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‫סטטיסטיקה לתלמידי תקשורת‬
‫‪36-410-4124/246‬‬
‫שם המרצה‪ :‬ד"ר ש‪ .‬פרידקין‬
‫שנה‪ :‬ב'‬
‫סוג הקורס‪ :‬חובה‪ ,‬תואר ראשון‪ ,‬הרצאה ותרגיל‬
‫היקף שעות‪ 3 :‬ש"ס‪ 1.1 ,‬ש"ש‬
‫שנת לימודים‪ :‬תשע"ז סמסטר‪ :‬ב'‬
‫שעות קבלה‪ :‬יום ג' ‪16:15-18:00‬‬
‫משרד‪ :‬בניין ‪ ,101‬חדר ‪11‬‬
‫טל'‪:‬‬
‫דוא"ל‪[email protected] :‬‬
‫א‪ .‬תיאור הקורס ומטרתו‪:‬‬
‫הקורס נועד להקנות מושגים וכלים סטטיסטיים בסיסיים במטרה לאפשר ניתוח נתונים עצמאי וקריאה ביקורתית של‬
‫ספרות מקצועית בתחומי התקשורת השונים‪ .‬הקורס מציג ומדגים שימושים מתחומי הסטטיסטיקה התיאורית‬
‫וההיסקית לצורך ניתוח‪ ,‬פירוש והצגת נתונים‪.‬‬
‫ב‪ .‬שיטה‪:‬‬
‫הקורס מבוסס על הרצאה ותרגיל שבועיים‪ .‬במהלך הקורס הסטודנט יקבל כלים‪ ,‬הן בתיאוריה והן ביישום מעשי‬
‫בעזרת מחשב‪ ,‬לביצוע ניתוחים כמותיים‪-‬סטטיסטיים במדעי החברה בכלל ובתקשורת בפרט‪ .‬בתרגיל‪ ,‬המשתתפים‬
‫יתרגלו חומר שנלמד בהרצאה שתקדם לו כולל פתרון שאלות שיילקחו בעיקר מתחום המחקר הכמותי בתקשורת‪.‬‬
‫חלק ניכר מהתרגילים יוקדש ללמידה ושימוש בתוכנת העיבודים הסטטיסטיים ‪ .SPSS‬פרטים מלאים והנחיות ימסרו‬
‫בקבוצות התרגול‪.‬‬
‫ג‪ .‬דרישות הקורס‪:‬‬
‫‪ .1‬שימוש במחשבון מדעי ‪ CASIO-991‬לצורך חישובים סטטיסטיים במהלך הקורס‪.‬‬
‫‪ .1‬השתתפות פעילה בשיעורים ובתרגילים‪.‬‬
‫‪ .3‬קריאת פרטי החובה לקראת כל שיעור‪.‬‬
‫‪ .4‬הגשת ‪ 1‬סוגים של מטלות‪:‬‬
‫(‪ )I‬מטלות בכל מפגש‪-‬תרגיל‪,‬‬
‫(‪ )II‬עבודת מחקר בתקשורת (ביצוע ניתוחים סטטיסטיים באמצעות ‪ SPSS‬וכתיבת ממצאי המחקר) אשר יש‬
‫להגיש לא מאוחר משבועיים לאחר תום הקורס‪ .‬העבודה תוגש באמצעות מערכת ה‪ .Moodle‬במסגרת‬
‫העבודה יש להגיש קבצים הבאים‪Word file, Syntax file, Output file, SPSS file. :‬‬
‫‪ .1‬מבחן‪.‬‬
‫סטודנטים שלא יגישו את התרגילים במועדים שנקבעו ללא סיבה מוצדקת‪ ,‬לא יורשו לגשת למבחן המסכם‪.‬‬
‫ד‪ .‬הרכב הציון הסופי‪:‬‬
‫הגשת מטלות בכל מפגש‪-‬תרגול –‪.10%‬‬
‫הגשת עבודה מסכמת אשר יש להגיש לא מאוחר משבועיים לאחר תום הקורס – ‪.10%‬‬
‫מבחן – ‪.00%‬‬
‫ה‪ .‬חומרי קריאה‪:‬‬
‫‪Books:‬‬
‫‪Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.‬‬
‫שמור לפי ‪)2386011( HAY‬‬
‫‪Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage.‬‬
‫שמור לפי ‪)2382828( REI c‬‬
‫‪1‬‬
Morgan, G. A., Leech, N. L., Gloeckner, G. W. & Barrett, K. C. (2011). SPSS for introductory and
intermediate statistics: IBM SPSS for introductory statistics: Use and interpretation. (4th ed). New
York: Taylor & Francis.
