PSY 302: Chapter 1 Textbook Notes
PSY 302: Chapter 1 Textbook Notes PSY 302
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Date Created: 09/30/15
CHAPTER 1 of Essentials of Statistics For The Behavioral Sciences Intro To Statistics Statistics Mathematical procedures that can organize analyze and interpret gathered information and put them in accurate simple gures Stats bring order out of chaos and are understood universally in the scienti c community 0 Used to organizesummarize info so the researcher can see what happened in the study and communicate results to others 0 Used to answer the questions that initiated the research by determining what conclusions are justi ed based on the results obtained Populations and Samples 0 Research begins with a general question about speci c groups of individuals Populations The entire set of individuals of interest for a certain research question Can be large or small and is not limited to humans can be rats corporations etc 0 Sample A set of individuals selected from a population intended to represent the population in a research study as it is hard to survey an entire population Can vary in size 0 When a researcher nishes examining a sample the goal is to generalize the results back to the population It39s a cycle 0 The population exists a sample is selected from the population the sample is researched and the results are generalized and is exposed back to the existing population Variables and Data 0 Variable A characteristiccondition that changes or has different values for different individuals A variable varies For example it39s raining A sample of 100 people are asked what they think Their resulting various moods are the variable Other examples are height weight gender temperature size of room etc They are environmental and behavioral Data plural Measurements or observations 0 Data Set Collection of measurements or observations 0 Datum singular Single measurement or observation commonly called a score or a raw score 0 NOTE Populations and samples are de ned in terms of individuals However populations or samples can also have scores So there could be a sample of scores single points of data A sample of 100 individuals produces a sample of 100 scores Parameters and Statistics 0 It39s important to distinguish whether data came from a population or a sample 0 Parameter A characteristic usually numerical that describes a population ie the average score of a population The research process often begins with a question about a population parameter Actual data however comes from a sample and are used to compute sample statistics Statistic A characteristic that describes a sample ie the average score of a sample Every parameter has a corresponding statistic o A lot of the textbook looks at the relationship between sample statistics and population parameters Descriptive and lnferential Statistical Methods 0 There are two categories of procedures to organize and interpret data 0 Descriptive Statistics Statistical procedures to summarize organize and simplify data 0 Techniques that take raw scores and organizessummarizes them in a manageable form usually in a table or graph or computing an average from as much as hundreds of scores 0 lnferential Statistics Techniques that allow us to study samples and make generalizations about the populations associated ie if there39s a 4 point difference in data an inferential statistic would be that there39s actually no difference it39s just chance Or the 4 point difference is accurate Descriptive statistics come rst and then inferential stats can be interpreted from that 0 Sampling Error Samples are used to represent a population however since they can only provide limited information about the population it39s not entirely accurate The discrepancy between a sample statistic and the corresponding population parameter is the sampling error This is the problem that inferential statistics must address Also called margin of error 0 For example a population of 1000 college students has an average age of 213 years ave IQ of 1125 and is 65 female Two samples of ve students each were taken Sample 139s ave age is 198 IQ is 1046 and is 60 female Sample 239s ave age is 204 IQ is 1142 and is 40 female Relationships Between Variables 0 Sometimes research is meant to look at and present one variable sometimes it39s comparing two or more There are multiple ways to do this 0 The Correlational Method Observing two variables naturally as they are for a set of individuals Can help develop relationships between two things ie wakeup time and grades Great to be presented on a graph speci cally scatter plots Does not provide an explanation for the relationship however speci cally a causeandeffect relationship 0 The Experimental Method Compares two or more groups of scores Examined by using one of the variables to de ne the groups then measuring the second variable to obtain the scores for each group EX 15 kids watch a violent lm 15 watch a nonviolent Violence is the variable Kids who watched the violent lm committed more violent acts later at recess the second variable There is a causeandeffect to be seen here There are two characteristics that differentiates this method from others 0 Manipulation Researcher manipulates one variable and the second is examined to see if changes occur 0 Control Researcher exercises control over research situation to make sure other variables to not tamper the relationship being examined To maintain control there are two types of variables to be considered 0 Participant Variables Characteristics like age gender intelligence that vary from one to another ie in two sample groups one group is primarily female which affects results 0 Environmental Variables Environmental characteristics like lighting or time of day that can affect behaviors of individuals being tested There are also three basic techniques to control other variables 0 Random Assignment Each participant has an equal chance of being assigned to each of the treatment conditions 0 Matching The researcher ensures equivalent groups and environments this