) a variable under certain conditions. If (smartverbrauchinfo_3 > smartevalu) smartevalu = smartverbrauchinfo_3.

24 Selection of a relevant subsample Selecting Data Subsamples & Weighting the Data Set USE ALL. COMPUTE filter_$=(welle_gr_3 = 1). VARIABLE LABELS filter_$ 'welle_gr_3 = 1 (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMATS filter_$ (f1.0). FILTER BY filter_$. EXECUTE. Weighting the data set WEIGHT BY gfaktor_3. Weight off.

25 Analyzing Data Crosstabs: Are the distributions of two variables independent from each other? CROSSTABS /TABLES= smartbuy_3 BY bildung_3 /STATISTICS=CHISQ /CELLS=COUNT COLUMN /COUNT ROUND CELL. T Test: Comparing Means of independent samples T TEST GROUPS=eeressourcen2_3(1 2) /VARIABLES=smarteuro_3 /CRITERIA=CI(.95). T Test: Comparing Means of dependent samples T TEST PAIRS=smartverbrauchinfo_3 smartverbrauchinfo_3 WITH smarttarife_3 smartautomatisch_3 (PAIRED) /CRITERIA=CI(.9500). Anova: Comparing more then two independent groups UNIANOVA smarteuro_3 BY ein1_3 /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC= ein1_3 (DUNCAN) /CRITERIA=ALPHA(0.05) /DESIGN=ein1_3.

26 Analyzing Data II Relationship between variables CORRELATIONS /VARIABLES=smartverbrauchinfo_3 smarttarife_3 smartautomatisch_3 with smartbereit_3 /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE. Identifying dimensions FACTOR /VARIABLES eewinndrad_1 eestromnetz_1 eesolar_1 eeinfopflicht_1 eemotoren_1 /MISSING LISTWISE /ANALYSIS eewinndrad_1 eestromnetz_1 eesolar_1 eeinfopflicht_1 eemotoren_1 /PRINT INITIAL KMO EXTRACTION ROTATION /FORMAT SORT BLANK(.30) /CRITERIAMINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIAITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. Causal Relationship REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /DEPENDENT smartbereit_3 /METHOD=ENTER smartverbrauchinfo_3 smarttarife_3 smartautomatisch_3.

27 Research Questions for the Presentation How can peoples willingness to install a smart meter and to pay for the equipment be explained? Do communication / media related variables matter? Theory I: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their socioeconomic living conditions. H1: People with higher education & higher income are more willing to install a smart meter and pay for it. Theory II: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their knowledge about renewable energies. H2: People with higher knowledge are more willing to install a smart meter and pay for it. Theory III: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their intensity of communication about energy. H3: People with higher communication intensity about energy are more willing to install a smart meter and pay for it.

28 Independent Variable Value 1 Value 2 total Dependent Variable Value A Value B total 100 % 100 % 100 % The rules: 1. On the up side (Columns) always put the independent Variable! 2. On the near side (Rows) always put the dependent Variable! 3. Calculate the column percentages! 4. Compare the percentages within the rows! Don t calculate crosstabs with more then 12 cells (3*4)!

29 If participation in the seminar has no impact on the skills, how many students should be in the cells? SPSS skills next week Participation in the seminar Every day Not every day total low 15 Students high 15 Students total 20 Students 10 Students 30 Students

30 If participation in the seminar has no impact on the skills, how many students should be in the cells? SPSS skills next week Participation in the seminar Every day Not every day total low Students high Students total 20 Students 10 Students 30 Students

31 And if it has a positive impact? SPSS skills next week Participation in the seminar Every day Not every day total low 15 Students high 15 Students total 20 Students 10 Students 30 Students

32 And if it has a positive impact? SPSS skills next week Participation in the seminar Every day Not every day total low Students high Students total 20 Students 10 Students 30 Students

33 And if it has a negative impact? SPSS skills next week Participation in the seminar Every day Not every day total low 15 Students high 15 Students total 20 Students 10 Students 30 Students

34 And if it has a negative impact? SPSS skills next week Participation in the seminar Every day Not every day total low Students high Students total 20 Students 10 Students 30 Students

35 Degrees of freedom Significance

36 Why calculating significance? Because of sampling errors (Because of measurement errors) Calculate the probability that Relationships or Differences between distributions can be found not only in the sample but also in the basic population Why weighting the data?