‫אין‬
Articles:
Neuman, W. R., Guggenheim, L., Jang, S. M., & Bae, S. Y. (2014). The Dynamics of Public
Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2), 193–214.
)10044/( ‫כתב עת אלקטרוני‬
Shaw, A., & Hill, B. M. (2014). Laboratories of Oligarchy? How the Iron Law Extends to Peer
Production. Journal of Communication, 64(2), 215–238.
)10044/( ‫כתב עת אלקטרוני‬
Jungher, A. (2014). The Logic of Political Coverage on Twitter: Temporal Dynamics and Content.
Journal of Communication, 64(2), 239–259.
)10044/( ‫כתב עת אלקטרוני‬
Giglietto, F., & Selva, D. (2014). Second Screen and Participation: A Content Analysis on a Full
Season Dataset of Tweets. Journal of Communication, 64(2), 260–277.
)10044/( ‫כתב עת אלקטרוני‬
Emery, S. L., Szczypka, G., Abril, E. P., Kim, Y., & Vera, L. (2014). Are You Scared Yet?
Evaluating Fear Appeal Messages in Tweets about the Tips Campaign. Journal of Communication,
64(2), 278–295.
)10044/( ‫כתב עת אלקטרוני‬
Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter
During the 2012 U.S. Presidential Election. Journal of Communication, 64(2), 296–316.
)10044/( ‫כתב עת אלקטרוני‬
Park, J., Baek, Y. M., & Cha, M. (2014). Cross-Cultural Comparison of Nonverbal Cues in
Emoticons on Twitter: Evidence from Big Data Analysis. Journal of Communication, 64(2), 333–
354.
)10044/( ‫כתב עת אלקטרוני‬
Knobloch-Westerwick, S., & Crane, J. (2012). A losing battle: Effects of prolonged-exposure to thin
ideal images on dieting and body satisfaction. Communication Research, 39, 79-102.
)101116( ‫כתב עת אלקטרוני‬
Knobloch-Westerwick, S., & Hoplamazian, G. J. (2012). Gendering the self: Selective magazine
reading and reinforcement of gender conformity. Communication Research, 39, 358-384.
)101116( ‫כתב עת אלקטרוני‬
Velez, J. A., Mahood, C., Ewoldsen, D. R., & Moyer-Gusé, E. (2014). Ingroup Versus Outgroup
Conflict in the Context of Violent Video Game Play: The Effect of Cooperation on Increased
Helping and Decreased Aggression. Communication Research, 41(5), 607-626.
)101116( ‫כתב עת אלקטרוני‬
Custers, K., & Van den Bulck, J. (2013). The Cultivation of Fear of Sexual Violence in Women:
Processes and Moderators of the Relationship between Television and Fear. Communication
Research, 40(1), 96-124.
)101116( ‫כתב עת אלקטרוני‬
Fu, W. W. (2013). National Audience Tastes in Hollywood Film Genres: Cultural Distance and
Linguistic Affinity. Communication Research, 40(6), 789-817.
)101116( ‫כתב עת אלקטרוני‬
1
Lee, N-J., Shah, D. V., & McLeod, J. M. (2013). Processes of Political Socialization: A
Communication Mediation Approach to Youth Civic Engagement. Communication Research, 40(5),
669-697.