way 0 Holding them constant The researcher keeps one element the same ie all participants are age 15 0 Independent Variable The variable being manipulated by the experimenter The treatment individuals are assigned ie watching a violent or nonviolent lm 0 Dependent Variable Variable observed and measured to get scores Control Conditions in an Experiment 0 Often an experiment will have a condition where some participants don39t receive treatment to compare to participants that do 0 Control Condition The individuals in this don39t receive experimental treatment These individuals are in the control group 0 Experimental Condition These individuals do receive experimental treatment and are in the experimental group Nonexperimenta Methods Nonequivalent Groups and PrePost Studies Real experiments must include manipulation of an independent variable and rigorous control of other variables This means there are other research methods that exist they39re just not considered experiments Nonequivalent Groups Study A study that involves the researcher39s inability to use techniques like random assignment to sort participants of a study into groups ie all boys go to a group all girls go to a group Because researchers can39t use techniques to control participant variables and ensure equivalent groups N65 is not considered a true kind of experiment PrePost Study One variable is initially examined then examined again later The researcher however has no control over the passage of time or any environmental factors ie measuring depression initially and then later on You can39t control the weather or how fast time goes 0 Although they are not real experiments both of the above studies still produce the same data an experiment would Independent and dependent variables in terms of nonexperimental studies are the same However since the independent variable is notcannot be manipulated in many nonexperimental studies the independent variable is often called the quasiindependent variable 0 To be considered an experiment the two core elements are that the researcher must manipulate one of the two variables and all other variables that might in uence the experiment must be controlled Data structures and Statistical Methods 0 Correlation The measured and described relationship between two variables Not always numerical Constructs and Operational De nitions ConstructHypothetical Construct Internal attributes that can39t be directly observed but are useful in describing behavior ie personality traits 0 We can39t measure constructs but we can observe things representative of the construct ie we can39t see intelligence but we can observe inteigent behaviors Those behaviors can make an operational de nition of the construct Operational De nition Measures and de nes a construct in terms of external behaviors ie IQ test scores Discrete and Continuous Variables Variables in a study can be de ned by the type of values assigned to them 0 Discrete Variable Separate indivisible categories There are no intermediate values between two adjacent categories ex roing dice you can roll a 4 not a 45 Others are occupations gender major etc 0 Continuous Variable A variable with an in nite amount of values between two categories 0 Whenever a researcher is free to determine the degree of precision or number of categories for a variable then the variable is continuous ie choosing between only short average and tall for heights is continuous like numerical height values o It is rare to obtain identical measurements for two individuals with continuous variables 0 Discrete and continuous apply to the variables not the obtained scores 0 Real Limits The boundaries of intervals for scores represented on a continuous number line The real limit separating two scores is exactly halfway between the Upper Real Limit or the top of the interval and the Lower Real Limit which is at the bottom For example the Real Limit in a study is 150 the lower real limit is 1495 and the upper real limit is 1505 If two pieces of data are 1497 and the other is 1503 they would both be labeled at 150 Scales of Measurement 0 The Nominal Scale Consists of categories with different names but no quantitative distinctions between observations ie looking at different college majors Can still be occasionally represented by numbers like if you were looking at room numbers but they still don39t re ect quantitative information No category is bigger or smaller than the other e The Ordinal Scale A set of categories ranked from smallest to largest An example is people nishing rst second or third in a track race However the ordinal scale doesn39t tell you how much time passed between the rst and second runner nishing The Interval Scale Consists of a series of ordered categories with even intervals between categories ie inches on a ruler The zero point however is arbitrary and doesn39t indicate a zero amount of the variable being measured ie temperature scales have 0 degrees but it doesn39t mean there39s an absence of temperature 0 The Ratio Scale Consists of a series of ordered categories with even intervals between categories ie inches on a ruler However it contains an absolute zero point the absence of something ie one gas truck has ten gallons the other has 0 gallons Statistical Notation Scores 0 Raw scores the original unchanged scores obtained in a study 0 Scores for a variable can be represented by X which can be used to title a column of scores for example Y can also be used if there are two variables If one score is 35 then X35 o The letter N is used to specify how many scores are in one set An uppercase N shows the scores in a population lowercase n is the scores of a sample Summation Notation o The sum of a set of scores summation is represented by sigma Z and directly represents quotthe sum ofquot Ex for example means to add all scores for the variable X This means Ex is quotthe sum of the scoresquot 0 Can be accompanied by equations ie Zx1quot2 That means you need to gure out all xlquot2 variables then add them all together
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