37 Chi square Distribution Error just on one side! tables/

38 How to calculate the expected values in the Cells 531 * (127 / 531) * (66 / 531) = 531 * 0,239 * 0,124 = 15,8

39

40 If the distances between expected and observed values are big, Chi 2 also gets big. By dividing these absolute distances through the expected values the relative importance of these differences is modified. For example: The distance between an observed 100 and an expected 150 is 50. The distance between an observed 1100 and an expected 1150 is 50 too. But in the first case the squared distance will be divided by 150 in the second by That s why Chi 2 in the second example is much smaller.

41 In which of the two cross tables, do we get a higher Chi 2? low high Knowledge low high total total Willingness Willingness low high Knowledge low high total total Red: Green: Observed numbers Expected numbers

42 How to calculate the Chi 2 Square sum of the differences between expected and observed (for the nine cells) (26 15,8) 2 (60 62,9) 2 (41 48,3) 2 (15 21,1) 2 (91 84,2) 2 (64 64,7) 2 (25 29,1) 2 ( ,9) 2 (97 89) 2 (104,04) + (8,41) + (53,29) + (37,21) + (46,24) + (0,49) + (16,81) + (15,21) + (64,00) 15,8 62,9 48,3 21,1 84,2 64,7 29,1 115,9 89,0 6,58 + 0,13 + 1,10 + 1,76 + 0,55 + 0,01 + 0,58 + 0,13 + 0,72 = 11,56 = Chi 2

43 Research Question for the Exercise How can peoples online use be explained? Does the use of traditional media matter? Develop a theory, how online use can be explained! Which variables are available to operationalize the concepts used in the theory? State hypotheses that can be tested by the data! Use Crosstabs to test the hypotheses! Modify the Variables using COMPUTE, COUNT and IF! Make an annotated documentation of the analyses! (Syntax)

44 Concepts / Variables / Indicators Socioeconomic living conditions Formal Income Education Attitudes towards smart meter Willingness to install Willingness to pay Highest educational attainment 1. Basic qualification 2. Secondary Education 3. A Level 4. University degree Monthly net income 1. up to above Would you be willing to get a Smart Meter installed? 1. Yes 2. Maybe 3. No 4. has a SM already How much are willing to pay for the installation of a Smart Meter? 1. not willing to pay 2. up to above 500

45 Different kinds of variables Control Variable Independent Variable Dependent Variable Intervening Variable

46 3. Day Comparing Means T Test and Anova

47 Two groups of Students are comparing their SPSS skills by counting the No. of warnings

48 No of Warnings Group 1 No of Warnings Group 2 Which of the two groups is better?

49 No of Warnings Group 3 No of Warnings Group 2 What is the difference between the two distributions?

50 Variance The Standard Deviation is the Square Root of the Variance

51 Example: Calculation of Variance and Standard Deviation = 20 = 108 Ø = 5,00 Ø = 5,00 Variance: 20/9 = 2,22 108/9 = 12 SD: SQRT 2,22 = 1,49 SQRT 12 = 3,46

52 How to calculate the T Value T Value = (Mean (first Group) Mean (second Group)) Variance (first Group) n (first Group) + Variance (secondgroup) n (secondgroup) 1. Big differences between means = high T Value 2. Big Variances = smallt Value 3. Big number of Cases = high T Value

53 What are the differences between these five distributions?

54 Small Difference between Means Small Standard Deviation Big Difference between Means Small Standard Deviation

55 Big Difference between Means Big Standard Deviation Big Difference between Means Small Standard Deviation

56 Same variance Different distances between means Distribution small & Distribution small Distribution small & Distribution small

