class: center, middle, inverse, title-slide # Introduction to statistics for psychologists ## ⚔<br/>with xaringan ### Goran Kardum ### Department of Psychology ### 2016/12/12 (updated: 2021-10-04) --- ``` ## Loading required namespace: bibtex ``` # Few words about statistics - “In God we trust. All others must bring data.” (William Edwards Deming) -- - it is quite common that a large number of students starting an under- graduate degree in psychology are not enthusiastic about having to study statistics and research methodology. [1] "(Russo, 2021)" -- - In Social Sciences we try to describe or predict the behavior of the population on the basis of information obtained from a representative sample from that population [1] "" --- # Data science vs. statistics - Statistics is a field or branch of mathematics which seeks to collect and interpret quantitative data (skills - scientific methods, R or software with visual interfaace like SPSS, SAS, Statistica) -- - Data science is interdisciplinary or multidisciplinary field that uses scientific methods, statistical procedure, models and big data -- - Data science problems often relate to making predictions (regression analysis) and optimizing search of large, big databases (skills - programming languages like Python and R, database with SQL) -- - Both of them extract knowledge from data --- # Why statistics in psychology? - Psychology (among other reasons) is statistical science because of subject of interests - People -- - At the fundamental level - psychology is harder than other sciences because of subject or matter of measurement -- - People are not constant - they are different among many aspects or dimensions. The key word - variability -- - Objects of study have variations. We could not direct measure ... we have different expressions --- # Why study statistics? Why study statistics make you some advantages according to others? [1] "" - Having statistical skills puts you at an advantage when applying to graduate level -- - must have for a research or academic position -- - a basic statistics course on your transcript sets you apart from other students -- - statistics course can be an invigorating intellectual challenge / cognitive skills. -- - Having a knowledge of statistics makes you a better student, as it will enable you to better understand journal articles and books -- - It will also make you a critical consumer of information outside of an educational setting. When logging onto the Internet, reading a newspaper or magazine, or watching the local news, you are often exposed to research results. But just how valid and reliable are those studies? This course will give you valuable insight into the methods used in those research reports, allowing you to determine whether the studies are dependable. -- - A basic knowledge of statistics will position you well for further study if you plan to pursue a graduate degree in the social or behavioral sciences or in many other fields. -- - statistical course push you apart from other job candidate --- # About statistics 1. Statistical Inference requires assumptions about the data being analysed. [1] "" -- 2. Statistics is concerned with scientific methods for collecting, organizing, summarizing, presenting and analyzing data as well as with drawing valid conclusions and making reasonable decisions on the basis of such analysis. --- # Individuals vs. variables - individuals, objects, entities: described by the data. It must be a clear definition of the objects, entitis, subjects or individuals. -- - variable(s): a different measurement made on an individual -- - variable(s) are on different scale measurement --- # Population vs. sample: asumptions - Population in statistics describe as totality or all subjects / entities which reference is to be made -- - Population reference to all subjects / entities with same one or several atributes -- - A population statistics has no error! -- - Sample is finite number of subjects / entities from population -- - Sample is subset of population and statistics in 99% of research deal with them. Not with population! -- - When we deal with sample that means all statistics have error. We estimate population parameters. -- - Sampling is main step in the research design. We could not change that in next steps.... statistical analysis could! --- # Uncertainty and error - Errors and uncertainty are exists in any research -- - Statistical procedure estimate the amount of errors and/or uncertainty -- - Statistical knowledge expressed as probability -- - Probability is main term / concept of statistical explanation --- # Degrees Degrees of research associate with statistical complexity; 1. Descriptive level -- 2. Interpretative level -- 3. Prediction level --- # Statistical inference - Explaratory data analysis or descriptive statistics -- - Statistical inference or inferential statistics --- # Definitions Two main parts of statistics [1] "" - Descriptive statistics are procedures used to summarize and describe the important characteristics of a set of measurements. -- - Inferential statistics are procedures used to make inferences (that is, draw conclusions, make predictions, make decisions) about a population from information contained in a sample drawn from this population. --- # Close connection with research methods - estimation and hypothesis testing -- - research design -- - correlation research vs. experiments -- - quantitative analysis -- - but! not only... qualitative analysis needs some calculation and software --- # Variables Simple point out about some kind of variables:[@dancey_2020] - Continuous variables can take on absolutely any value within a given range. -- - Discrete variables can only take on certain discrete values in a range. -- - Categorical variables are those in which we simply allocate people to categories. --- # References ``` ## Warning in `[[.BibEntry`(x, ind): subscript out of bounds ``` Russo, R. (2021). _Statistics For The Behavioural Sciences: An Introduction To Frequentist And Bayesian Approaches_. 2nd ed. Routledge/Taylor & Francis Group. ISBN: 978-1-138-71148-8. (Visited on Oct. 03, 2021). --- class: center, middle # Thanks! Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan). The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](https://yihui.org/knitr/), and [R Markdown](https://rmarkdown.rstudio.com).