)101116( ‫כתב עת אלקטרוני‬
‫ נושאים ורשימת קריאה‬.‫ו‬
‫ סטטיסטיקה ומדעי תקשורת‬.1
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Statistics and Communication Science (pp. 1-16) - Statistics and Communication Science; Why Do
Science?; Assumptions and Philosophies of Scientific Investigation; Building Your Statistical Vocabulary;
The Role of Statistics in Scientific Investigation]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Using Statistics
to Conduct Quantitative Research (pp. 1-16) - A World of Statistics; Why Do Quantitative Research?;
Typical Steps Involved in Quantitative Research]
‫ יסודות של מדידה בתקשורת‬.2
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Fundamentals of Measurement (pp. 16-30) - Methods of Measurement: Operationalization; Levels of
Measurement; Measurement Precision; Qualitative Data versus Quantitative Measurement; Measurement
Quality: Reliability of Measurement;Validity of Measurement]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Using Statistics
to Conduct Quantitative Research (pp. 17-27) - Collecting Data on Variables; Variables and Hypotheses;
Measurement of Variables]
‫ דגימה‬.3
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Sampling (pp.31-44) - Population Inference; The Literary Digest Poll: Population Inference Gone Awry;
Population Inference Through Representativeness; Sampling Methods:Nonprobability Sampling; Probability
Sampling; Is Nonprobability Sampling Really So Bad?]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Using Statistics
to Conduct Quantitative Research (pp. 28-42) - Sampling]
‫ תיאורים גרפיים וטבלאיים של נתונים‬.4
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge. [Data
Description and Visualization (pp. 45-50) - Graphical and Tabular Descriptions of Data; Frequency Tables;
The Histogram; Describing the Shape of a Distribution; Another Graphical Tool: The Box Plot (pp. 60-61)]
‫ מדדי נטייה מרכזית‬.5
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Measures of Central Tendency (pp. 51-55) - The Mode; The Median; The Arithmetic Mean; Choosing a
Measure of Central Tendency]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Central
Tendency (pp. 45-60) - Doing a Study and Reporting Descriptive Information; Typical Measures of Central
Tendency; Relations among Mean, Median, and Mode]
‫ מדדי השתנות ופיזור‬.6
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Measures of Variation (pp. 56-59) - The Range and Interquartile Range; The Standard Deviation; The
Variance; Quantifying Skewness and Kurtosis (pp. 59-60); Standardization (pp. 61-63)]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Looking at
Variability and Dispersion (pp. 61-86) - Assessing Dispersion; the Relationship between Measures of
Central Tendency and Variability; Examining Distributions]
3
‫ יסודות הסתברות‬.7
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Fundamentals of Probability (pp. 83-102) - Defining Probability; Laws of Probability; The Additive Law of
Probability; The Multiplicative Law of Probability; Probability Distributions; The Binomial Probability
Distribution; The Normal Probability Distribution; Chebyshev's Theorem; Random Variables and Expected
Values]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Examining
Distributions (pp. 74-86)]
‫ הערכה וכימות מהימנות‬.8
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Assessing and Quantifying Reliability (pp. 103-129) - Classical Test Theory; Partioning Measurements
into Their Components; The Definition of Reliability Under Classical Test Theory; Estimating the Reliability
of Quantitative Measurements; Estimating Reliability From Repeated Measurements Over Time;
Estimating Reliability From Internal Consistency of Indicator Scores; Reliability of Method or of
Measurement?; Reliability of Subjective Categorical Judgments; Holsti's Method; Correcting for Chance;
Agreement: Scott's π and Cohen's k; Using an Agreement lndex; How High is High Enough?]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Ensuring
Reliability and Validity (pp. 117-142) - The Notion of Measurement Acceptability; How to Do a Study of
Measurement Adequacy; Reliability; Validity; The Relation of Validity to Reliability]
‫ אומדן פרמטרים‬.9
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Parameter Estimation (pp. 130-157) - The Theory of Estimation;The Sampling Distribution;Properties of
the Sampling Distribution of the Sample Mean;Deriving the Probability of Obtaining Certain Sample Means
Interval Estimates; The Confidence Interval ;A More Realistic Approach to Interval Estimation; The
Relationship Between Confidence and Precision; Computing the Probability of a Sample Mean, Revisited
;Interval Estimates Derived From Samples of Convenience;Estimating a Population Proportion;
Bootstrapping a Confidence lnterval]
‫ בדיקת השערות‬.11
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Hypothesis Testing Concepts (pp. 158-182) - Hypothesis Testing Steps:Step 1: Tlanslate the Research
Hypothesis or Question into statistical Hypothesis;Step 2: Quantify the Obtained Result; Step 3: Derive the p
value ;Step 4: Decide Between the Null and Alternative Hypothesis; Step 5: Interpret the Result of the Test in
Substantive Terms; Testing a Hypothesis About a Population Proportion; Testing a Nondirectional ("Twotailed") Hypothesis; Testing a Directional ("One-tailed") Hypothesis; Decision Errors, Power, and Validity;
Type I, Type II, and Type III errors; The Validity and Power of a Statistical Test; Hypothesis Test or
Confidence Interval?]
‫ מתאם‬.11
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge.