57 Same distance between means Different variances Distribution small & Distribution small

58 Nominal Scale The Three Types of Scales Example: What is your favorite television program? 1 = private channel 2 = public channel 3 = open chanel Ordinal Scale Example: How useful is this application for you? 1 = very useful 2 = somewhat useful 3 = not very useful 4 = not useful at all Interval Scale Example: How old are you? 18,19,20 years

59 Minimum required Scale Level Independent Variable (IV) Dependent Variable (DV) Crosstabs nominal nominal T Test (independent) dummy interval T Test (paired) No IV/DV: all Variables interval Anova nominal interval Correlations (Pearson) interval interval Correlations (Spearman) ordinal ordinal Factor Analysis No IV/DV: all Variables interval Regression Analysis interval/dummy interval A dummy variable is any variable with just two values

60 Important! All these methods of data analysis are quite robust against violations of these requirements. If it is plausible to argue, that the distances between the values of an ordinal scale are almost the same, you can use ordinal scales like interval scales. But you can never use a nominal scale like an ordinal or an interval scale!

61 Unianova (F Test) Comparing the means of more than 2 groups Question 1: Do people with different preferences for energy policy have different attitudes towards smart meter? 1. Cost effectiviness 2. Security of supply 3. Environmental compatibility Question 2: Do people with different innovation orientation have different attitudes towards smart meter? I am among the first to try new technologies. 1. completely agree 2. predominantly agree 3. rather disagree 4. completely disagree

62 The Basic Idea of Analysis of Variance (Unianova) Three types of Variance 1. The overall variance of the whole distribution (V total ) 2. Variance within the groups (V within ) 3. The variance between the groups (the mean distance between the groups) (V between ) F = V between V within High F value = differences between the groups are significant Big differences between the groups (a high V between ) high F value High variance within the groups (a high V within ) low F value

63 F Test and Post Hoc Tests F Test: Are there significant differences between the groups? No matter between which groups! Post hoc Tests: Between which groups exists a significant difference? Different Types of Tests: Conservative: Schaffee Liberal: Duncan

64 Why Post hoc Tests? Reality No difference between groups exists Yes a difference between groups exists Statistics No difference between groups exists Yes a difference between groups exists Alpha Error Beta Error Post hoc Tests prevent from the inflation of Alpha Errors

65 A Bit on the Side! If you don t want to compare groups of people but 1. different answers of the same person to the same question at different times 2. different answers of the same person to different questions than use: T TEST PAIRS=smartverbrauchinfo_3 smartverbrauchinfo_3 WITH smarttarife_3 smartautomatisch_3 (PAIRED) /CRITERIA=CI(.9500).

66 Research Questions for the Presentation How can peoples willingness to install a smart meter and to pay for the equipment be explained? Do communication / media related variables matter? Theory I: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their perception of the scarcity of energy resources. H1: People who are more worried about the scarcity of energy resources are more willing to install a smart meter and pay for it. Theory II: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their use of online media. H2: People who use online media are more willing to install a smart meter and pay for it. Theory III: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their general behavior towards innovations. H3: People who are open regarding innovations are willing to install a smart meter and pay for it.

67 Research Question for the Exercise How can peoples online use be explained? Does the use of traditional media matter? Develop a theory, how online use can be explained! Which variables are available to operationalize the concepts used in the theory? State additional hypotheses that can be tested by the data! Use T tests and/or UNIANOVA to test the hypotheses! Modify the Variables using RECODE, COMPUTE and COUNT! Make an annotated documentation of the analyses! (Syntax)

68 4. Day Relationships between Variables: Correlations

69 After the SPSS Seminar the lecturer conducted a test with 2 knowledge questions and a survey with 4 questions about the evaluation of the seminar The maximum of points obtainable in each of the two tests was 10. The evaluations were measured using 5 point Likert scales from 1 = I don t agree up to 5 = I completely agree Liking: Learned: Frustration: Competencelecturer: I liked the seminar very much I ve learned a lot in the seminar Participating in the seminar was frustrating The lecturer is quite competent.

70 The data of the test and the evaluations of 20 Students The results of the 2 knowledge tests

71 The data of the test and the evaluations of 20 Students A plot of the data in two dimensions X and Y axes

72 Are the two knowledge tests positively correlated? H1: If a student achieves a high score in the first tests he will also achieve a high score in the second test!

73 The Correlation coefficient r= X i X (mean) ) * Y i Y (mean) ) (N 1) * SD x * SD y r = 1.0 r = 1.0 r = 0.0 a perfect positive relationship a perfect negative relationship no relationship between the variables

74 /19 = 8,68 SQRT 8,68 = 2,95 SD = 2, /19 = 7,95 SQRT 7,95 = 2,82 SD = 2,819

75 r= X i X (mean) ) * Y i Y (mean) ) (N 1) * SD x * SD y r= 109 (20 1) * 2,946 * 2,819 r= ,79 = 0,691

76 r= X i X (mean) ) * Y i Y (mean) ) (N 1) * SD x * SD y 1. If both differences between measurement and mean are positive or both are negative the result of the multiplication is positive. 2. If one the two is negative, the result of the multiplication is negative. 3. If the sum of the negative results is bigger than the sum of the positive results, than the correlation is negative. If it is much bigger, than a high negative correlation 4. If the sum of the positive results is bigger than the sum of the negative results, than the correlation is positive. If it is much bigger, than a high positive correlation To standardize the results (all correlations are between 1 and +1) the sum is afterward divided through the product of the Standarddeviations of the single Variables.

77 Do students with higher rankings in the test like the seminar better as students with lower rankings?

78 Do students with higher rankings in the test have the impression that they have learned more than students with lower rankings?

79 Do students with higher rankings in the test feel less frustrated than students with lower rankings?

80 Do students with higher rankings evaluate the lecturer as more competent than students with lower rankings?

81 The lecturer decided to weight the second test question double? Has this an impact on the correlation between the knowledge variables?

82 Why weighting data? Random Sampling Representative Sample Sampling Errors Distorted Sample Census data available (complete inventory) Adjusting the Sample to the census (Weighting)

83 How to deal with Sampling Errors Weighting the data! Age Percent in the Basic Population Percent in the Sample % 9 % 12/9 1, % 10 % 15/10 1, % 14 % 18/14 1, % 30 % 17/30 0, % 16 % 15/16 0, % 21 % 23/21 1,10 Weighting factor

84 A model to visualize your theory Socioeconomic living conditions Income Education Energy related attitudes, knowledge and behavior Willingness to install a smart meter Towards resources Media use Online use Towards renewables Energy related communication Willingness to pay for a smart meter

85 Research Questions for the Presentation How can peoples willingness to install a smart meter and to pay for the equipment be explained? Do communication / media related variables matter? Theory I: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their by the evaluation of the different functions of the smart meter. H1: People who are evaluating the functions more positive are more willing to install a smart meter and pay for it. Question I: Does each of the three evaluations has the same impact? Is their common impact stronger than the impact of every single variable? Question II: Do very positive evaluations of more than one feature have a very strong impact on the willingness to install a smart meter and to pay for the equipment?