[Describing Association Between Quantitative Variables (pp. 63-82) - Pearson's Coefficient of Correlation;
Alternative Measures of Association;Cautions When Interpreting Correlation;Visualizing Correlation: The
Scatterplot; Descriptively Comparing Groups;Data Screening and Missing Data; Introducing Some
Common Symbolic Notation]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Correlations
(pp. 87-116) - The Notion of Correlation; Elements of the Correlation; Computing the Pearson ProductMoment Correlation; Matters Affecting Correlations; Methods of Correlations; Alternative Forms of
Association]
‫ השוואת ממוצעים‬.12
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge. [Testing
a Hypothesis About a Single Mean (pp. 183-209):The One-Samplet test ;Testing a Directional Hypothesis
4
About a Single Mean ;Testing a Nondirectional Hypothesis ;Conducting the One-Sample t test With a
Computer ;Statistical Assumptions ;Large Versus Small Samples ;Confidence lntervals ;Bootstrapping the p
value ;Comparing the Means of Paired Responses ;The Paired-Samples t test;Paired-Sample Inference
From Nonrandom Samples;Comparing Two Independent Groups (pp. 210-243): The Independent Groups t
test ;The Pooled Variance Approach;The Welch-Satterthwaite Approach;The Conditional Decision Rule
;The Behrens-Fisher Problem ;Violations of the Normality Assumption;Confidence Intervals for the Mean
Difference ;Bootstrapping Confidence Intervals and p-values ;Effect Size ;Testing for Group Differences in
Variability;Levene’s Test ;The Brown-Forsythe Test ;The F-ratio Test: A Test to Avoid ;Comparing Two
Groups from Nonrandom Samples;The Random Assignment Model of Chance ;Inference Without Random
Sampling or Random Assignment;Thinking Clearly About Inference;Comparing Two Independent
Proportions]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Statistical
Significance Hypothesis (pp. 143-178): Testing When Comparing Two Means: Doing a Study That Tests a
Hypothesis of Differences Between Means; Assumptions in Parametric Hypothesis Testing; Comparing
Sample and Population Means; Comparing the Means of Two Sample Groups: The Two-Sample r Test;
Comparing Means Differences of Paired Scores: The Paired Difference t; Assessing Power]
‫ ניתוח שונות‬.13
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge. [Single
Factor Analysis of Variance (pp. 366-407): Analysisof Variance: Partitioning the Outcome Variable into its
Sources of Variation; Total, Between-, and Within-Group Variability in Y; The F ratio; Underlying
Statistical Theory; Statistical Assumptions of ANOVA; Revisiting the Pooled Variance t test; Quantifying
Effect Size; Why Not Multiple t tests?; Inference with Nonrandom Samples; Pairwise Mean Comparisons;
The Multiple Test Problem Resurfaces; The Bonferroni Correction; Holm’s Sequential Rejection Method;
The Games-Howell Method; Using a Pooled Error Term; Focused Contrasts; Focused t tests; Contrast
Coefficients; Scheffe’s Test; Some Controversies in the Comparison of Multiple Groups; Planned Versus
Unplanned Comparisons: To Correct or Not?; Are We A Bit Fickle About The Multiple Test Problem?; Do
We Really Need ANOVA?]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Comparing
More Than Two Means (pp. 179-246): One-Way Analysis of Variance; Hypothesis Testing for More Than
Two Means; The Analysis of Variance Hypothesis Test; What After ANOVA? Multiple Comparison Tests;
Extensions of Analysis of Variance; Factorial Analysis of Variance; Doing a Study That Involves More Than
One Independent Variable; Types of Effects to Test; Computing the Fixed-Effect ANOVA; Random and
Mixed-Effects Designs]
‫ רגרסיה‬.14
Hayes, A. F. (2012). Statistical methods for communication science. New York: Routledge. [Simple
Linear Regression (pp. 271-309): The Simple Linear Regression Model; The Simple Regression Line; The
Least Squares Criterion; The Standard Error of Estimation; The Standardized Regression Equation;
Variance Explained by a Regression Model ; More on Residuals; The Dangers of Extrapolating Away From
the Data; Population Inference in Linear Regression; Testing a Hypothesis About the Population Regression
Weight; Confidence Intervals for β; Reframing Inference In Terms of the Population Correlation; Statistical
Assumptions; Inference in Nonrandom Samples: The Permutation Test; Detecting Influential Cases;
Distance, Leverage, and Influence; Influence as Change in the Model When a Case is Excluded]
Reinard, J. (2006). Communication research statistics. Thousand Oaks, CA: Sage. [Multiple
Regression Correlation (pp. 345-380): Contrasting Bivariate Correlation and Multiple Regression
Correlation; Components of Multiple Correlations; How to Do a Multiple Regression Correlation Study]
1