86 Research Question for the Exercise How can peoples online use be explained? Does the use of traditional media matter? Develop a theory, how online use can be explained! Which variables are available to operationalize the concepts used in the theory? State additional hypotheses that can be tested by the data! Use Correlations to test the hypotheses! Use Crosstab, T Test and Correlation to test the same hypotheses and compare the results Modify the Variables using RECODE, COMPUTE and COUNT and Make an annotated documentation of the analyses! (Syntax)

87 5. Day Identifying Dimensions: Factor Analysis

88 Do the selected indicators of the theoretical concepts really operationalize the concepts? Theoretical concept A Theoretical concept B Theoretical concept C Indicator A1 Indicator A2 Indicator A3 Indicator C1 Indicator C2 Indicator C3 Indicator B1 Indicator B2 Indicator B3 Empirical Dimension A Empirical Dimension B Empirical Dimension C

89 Dimension?? Dimension?? Measurement E Measurement D Measurement F Measurement A How many dimensions can be identified behind these single measurements? Measurement G Measurement B Which variable is part of Measurement H which dimension? Measurement C Measurement I Dimension?? Measurement K Dimension??

90 Very useful to identify dimensions of Attitudes Emotions Values Evaluations Gratifications (Knowledge) Not always useful to identify dimensions of behavior Media Use Leisure time activities (because one behavior can substitute an other)

91 Factor Analysis identifies groups of variables that are (strongly) correlated and which are not or just slightly correlated with other groups of variables.

92 Factor Analysis can transform the variables into factor scores. After the transformation the factor scores are unrelated.

93 First step: Are the date adequate for Factor Analysis? Bartlett's Test of Sphericity tests if the correlations between the variables are just a coincidence. If this Hypotheses can not be rejected, the data are not adequate for factor analysis (Chi 2 must be significant) Kaiser Meyer Olkin (KMO): Are the variables in the base population sufficiently correlated? The rule of thumb KMO KMO >= 0,9 KMO >= 0,8 KMO >= 0,7 KMO >= 0,6 KMO >= 0,5 KMO < 0,5 Interpretation marvelous very good good mediocre miserable unacceptable

94 How to identify the number of dimensions? 1. The idea of the Eigenvalue! By dividing a Variable through their own SD you can standardize a variable. After being standardized the variable has the SD of 1: Their Eigenvalue = 1. If you are running a factor analysis with 7 variables: The sum of the Eigenvalues of these seven Variables is 7. With factor analysis you want to reduce complexity (variance): Make of 7 Variables just as few factors as possible. At least one factor is always calculated. A second factor is calculated if the explained SD by the second factor is bigger than 1 (the Eigenvalue of a single variable). The program stops calculating new factor scores if their Eigenvalue is smaller than 1 (the Eigenvalue of a single variable) because calculating scores with a smaller Eigenvalue would not reduce complexity. 2. If you have theoretical assumptions you can also determine the number of dimensions from the beginning.

95 Research Questions for the Presentation Question I: How do people evaluate different activities to obtain a sustainable energy supply? Are their different dimensions of evaluation? Question II: Are their different dimensions of behavioral intentions concerning energy saving?

96 To obtain a sustainable energy supply several activities are discussed. I am now going to tell you some of these activities and I would like to know opinion regarding this proposals. Principle loading should be >.50 Secondary loading should be <.30 Item is double loaded

97 The state /energy providers shall do something One should subsidize the development of economical drives and engines. One should approve more wind turbines in order to promote the use of wind energy. One should push the expansion of the electricity grid through the Thuringian Forest in order to accelerate the turn to renewable energies. The people shall do something One should increase the price of electricity to be able to promote financially the use of solar energy. The citizens will be obliged to inform themselves more intensively about forms of energy savings in their homes. Giving names to the factors

98 Factor 2 Factor 1

99 How to build the indices? 1. Automatic calculation of factor scores by SPSS 2. Traditional index building using COMPUTE or COUNT

100 Check the reliability of the Scale! It is quite low! It should be higher than.60 It won t get better by excluding items! Ask yourself: Is it plausible, that the items are exchangeable Check: Are the items positively and significantly correlated

101 Second example: Energy related readiness to act Problem: Solution 1: Solution 2: Low factor loadings of some variabels Exclusion of the variable Predetermine the number of factors

102 Problems: 1. Very low factor loading 2. The third factor almost achieved the Eigenvalue level of 1 Solution: Determine the number of factors required!

103 A predetermined 3 Factor Solution

104 Task of the day! In the second wave 3 questions concerning emotional evaluations of nuclear power and 3 questions concerning evaluations of the possibility to substitute traditional forms of energy production through renewable energy were asked. Are these two aspects really two different dimensions? Build Indices based on the result of the factor analysis. Find out if people with negative emotions concerning nuclear power and/or positive attitudes concerning renewables energies are using more frequently online outlets of quality online media.

105 6. Day Causal relationships: Regression Analysis

106 How to identify causality? What is the problem? 1. The world is complicated: There are several plausible explications for a phenomena. 2. These explications are not independent from each other. (For example: As we have seen the amount of online use is not independent from the amount of print media use) 3. Focusing just on one factor ignores the others and the results obtained this way may be misleading. 4. Solutions: 1. Experimental Research Design = randomizing 2. Correlational Research Design = controlling other factors

107 A model to visualize your theory Socioeconomic living conditions Income Education Energy related attitudes, knowledge and behavior Willingness to install a smart meter Towards resources Towards renewables Willingness to pay for a smart meter Media use Online use Energy related communication

108 The independent variables are correlated Socioeconomic living conditions Income Education Energy related attitudes, knowledge and behavior Willingness to install a smart meter Towards resources Towards renewables Willingness to pay for a smart meter Media use Online use Energy related communication

109 The Multiple Regression Formula Y = b o + b 1 x 1 + b 2 x 2 + Y = dependent variable the estimation b 0 = constant term calculated b 1 = slope of the first independent variable X 1 calculated X 1 = values of the first independent variable known from data b 2 = slope of the second independent variable X 2 calculated X 2 = values of the second independent variable known from data

110

111 Y = b o + b 1 x 1 + b 2 x 2 +

112 Do students with higher rankings in the test have the impression that they have learned more than students with lower rankings? REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT learned /METHOD=ENTER PointsQ2_1. The constant: 1,66 1,7 The slope: 0,29 0,3

113 1. Step: Regression Analysis with one independent Variable Do students with higher rankings in the test have the impression that they have learned more than students with lower rankings? 1,7 +0,3 * 1 = 2 Y = b o + b 1 x 1 Y = 1, * (1 10) 1,7 +0,3 * 6 = 3,5 1,7 +0,3 * 10 = 4,7

114 How does SPSS calculates the regression line? The program calculates the position of a line which is less distant from all the measurements (the sum of the squared length of the green lines should be at a minimum).

115 Does a student feel that she/he has learned a lot? Without further information: The best estimator is the mean. Having the information from the Variable PointsQ2_1 we can make a better estimate How does SPSS calculate the explained variance? How much better? Explained Variance!

116 How does SPSS calculate the explained variance? Unexplained Variance: Between slope and measurement Explained Variance: Between slope and mean

117 The R Square * 100 is the Percentage of explained Variance: 39,2% It is calculated as follows: ExplainedVariance / Total Variance 13,411 / 34,200 = 0,392 Explained Variance Unexplained Variance The effect of the variable PointsQ2_1 is significant The Beta coefficient can be interpreted like a correlation coefficient.

118 2. Step: Regression Analysis with two independent Variables Some of the students had time to repeat at home what they have learned in the lesson. Some of them had not. Has repetition an additional effect on the subjective impression that you have learned something? Constant: 0,224 0,2 First slope: 0,277 0,3 Second slope: 1,034 1,0

119 2. Step: Regression Analysis with two independent Variables F value A test of the whole model: The F value will be always significant, if the single variables are significant. Number of Cases Be aware of the number of cases Danger of losing cases in multiple regression.

120 2. Step: Regression Analysis with two independent Variables Do students with higher rankings in the test have the impression that they have learned more than students with lower rankings? Has repetition an additional effect on the subjective impression that you have learned something? 0,2 + 0,3 * 1 + 1,0 * 2 = 2,5 0,2 + 0,3 * ,0 * 2 = 5,2 0,2 + 0,3 * ,0 * 1 = 4,2 Y = b o + b 1 x 1 + b 2 x 2 Y = 0, * (1 10) + 1,0 * (1 2) 0,2 + 0,3 * 1 + 1,0 * 1 = 1,5

121 A model to visualize your theory Socioeconomic living conditions Income Education Energy related attitudes, knowledge and behavior Willingness to install a smart meter Towards resources Media use Online use Towards renewables Energy related communication Willingness to pay for a smart meter

122 Research Questions for the Presentation How can peoples willingness to install a smart meter and to pay for the equipment be explained? Do communication / media related variables matter? Theory I: Peoples willingness to install a smart meter and to pay for the equipment is influenced by their socioeconomic living conditions, their attitudes towards renewable energies and their online use. H1: Higher income has a positive effect on willingness to install a smart meter and to pay for the equipment. H2: Positive attitudes toward renewables have a positive effect on willingness to install a smart meter and to pay for the equipment H3: Frequent use of online news has a positive effect on willingness to install a smart meter and to pay for the equipment.

123 Use the second wave! Find out, if the use of traditional media (TV, RADIO, Newspaper, Print Magazines) has an impact on the frequency of information orientated online use! Which of the traditional media has the biggest impact? Use regression analysis! The task of the day

124 7. Day Causal relationships: Regression Analysis Problems and Opportunities

125 Problems of Regression Analysis 1. Multicollinearity: The independent Variables are highly correlated 2. Non linearity: The relationship between independent and dependent variable is not linear recode 3. Scale Level: Nominal scaled variables as independent variables transform in dummy variables 4. Missing Cases High numbers of excluded cases because of listwise exclusion of missing cases pairwise 5. Heteroscedasticity The variances of the dependent variable is higher in subgroups of the independent Variable with higher values, than in subgroups with smaller values. (econometrics) 6. Autocorrelation The cases are not independent. But later cases are influenced by earlier cases (time series)

126 Opportunities 1. Modeling interactions between variables 2. Investigating causality using panel data and Granger Causality

127 The problem of Multicollinearity

128 The effect of POINTSQ2_1 is now low and not significant. The Tolerance is low.

129 The effect of FRUSTRATION is now positive and from POINTSQ1_1 extremely high. The tolerance is low.

130 Possible Solutions 1. Excluding one (or more) variables from the analysis 2. Building an index

131 1. How can peoples willingness to pay for the energy turn be explained? Do emotions towards nuclear energy influence the willingness? 2. Has online use a negative influence on newspaper use or has newspaper use a negative influence on online use? 3. Has the use of different types of television news programmes different effects on the attitudes towards nuclear energy? 4. Do political extremists use online media more frequently than traditional media?

132 How can confidence in technical innovations be explained? Socioeconomic living conditions Gender Age Education Political Interest Experiences & Knowledge Experiences with innovations Knowledge about new technologies Confidence in technical innovations Media use TV Magazine Use Print Magazine Use

133 GESIS: DATA, SERVICE, LITERATURE and more

134 Andy Field (2009): Discovering statistics using SPSS (and sex, drugs and rock'n'roll) Marija J. Norušis (2004): SPSS 12.0 statistical procedures companion / Christine P. Dancey; John Reidy (2004): Statistics without maths for psychology : using SPSS for Windows Eric L. Einspruch (1998): An introductory guide to SPSS for Windows T. W. Anderson and Jeremy D. Finn / Susan B. Gerber; Kristin E. Voelkl (1997): The SPSS guide to the new statistical analysis of data

135 The Syntax file Use the possibilty of making annotations! (Make it easy to the lecturer to understand what you have done) The whole syntax file should include just the analyses that you used for your paper! The whole syntax should run without error from the beginning to the end! (That s the way how I ll check what you have done)

136 The Structure of the Paper Descriptives Dimensions Crosstabs T Test Correlation Regression DV IV 1 + 2